Literature DB >> 21471947

Assessment of ESSENCE performance for influenza-like illness surveillance after an influenza outbreak--U.S. Air Force Academy, Colorado, 2009.

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Abstract

The Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE), version II, designed by the Johns Hopkins University Applied Physics Laboratory and the U.S. Department of Defense (DoD), is an Internet-based syndromic disease surveillance system used by civilian and military health departments. ESSENCE was designed to increase the timeliness of outbreak detection, serving as an early warning system and providing opportunities to prevent and control the spread of infection. After a 2009 pandemic influenza A (H1N1) outbreak at the U.S. Air Force (USAF) Academy in Colorado, CDC was invited to conduct an evaluation of the ESSENCE influenza-like illness (ILI) surveillance system to assess its performance during the outbreak. Medical records at the USAF Academy clinics from June 25 through July 8, 2009, the period of the outbreak, were reviewed. This report summarizes the results of the evaluation, which demonstrated strengths in data quality, flexibility, and representativeness; however, ESSENCE was not useful for detecting or monitoring the H1N1 outbreak because of its lack of timeliness (1-3 day delay), inadequate sensitivity (71.4%), and poor predictive value positive (PVP) (31.8%) for identifying ILI cases. In this localized, single-source outbreak, ESSENCE did not serve as an early warning system for an emerging infectious disease and did not detect the outbreak soon enough to institute prevention and control measures that might have slowed the spread of infection. More frequent Internet data transmissions from the clinics to the ESSENCE server could improve timeliness, and PVP could be enhanced by including measured body temperature in the ESSENCE ILI case definition.

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Year:  2011        PMID: 21471947

Source DB:  PubMed          Journal:  MMWR Morb Mortal Wkly Rep        ISSN: 0149-2195            Impact factor:   17.586


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