Evaluation of a method for detecting aberrations in public health surveillance data.
The detection of unusual patterns in routine public health surveillance data on diseases and injuries presents an important challenge to health workers interested in early identification of epidemics or clues to important risk factors. Each week, state health departments report the numbers of cases of about 50 notifiable diseases to the Centers for Disease Control and Prevention, and these reports are published weekly in the Morbidity and Mortality Weekly Report. A new analytic method and a horizontal bar graph were introduced in July 1989 to facilitate easy identification of unusual numbers of reported cases. Evaluation of the statistical properties of this method indicates that the results are fairly robust to nonnormality and serial correlation of the data. An epidemiologic evaluation of the method after the first 6 months showed that it is useful for detection of specific types of aberrations in public health surveillance.
Stroup
D F
DF
Division of Surveillance and Epidemiology, Centers for Disease Control and Prevention, Atlanta, GA 30333.
Wharton
M
M
Kafadar
K
K
Dean
A G
AG
D015982:Bias; D003627:Data Interpretation, Statistical; D004196:Disease Outbreaks; D006801:Humans; D015233:Models, Statistical; D009017:Morbidity; D009937:Organizational Objectives; D011159:Population Surveillance / Q000379:methods*; D011635:Public Health Administration / Q000458:organization & administration; D012680:Sensitivity and Specificity