Syndromic surveillance using laboratory test requests: a practical guide informed by experience with two systems.
Syndromic surveillance systems can enhance early disease warning, endemic disease monitoring, or help to accumulate proof of disease freedom. In order to provide immediate feedback to achieve these goals, the health data sources scanned should be acquired continuously, in an automated fashion, and should be stored electronically. Recognizing that data from diagnostic test requests often meet these requirements, two systems designed to automatically extract surveillance information from animal laboratory databases have been developed and are described in this paper. These systems are designed to contribute to early disease detection, as well as the timely management of epidemiological information, in a province of Canada and in Sweden, the areas served by the diagnostic laboratories concerned. Classifying in-coming requests into syndromes, the first step, was the most time-consuming and the least portable step between the two systems. The remaining steps were more easily adjusted from one system to implementation in the other. These steps included: retrospective evaluation of data to create baseline profiles following the removal of excessive noise and aberrations; the identification of temporal effects; prospective evaluation of detection algorithms; and finally real-time monitoring and implementation. Building upon the institutions' existing data management software, all steps to use those data for the purposes of syndromic surveillance were set up using open source software; as a result this approach could be readily adopted by other institutions. Relatively straight-forward development and maintenance is expected to lead to the incorporation of these systems into each institution's surveillance processes, becoming an indispensable tool for diagnosticians and epidemiologists, as well as stimulating further technical development of such systems.
Swedish Zoonosis Centre, Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), Uppsala, Sweden; Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Canada. Electronic address: fernanda.dorea@sva.se.
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FC
Dórea
Swedish Zoonosis Centre, Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), Uppsala, Sweden.
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Lindberg
Animal Health Laboratory, University of Guelph, Canada.
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BJ
McEwen
Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Canada.
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Revie
Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Canada.
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Sanchez
D000465:Algorithms; D000820:Animal Diseases / Q000453:epidemiology* / Q000209:etiology; D000818:Animals; D004196:Disease Outbreaks / Q000662:veterinary*; D062665:Epidemiological Monitoring / Q000662:veterinary*; D009864:Ontario / Q000453:epidemiology; D011446:Prospective Studies; D012189:Retrospective Studies; D013548:Sweden / Q000453:epidemiology
Animal health; Early disease detection; Laboratory; Syndromic surveillance