The applications of capture-recapture models to epidemiological data

Stat Med. 2001 Oct 30;20(20):3123-57. doi: 10.1002/sim.996.


Capture-recapture methodology, originally developed for estimating demographic parameters of animal populations, has been applied to human populations. This tutorial reviews various closed capture-recapture models which are applicable to ascertainment data for estimating the size of a target population based on several incomplete lists of individuals. Most epidemiological approaches merging different lists and eliminating duplicate cases are likely to be biased downwards. That is, the final merged list misses those who are in the population but were not ascertained in any of the lists. If there are no matching errors, then the duplicate information collected from a capture-recapture experiment can be used to estimate the number of missed under proper assumptions. Three approaches and their associated estimation procedures are introduced: ecological models; log-linear models, and the sample coverage approach. Each approach has its unique way of incorporating two types of source dependencies: local (list) dependence and dependence due to heterogeneity. An interactive program, CARE (for capture-recapture) developed by the authors is demonstrated using four real data sets. One set of data deals with infection by the acute hepatitis A virus in an outbreak in Taiwan; the other three sets are ascertainment data on diabetes, spina bifida and infants' congenital anomaly discussed in the literature. These data sets provide examples to show the usefulness of the capture-recapture method in correcting for under-ascertainment. The limitations of the methodology and some cautionary remarks are also discussed.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Adult
  • Animals
  • Biometry / methods*
  • Congenital Abnormalities / epidemiology
  • Diabetes Mellitus / epidemiology
  • Ecology
  • Epidemiologic Methods*
  • Hepatitis A
  • Humans
  • Infant, Newborn
  • Italy / epidemiology
  • Massachusetts / epidemiology
  • Models, Biological*
  • Models, Statistical*
  • New York / epidemiology
  • Spinal Dysraphism / epidemiology
  • Taiwan / epidemiology