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ISDS 2012 Conference Abstracts

Syndromic Surveillance for Outbreak Detection and
Investigation
Tom Andersson*1, 2, 4, Pär Bjelkmar3, Anette Hulth1, Johan Lindh1, Stephan Stenmark5 and
Mikael Widerström6

1Swedish Institute for Communicable Disease Control (SMI), Solna, Sweden; 2National Food Agency, Uppsala, Sweden; 3Inera AB,
Stockholm, Sweden; 4Stockholm University Section for Mathematical Statistics, Stockholm, Sweden; 5Västerbotten County Medical
Officer, Umeå, Sweden; 6Jämtland County Medical Officer, Östersund, Sweden

Objective
For the purpose of developing a national system of outbreak sur-

veillance, we compared local outbreak signals in three sources of syn-
dromic data – telephone triage of acute gastroenteritis (Swedish
Health Care Direct 1177), web queries about symptoms of gastroin-
testinal illness (Stockholm County’s website for healthcare informa-
tion), and OTC pharmacy sales of anti-diarrhea medication.

Introduction
A large part of the applied research on syndromic surveillance tar-

gets seasonal epidemics, e.g. influenza, winter vomiting disease, ro-
tavirus and RSV, in particular when dealing with preclinical
indicators, e.g. web traffic (Hulth et al, 2009). The research on local
outbreak surveillance is more limited. Two studies of teletriage data
(NHS Direct) have shown positive and negative results respectively
(Cooper et al, 2006; Smith et al, 2008). Studies of OTC pharmacy
sales have reported similar equivocal performance (Edge et al, 2004;
Kirian and Weintraub, 2010). As far as we know, no systematic com-
parison of data sources with respect to multiple point-source out-
breaks has so far been published (cf. Buckeridge, 2007). In the current
study, we evaluated the potential of three data sources for syndromic
surveillance by analyzing the correspondence between signal prop-
erties and point-source outbreak characteristics.

Methods
The extracted data streams were compared with respect to nine

waterborne and foodborne outbreaks in Sweden in 2007-2011. The
analysis consisted of three parts: (1) the validation of outbreak signals
by comparing signal counts during outbreak and baseline periods, (2)
the estimation of detection limits by modeling signal rates (signal-
to-case ratios), and (3) the evaluation of early warning potential by
means of signal detection analysis.

Results
The four largest outbreaks generated strong and clear outbreak sig-

nals in the 1177 triage data. The two largest outbreaks produced sig-
nals in OTC sales of anti-diarrhea. No signals could be identified in
the web query data. The outbreak detection limit based on triage data
was about 100-1000 cases. For two outbreaks, triage data on diarrhea
provided outbreak signals early on, weeks and months respectively,
potentially serving the purpose of early warning.

Conclusions
The sensitivity and specificity were highest for telephone triage

data on patient symptoms. It provided the most promising source of

syndromic data for surveillance of point-source outbreaks. Currently,
a project has been initialized to develop and implement a national
system in Sweden for daily syndromic surveillance based on 1177
Health Care Direct, supporting regional and local outbreak detection
and investigation.

Keywords
syndromic surveillance; outbreak detection; point-source outbreak;
outbreak investigation; data analysis

Acknowledgments

The study is part of an ongoing research and development project on syn-
dromic surveillance (SUMO) funded by the Swedish Agency for Contin-
gency Planning (MSB).

References

Buckeridge DL. Outbreak detection through automated surveillance: a
review of the determinants of detection. J Biomed Inform. 2007
Aug;40(4):370-9.

Cooper DL, Verlander NQ, Smith GE, Charlett A, Gerard E, Willocks L,
O’Brien S. Can syndromic surveillance data detect local outbreaks of
communicable disease? A model using a historical cryptosporidiosis
outbreak. Epidemiol Infect. 2006 Feb;134(1):13-20.

Edge VL, Pollari F, Lim G, Aramini J, Sockett P, Martin SW, Wilson J,
Ellis A. Syndromic surveillance of gastrointestinal illness using phar-
macy over-the-counter sales. A retrospective study of waterborne out-
breaks in Saskatchewan and Ontario. Can J Public Health. 2004
Nov-Dec;95(6):446-50.

Hulth A, Rydevik G, Linde A. Web queries as a source for syndromic sur-
veillance. PLoS One. 2009;4(2).

Kirian ML, Weintraub JM. Prediction of gastrointestinal disease with
over-the-counter diarrheal remedy sales records in the San Francisco
Bay Area. BMC Med Inform Decis Mak. 2010 Jul 20;10:39.

Smith S, Elliot AJ, Mallaghan C, Modha D, Hippisley-Cox J, Large S,
Regan M, Smith GE. Value of syndromic surveillance in monitoring
a focal waterborne outbreak due to an unusual Cryptosporidium geno-
type in Northamptonshire, United Kingdom, June - July 2008. Euro
Surveill. 2010 Aug 19;15(33):19643.

*Tom Andersson
E-mail: tom.andersson@smi.se

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(1):e78, 2013