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A Practical Method for Surveillance of Novel H1N1 Influenza Using Automated Hospital Data

Published online by Cambridge University Press:  02 January 2015

Teena Chopra ,
Juliann Binienda ,
Mazin Mohammed ,
Rushyal Shyamraj ,
Patrick Long ,
David Bach ,
Cristi Carlton ,
Susan Peters ,
Paul Lephart  and
George Alangaden
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Teena Chopra*
Affiliation:
Wayne State University, Detroit, Michigan
Juliann Binienda
Affiliation:
Wayne State University, Detroit, Michigan
Mazin Mohammed
Affiliation:
Wayne State University, Detroit, Michigan
Rushyal Shyamraj
Affiliation:
Wayne State University, Detroit, Michigan
Patrick Long
Affiliation:
Wayne State University, Detroit, Michigan
David Bach
Affiliation:
Wayne State University, Detroit, Michigan
Cristi Carlton
Affiliation:
Michigan Department of Community Health, Lansing, Michigan
Susan Peters
Affiliation:
Michigan Department of Community Health, Lansing, Michigan
Paul Lephart
Affiliation:
Wayne State University, Detroit, Michigan
George Alangaden
Affiliation:
Wayne State University, Detroit, Michigan
Sorabh Dhar
Affiliation:
Wayne State University, Detroit, Michigan
Dror Marchaim
Affiliation:
Wayne State University, Detroit, Michigan
Michelle Schreiber
Affiliation:
Wayne State University, Detroit, Michigan
Keith S. Kaye
Affiliation:
Wayne State University, Detroit, Michigan
*
Division of Infectious Diseases, 5 Hudson, Harper University Hospital, 3990 John R. Street, Detroit, MI, 48201 (tchopra@med.wayne.edu)
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Extract

We report a surveillance method for influenza that is based on automated hospital laboratory and pharmacy data. During the 2009 H1N1 influenza pandemic, this method was objective, easy to perform, and utilized readily available automated hospital data. This surveillance method produced results that correlated strongly with influenza-like illness surveillance data.

Type
Concise Communication
Information
Infection Control & Hospital Epidemiology , Volume 32 , Issue 7 , July 2011 , pp. 700 - 702
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2011

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