Layout 1 ISDS Annual Conference Proceedings 2012. This is an Open Access article distributed under the terms of the Creative Commons Attribution- Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. ISDS 2012 Conference Abstracts Selecting Targeted Symptoms/Syndromes for Syndromic Surveillance in Rural China Li Tan1, Jie Zhang1, Liwei Cheng1, Weirong Yan1, 2, Vinod K. Diwan2, Lu Long1 and Shaofa Nie*1 1Tongji Medical College, Wuhan City, China; 2Karolinska Institutet, Stockholm, Sweden Objective To select the potential targeted symptoms/syndromes as early warning indicators for epidemics or outbreaks detection in rural China. Introduction Patients’ chief complaints (CCs) as a common data source, has been widely used in syndromic surveillance due to its timeliness, ac- curacy and availability (1). For automated syndromic surveillance, CCs always classified into predefined syndromic categories to facil- itate subsequent data aggregation and analysis. However, in rural China, most outpatient doctors recorded the information of patients (e.g. CCs) into clinic logs manually rather than computers. Thus, more convenient surveillance method is needed in the syndromic sur- veillance project (ISSC). And the first and important thing is to select the targeted symptoms/syndromes. Methods Epidemiological analysis was conducted on data from case report system in Jingmen City (one study site in ISSC) from 2004 to 2009. Initial symptoms/syndromes were selected by literature reviews. And finally expert consultation meetings, workshops and field investiga- tion were held to confirm the targeted symptoms/syndromes. Results 10 kinds of infectious diseases, 6 categories of emergencies, and 4 bioterrorism events (i.e. plague, anthrax, botulism and hemorrhagic fever) were chose as specific diseases/events for monitoring (Table 1). Two surveillance schemes were developed by reviewing on 565 literatures about clinical conditions of specific diseases/events and 14 literatures about CCs based syndromic surveillance. The former one was to monitor symptoms (19 initial symptoms), and then ag- gregation or analysis on single or combined symptom(s); and the other one was to monitor syndromes (9 initial syndromes) directly (Table 2). The consultation meeting and field investigation identified three issues which should be considered: 1) the abilities of doctors especially village doctors to understand the definitions of symp- toms/syndromes; 2) the workload of data collection; 3) the sensitive and specific of each symptom/syndrome. Finally, Scheme 1 was used and 10 targeted symptoms were determined (Table 2). Conclusions We should take the simple, stability and feasibility of operation, and also the local conditions into account before establishing a sur- veillance system. Symptoms were more suitable for monitoring com- pared to syndromes in resource-poor settings. Further evaluated and validated would be conducted during implementation. Our study might provide methods and evidences for other developing countries with limited conditions in using automated syndromic surveillance system, to construct similar early warning system. Table 1. Epidemiological analysis on cases and emergencies data * Chronic infectious diseases (excluded). † Selected specific diseases (top 5) or events (non-infectious excluded). Table 2 List of symptoms/syndromes * The incidence of symptom was >= 20% of specific disease(s)/event(s). ** The number of times of syndromes monitored was >= 4 times. Asthma (4 times) and diarrhea (5 times) were excluded due to study objectives. † Final targeted symptoms. Keywords Syndromic surveillance; Chief complaint; Early warning Acknowledgments This study was funded by [European Union’s] [European Atomic Energy Community’s] Seventh Framework Programme ([FP7/2007-2013] [FP7/2007-2011]) under grant agreement no. [241900]. References 1.Chapman WW, Dowling JN, Wagner MM. Generating a reliable refer- ence standard set for syndromic case classification. J Am Med Inform Assoc. 2005;12:618-29. *Shaofa Nie E-mail: sf_nie@mails.tjmu.edu.cn Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(1):e140, 2013