Back to Annual Meeting

Back to Annual Meeting

Abstract #152475

• Slides (pdf) or Handout

Michael A. Stoto, PhD¹, Beth Ann Griffin, PhD², Arvind K. Jain, MS², John O. Davies-Cole, PhD, MPH³, Chevelle Glymph, MPH³, Garret Lum, MPH³, Gebreyesus Kidane, PhD³, and Samuel Washington, MPH⁴. (1) School of Nursing & Health Studies, Georgetown University, 3700 Reservoir Road, NW Box 571107, Washington, DC 20057-1107, (202) 687-3292, stotom@georgetown.edu, (2) RAND Corporation, 1200 South Hayes Street, Arlington, VA 22202-5050, (3) Bureau of Epidemiology & Health Risk Assessment, District of Columbia Department of Health, 825 North Capitol St. N.E., Washington, DC 20002, (4) BEHRA, DC Department of Health, 825 N Capitol St NE, 3rd floor, Washington, DC 20002

Immediately following September 11, 2001, the District of Columbia Department of Health began a syndromic surveillance program based on emergency room visits. The number of patients is recorded on the basis of chief complaint coded the following syndromic categories: death, sepsis, rash, respiratory complaints, gastrointestinal complaints, unspecified infection, neurological, or other complaints. Extending a previous analysis with the addition of two years of data and optimally chosen parameters for detection algorithms, we compared the timing of first alerts of the flu season in the DC syndromic surveillance data and other syndromic and non-syndromic data systems. Comparing the earliest dates at which various systems flag, we found that “unspecified infection” cases, and especially those from a children's hospital, consistently flags earlier than any of the other six hospitals and before a multivariate analysis of the other six hospitals in our analysis. In addition, we found that although the respiratory syndrome group also provides in-formation about influenza, it doesn't appear to add anything to the information contained in unspecified infection. Comparing unspecified infection cases in DC hospitals using optimal detection algorithms to CDC's sentinel physician data for the South Atlantic states for four years in which there was a discernable influenza outbreak, we found that in two of these years, DC syndromic surveillance outperformed the other two systems, and in one year it flagged only two days after the CDC system. Given a built in delay of about two weeks in the CDC system, this is a substantial advantage.

Learning Objectives:

• • To understand how to use an innovative statistical methodology – syndromic surveillance – in public health practice.
• • To understand how to evaluate the performance of syndromic surveillance systems in sta-tistical terms.
• • To illustrate how statistical analyses can help public health deal with the public health challenge of influenza and pandemic influenza.

Keywords: Surveillance, Infectious Diseases

Presenting author's disclosure statement:

Any relevant financial relationships? No
Any institutionally-contracted trials related to this submission?

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.

• Slides (pdf) or Handout