Back to Annual Meeting Page		American Public Health Association 133rd Annual Meeting & Exposition December 10-14, 2005 Philadelphia, PA

Abstract #113623

Michael A. Stoto, PhD¹, Arvind Jain, MS¹, John O. Davies-Cole, PhD, MPH², and A. Chevelle Glymph, MPH³. (1) Statistics group and RAND Health, RAND, 1200 South Hayes St., Arlington, VA 22202-5050, 703-413-1100 x5472, mstoto@rand.org, (2) Bureau of Epidemiology and Health Risk Assessment, District of Columbia Department of Health, 825 North Capitol Street, NE, Third Floor, Washington, DC 20002, (3) Bureau of Epidemiology & Health Risk Assessment, District of Columbia Department of Health, 825 North Capitol St NE, #3142, Washington, DC 20002

Immediately following September 11, 2001, the District of Columbia Department of Health began a syndromic surveillance program based on hospital emergency room visits. Daily counts of chief complaints such as unspecified infection and gastrointestinal complaints are analyzed daily, and an unusually high count triggers a chart review. Preliminary analyses show that the syndromic surveillance system does well in identifying the onset of the flu season and a previously undetected series of gastrointestinal illness outbreaks that appeared over four months in different hospitals. In addition, simulation studies using the same data have shown that over a range of simulated outbreak types, the univariate and multivariate CUSUM algorithms performed more effectively than other algorithms. The multivariate CUSUM was preferred to the univariate CUSUM for some but not all outbreak types. We are extending these analyses, with additional data and optimal detection algorithms, by carefully examining the data signaling the beginning of the flu season and the gastrointestinal outbreaks. This will enable us to characterize the performance of the statistical detection algorithms in practical terms, and on this basis identify the most effective algorithms for future use. In collaboration with the National Capital Region surveillance group, we will also compare the existence and timing of anomalies suggesting gastrointestinal outbreaks and the flu season in the DC syndromic surveillance data with other syndromic and non-syndromic data sources in the region. This analysis is intended to yield practical information on the sensitivity, specificity, and timeliness of the DC syndromic surveillance system.

Learning Objectives:

• · To understand the practical aspects of using syndromic surveillance in a local health department setting.
• · To understand the value of multivariate detection algorithms.
• · To illustrate the performance of ER-based syndromic surveillance systems for influenza and gastrointestinal complaints.
• · To illustrate methods for evaluating syndromic surveillance systems.

Keywords: Surveillance, Bioterrorism

Presenting author's disclosure statement:

I wish to disclose that I have NO financial interests or other relationship with the manufactures of commercial products, suppliers of commercial services or commercial supporters.