A systems overview of the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE II)

Autor: Howard Burkom, Joseph S. Lombardo, Wayne Loschen, Julie A. Pavlin, Sheryl Happel Lewis, Eugene Elbert, James Sari, Carol Sniegoski, Richard Wojcik, Steven Magruder
Rok vydání: 2003
Předmět:
Zdroj: Scopus-Elsevier
ISSN: 1468-2869
1099-3460
Popis: The Electronic Surveillance System for the Early Notification of Community-Based Epidemics, or ESSENCE II, uses syndromic and nontraditional health information to provide very early warning of abnormal health conditions in the National Capital Area (NCA). ESSENCE II is being developed for the Department of Defense Global Emerging Infections System and is the only known system to combine both military and civilian health care information for daily outbreak surveillance. The National Capital Area has a complicated, multijurisdictional structure that makes data sharing and integrated regional surveillance challenging. However, the strong military presence in all jurisdictions facilitates the collection of health care information across the region. ESSENCE II integrates clinical and nonclinical human behavior indicators as a means of identifying the abnormality as close to the time of onset of symptoms as possible. Clinical data sets include emergency room syndromes, private practice billing codes grouped into syndromes, and veterinary syndromes. Nonclinical data include absenteeism, nurse hotline calls, prescription medications, and over-the-counter self-medications. Correctly using information marked by varying degrees of uncertainty is one of the more challenging as pects of this program. The data (without personal identifiers) are captured in an electronic format, encrypted, archived, and processed at a secure facility. Aggregated information is then provided to users on secure Web sites. When completed, the system will provide automated capture, archiving, processing, and notification of abnormalities to epidemiologists and analysts. Outbreak detection methods currently include temporal and spatial variations of odds ratios, autoregressive modeling, cumulative summation, matched filter, and scan statistics. Integration of nonuniform data is needed to increase sensitivity and thus enable the earliest notification possible. The performance of various detection techniques was compared using results obtained from the ESSENCE II system.
Databáze: OpenAIRE
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