The value of capture-recapture methods even for apparent exhaustive surveys. The need for adjustment for source of ascertainment intersection in attempted complete prevalence studies.
Almost all reported prevalence studies of which we are aware make exhaustive attempts to find diagnosed individuals and report all affected individuals, but make no attempt to estimate or adjust for missing cases. Yet very simple methods introduced in the planning stage of a prevalence study may enable investigators, or at least those subsequently reading their reports, to derive such adjusted estimates. If investigators keep track of the nature of the ascertainment of cases by source and collect and report data that allow calculation of the number of cases by source intersection, then they, or at least others, may derive estimates of missing cases and of the total population affected, by using readily available analogues of capture-recapture methods developed for wildlife populations censuses. Unfortunately, such methods are often inappropriately disparaged or ignored by epidemiologists. The derived estimates are sensitive to assumptions about dependence or independence ("interaction") of various sources, assumptions that sometimes are unprovable, and these estimates have some uncertainty because of statistical fluctuation. Moreover, most investigators who attempt exhaustive prevalence studies apparently believe that they have ascertained all cases and that there is no need to attempt to adjust for, let alone provide data pertinent to, the number of missing cases or to use a statistical method that will at best imply a certain imprecision to their result. Yet a survey that reports prevalence data without adjustment for, or data on, source intersection in essence makes an estimate of missing cases--zero--while providing no quantitative grounds for that claim. The results of all such surveys should be regarded with skepticism because, at best (if the case reports are accurate), they provide only a lower boundary of prevalence. We illustrate the grounds for these views by analyzing data from an apparently exhaustive prevalence study that used at least 14 distinct sources for ascertainment, including advertising, to find cases. Available limited data on source intersection provided in the report enable the plausible inference that the study missed about 25-40% of cases. We urge that no attempted complete prevalence studies be presented without data on ascertainment by source intersection.
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School of Public Health, University of California, Berkeley.
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D015982:Bias; D016010:Binomial Distribution; D003430:Cross-Sectional Studies; D004812:Epidemiologic Methods; D006801:Humans; D006816:Huntington Disease / Q000453:epidemiology; D016013:Likelihood Functions; D016014:Linear Models; D008396:Maryland / Q000453:epidemiology; D015201:Meta-Analysis as Topic; D012042:Registries; D015203:Reproducibility of Results