title: Analysis of aberrations in public health surveillance data: estimating variances on correlated samples.
abstract: The detection of unusual patterns in health data presents an important challenge to health workers interested in early identification of epidemics or important risk factors. A useful procedure for detection of aberrations is the ratio of a current report to some historic baseline. This work addresses the problem of finding the variance of such a ratio when the surveillance reports are correlated. Results show that, when estimating this variance or the variance of the sample mean from a series of observations with an estimated correlation structure, bootstrap and jackknife estimates may be overly optimistic. The delta method or a classical method may be more useful when such model dependence is inappropriate.
authors.affiliation: National Cancer Institute, Bethesda, Maryland 20892.
authors.forename: K
authors.initials: K
authors.lastname: Kafadar
authors.forename: D F
authors.initials: DF
authors.lastname: Stroup
mesh_terms: D000704:Analysis of Variance; D002487:Centers for Disease Control and Prevention, U.S.; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D015987:Effect Modifier, Epidemiologic; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D009017:Morbidity; D011159:Population Surveillance / Q000379:methods*; D011634:Public Health / Q000706:statistics & numerical data*; D014481:United States