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Using Data on an Influenza B Outbreak To Evaluate a
Syndromic Surveillance System --- Israel, June 2004
Zalman Kaufman,1 E.
Cohen,2 T. Peled-Leviatan,1 C.
Lavi,2 G. Aharonowitz,1 R. Dichtiar,1
M. Bromberg,1 O. Havkin,2 Y. Shalev,3 R. Marom,4 V. Shalev,4 J. Shemer,4,5 M. Green1,5
1Israel Center for Disease Control, Ministry of Health, Tel Hashomer; 2Central District Health Office, Ministry of Health, Rechovot; 3Central Virology Laboratory, Ministry of Health, Tel Hashomer; 4Maccabi Healthcare Services, Tel-Aviv; 5Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
Corresponding author: Manfred S. Green, Israel Center for Disease Control, Gertner Institute, Sheba Medical Center, Tel Hashomer 52621, Israel. Telephone: 972-3-737-1500; Fax: 972-3-737-1515; E-mail: m.green@icdc.health.gov.il.
Disclosure of relationship: The contributors of this report have disclosed that they have no financial interest, relationship, affiliation, or other association with any organization that might represent a conflict of interest. In addition, this report does not contain any discussion
of unlabeled use of commercial products or products for investigational use.
Abstract
Introduction: Since 2002, as part of a national biologic terrorism preparedness
program, the Israel Center for Disease Control (ICDC) has been developing a syndromic surveillance system based on data from
community clinics and hospital emergency departments. Selected analytic tools are being evaluated for possible integration within this system.
Objectives: This study evaluated the performance of the What's Strange About Recent Events (WSARE) algorithm (http://www.autonlab.org/autonweb/showSoftware/159) for anomaly pattern detection when applied to records of daily patient visits to clinics of a local health maintenance organization (HMO).
Methods: Data from an influenza B outbreak that occurred in June 2004 in an elementary school in a small (population: approximately 7,000 persons) Israeli town were used. WSARE searches for groups with specific characteristics (e.g., a recent pattern of place, age, and diagnosis associated with illness that is anomalous when compared with historic patterns). The data set used was limited to 1) patients living in the county where
the outbreak occurred; 2) a 35-day period during May--June 2004; and 3) records containing International Classification of Diseases, Ninth Revision
(ICD-9) codes for signs, symptoms, and syndromes associated with infectious
morbidity. On average, the data set included 510 records/day. Besides ICD-9 codes, data included date
of visit to clinic, day of week, city/town code, and patient's age.
Results: Two successive significant anomalies (p<0.0001) were detected in the HMO data set that could signal the influenza outbreak, both sharing three constituents: 1) the town code; 2) the age category of affected children;
and 3) the ICD-9 code for viral infection, which was the most prevalent diagnosis assigned by HMO physicians identified in an investigation by the regional health department. Had the data been available for realtime analysis, the first anomaly could have been detected on day 2, when the outbreak was first reported to public health officials.
Conclusion: A centralized, comprehensive surveillance system can rapidly detect localized, fast-developing outbreaks. Although early detection is hard to achieve in this instance, timely and reliable
information produced by syndromic surveillance is of great value in supporting outbreak management and placing it in the context of
the background morbidity in the country. However, had the outbreak occurred in winter, detection would have
been
more complex. When the outbreak data were superimposed on winter background, only a single significant two-constituents anomaly was detected at day 2 of the simulated outbreak, lacking the information
to target on the specific age group of the schoolchildren.
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