Novel Data Sources for Epi Modeling - or - Enhancing influenza - - PowerPoint PPT Presentation

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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

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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 the Epidemiology & Evolution of Influenza DIMACS, Rutgers University, January 27, 2006

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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.

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Influenza

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OBJECTIVE

Characterize influenza season epidemiology using emergency department data by Age

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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)

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What is Syndromic Surveillance?

“Real-time” public health surveillance using data that is routinely collected for other purposes

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What are its Goals?

  • Early detection of large outbreaks
  • Characterization of size, spread, and tempo of
  • utbreaks once detected
  • Monitoring of disease trends

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48 (75%) of 64 NYC EDs 90% of ED visits

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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

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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”

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ED fever/respiratory & viral data in NYC

A/H3N2 B/Victoria A/H1N1 A/H3N2 Fujian A/H3N2 California & A/Fujian B/Victoria & B/Yamagata

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ED fever/respiratory chief complaint data in New York City impact by Age

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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

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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).

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