A monitoring system for detecting aberrations in public health surveillance reports.
Routine analysis of public health surveillance data to detect departures from historical patterns of disease frequency is required to enable timely public health responses to decrease unnecessary morbidity and mortality. We describe a monitoring system incorporating statistical 'flags' identifying unusually large increases (or decreases) in disease reports compared to the number of cases expected. The two-stage monitoring system consists of univariate Box-Jenkins models and subsequent tracking signals from several statistical process control charts. The analyses are illustrated on 1980-1995 national notifiable disease data reported weekly to the Centers for Disease Control and Prevention (CDC) by state health departments and published in CDC's Morbidity and Mortality Weekly Report. Published in 1999 by John Wiley & Sons, Ltd. This article is a U.S. Government work and is in the public domain in the United States.
Williamson
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Epidemiology Program Office, MS K73, Centers for Disease Control and Prevention, 4770 Buford Highway, Atlanta, GA 30341, USA. dxw2@cdc.gov
Weatherby Hudson
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D002487:Centers for Disease Control and Prevention, U.S.; D003140:Communicable Disease Control; D004812:Epidemiologic Methods; D006506:Hepatitis A / Q000453:epidemiology; D006801:Humans; D008954:Models, Biological; D011159:Population Surveillance / Q000379:methods*; D011634:Public Health; D014481:United States / Q000453:epidemiology