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Novel Data Sources for Epi Modeling - or - Enhancing influenza surveillance by monitoring age-specific trends in emergency department chief complaint data Donald R. Olson New York City Department of Health & Mental Hygiene Workshop on

  1. Novel Data Sources for Epi Modeling - or - Enhancing influenza surveillance by monitoring age-specific trends in emergency department chief complaint data Donald R. Olson New York City Department of Health & Mental Hygiene Workshop on the Epidemiology & Evolution of Influenza DIMACS, Rutgers University, January 27, 2006

  2. You're going to be told lots of things. You get told things every day that don't happen. It doesn't seem to bother people… It's printed in the press. The world thinks all these things happen. They never happened. Everyone's so eager to get the story Before in fact the story's there That the world is constantly being fed Things that haven't happened. All I can tell you is, It hasn't happened. It's going to happen. accidental poetry from Feb. 28, 2003, Department of Defense news briefing.

  3. Influenza

  4. OBJECTIVE Characterize influenza season epidemiology using emergency department data by Age Olson, DOHMH, NYC - DIMACS 1/27/06

  5. Legal Mandate Local health officers shall exercise due diligence in ascertaining the existence of outbreaks of illness or the unusual prevalence of diseases, and shall immediately investigate the causes of same. New York State Sanitary Code, 10 NYCRR Chapter 1, Section 2.16(a) Olson, DOHMH, NYC - DIMACS 1/27/06

  6. What is Syndromic Surveillance? “Real-time” public health surveillance using data that is routinely collected for other purposes Olson, DOHMH, NYC - DIMACS 1/27/06

  7. What are its Goals? • Early detection of large outbreaks • Characterization of size, spread, and tempo of outbreaks once detected • Monitoring of disease trends Olson, DOHMH, NYC - DIMACS 1/27/06

  8. 48 (75%) of 64 NYC EDs 90% of ED visits Olson, DOHMH, NYC - DIMACS 1/27/06

  9. Coding chief complaints into syndromes Respiratory illness key words: cough, shortness of breath, URI, pneumonia excludes: cold symptoms Non-specific febrile illness key words: fever, chills, body aches, flu/influenza, viral syndrome Gastrointestinal illness key words: diarrhea, vomiting excludes: abdominal pain alone, nausea alone Olson, DOHMH, NYC - DIMACS 1/27/06

  13. Methods Data fever & respiratory ED visits viral influenza isolates Statistical Approach Serfling regression Analytical Approach excess visits (observed – expected) relative excess (observed / expected) influenza type, subtype & strain “signatures” Olson, DOHMH, NYC - DIMACS 1/27/06

  14. ED fever/respiratory & viral data in NYC A/H3N2 California A/H3N2 Fujian & A/Fujian A/H3N2 B/Victoria & B/Yamagata A/H1N1 B/Victoria Olson, DOHMH, NYC - DIMACS 1/27/06

  15. ED fever/respiratory chief complaint data in New York City impact by Age Olson, DOHMH, NYC - DIMACS 1/27/06

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  53. FINDINGS Syndrome data can be used to retrospectively describe • impact by influenza season specific to influenza type, subtype & strain • impact within-season consistent with early spread among kids Olson, DOHMH, NYC - DIMACS 1/27/06

  54. Acknowledgments • NYC DOHMH colleagues: Rick Heffernan, Marc Paladini, Don Weiss, Farzad Mostashari, and others. • Helpful discussions with Lone Simonsen (NIAID/NIH), Cecile Viboud and Mark Miller (FIC/NIH). Olson, DOHMH, NYC - DIMACS 1/27/06

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