INTRODUCTION: Syndromic surveillance's capability to augment existing surveillance for community-acquired gastrointestinal disease is unknown. OBJECTIVE: The objective of this study was to evaluate the capability of a syndromic surveillance system to detect outbreaks of gastrointestinal disease. METHODS: A retrospective analysis was conducted comparing ambulatory care data from a health plan with a set of 110 gastrointestinal-disease outbreaks identified by the Minnesota Department of Health during 2001-02. Unusual clusters of illness (i.e., signals) in the health-plan data were identified by analyzing daily counts of gastrointestinal illness using an adjusted space-time scan statistic. Concordance was defined as < or =5 km between outbreak and signal and the signal occurring within 1 week of the outbreak. RESULTS: During 104 weeks, the number of signals was roughly what would have been expected by chance, suggesting that the modeling did a good job of estimating the expected counts of illness and that false alarms would not have occurred much more often than the number predicted at the various thresholds. During the same period, the health department identified 110 eligible gastrointestinal outbreaks. Apparent associations of the three statistically most unusual concordant signals with outbreaks of viral or bacterial gastrointestinal illness were ruled out by the health department on the basis of detailed knowledge of the circumstances and low numbers of affected persons seeking medical care. CONCLUSION: No previously known gastrointestinal outbreaks were identified by this surveillance system. However, relatively few recognized outbreaks resulted in patients seeking medical care, and the sensitivity of this system to detect outbreaks of real significance to public health remains to be determined. Prospective evaluation probably will be required to understand the usefulness of syndromic surveillance systems to enhance existing disease surveillance.
INTRODUCTION: Syndromic surveillance's capability to augment existing surveillance for community-acquired gastrointestinal disease is unknown. OBJECTIVE: The objective of this study was to evaluate the capability of a syndromic surveillance system to detect outbreaks of gastrointestinal disease. METHODS: A retrospective analysis was conducted comparing ambulatory care data from a health plan with a set of 110 gastrointestinal-disease outbreaks identified by the Minnesota Department of Health during 2001-02. Unusual clusters of illness (i.e., signals) in the health-plan data were identified by analyzing daily counts of gastrointestinal illness using an adjusted space-time scan statistic. Concordance was defined as < or =5 km between outbreak and signal and the signal occurring within 1 week of the outbreak. RESULTS: During 104 weeks, the number of signals was roughly what would have been expected by chance, suggesting that the modeling did a good job of estimating the expected counts of illness and that false alarms would not have occurred much more often than the number predicted at the various thresholds. During the same period, the health department identified 110 eligible gastrointestinal outbreaks. Apparent associations of the three statistically most unusual concordant signals with outbreaks of viral or bacterial gastrointestinal illness were ruled out by the health department on the basis of detailed knowledge of the circumstances and low numbers of affected persons seeking medical care. CONCLUSION: No previously known gastrointestinal outbreaks were identified by this surveillance system. However, relatively few recognized outbreaks resulted in patients seeking medical care, and the sensitivity of this system to detect outbreaks of real significance to public health remains to be determined. Prospective evaluation probably will be required to understand the usefulness of syndromic surveillance systems to enhance existing disease surveillance.
Authors: W Katherine Yih; Swati Deshpande; Candace Fuller; Dawn Heisey-Grove; John Hsu; Benjamin A Kruskal; Martin Kulldorff; Michael Leach; James Nordin; Jessie Patton-Levine; Ella Puga; Edward Sherwood; Irene Shui; Richard Platt Journal: Public Health Rep Date: 2010 Jan-Feb Impact factor: 2.792
Authors: Sharon K Greene; Jie Huang; Allyson M Abrams; Debra Gilliss; Mary Reed; Richard Platt; Susan S Huang; Martin Kulldorff Journal: Foodborne Pathog Dis Date: 2012-03-19 Impact factor: 3.171