Rob van Hest Capture-recapture Methods in Surveillance - RePub ...

Chapter 1
1.1 Assessing completeness of ascertainment in epidemiology
Epidemiology is the study of how often diseases occur in different groups of people and
why. 1 This includes knowledge about classification errors, i.e. the absence of true cases
and the presence of false-positive cases in registrations. Observing and monitoring health
and behaviour trends requires a surveillance system that captures useful data on those
persons correctly identified with the characteristic under study. This information can be
used to identify priorities and evaluate interventions. To determine the usefulness of a
surveillance system there is the need to assess the quality of the data and completeness of
ascertainment. 2 The number of individuals with a certain condition (i.e. cases) or events in
a population can be ascertained directly, by counting every single person or event as
attempted in a census, or indirectly, by obtaining sufficient information to estimate
prevalence (i.e. the number of cases at a specific point in time) or incidence (i.e. the
number of new cases during a specific period of time), as attempted in a survey (active
case-finding) or by notification (passive case-finding). Other examples of indirect
ascertainment of the number of cases in a population are pharmaco-epidemiolocal studies
and record-linkage, i.e. comparing patient data across multiple registers. It is difficult to
establish whether these counts are complete or biased to under-ascertainment and only
on a few occasions it is attempted to estimate or adjust for missing cases. 3,4 An indirect
technique that estimates completeness of ascertainment of surveys and registers used in
epidemiological studies is capture-recapture analysis. 5,6
1.2 Brief introduction to capture-recapture analysis
A more extensive overview of the historical development of capture-recapture analysis is
given elsewhere. 5 Briefly, the first use of capture-recapture analysis can be traced back to
Graunt who used a similar method for estimating the population of England as early as
1662 7 and Laplace, who attempted to estimate the population size in France in 1782, 8 but
usually it is mentioned that capture-recapture analysis was first applied by Petersen in
1894 for the study of fish populations. He used the so-called two-sample method, the
simplest capture-recapture model, to estimate the unknown size of a population of plaice
in the Limfjord in Denmark. 9 The first sample provides the animals for marking or
tagging and is returned to the population, while the second sample provides the
recaptures, i.e. the numbers of animals caught in both samples. Using the number of
recaptures and the number of animals caught in the first and the second sample, it is
possible, under certain assumptions, to estimate the number not caught in either sample,
thus providing an estimate of the total population size. Two-sample capture-recapture
analysis was extended to multiple-sample capture-recapture analysis by Schnabel in
1938. 10 Unmarked animals in each sample are given individual (i.e. numbered) marks
before being returned to the population, resulting in a known capture history of each
marked animal. The theory of capture-recapture models was developed more fully in the
1950's, for example by Chapman, 11 who suggested an adjustment of the capture-recapture
estimate to reduce small sample bias, known as the “Nearly Unbiased Estimator”, and
Darroch, 12 who founded the mathematical framework. To tackle the problem of violation
of the underlying assumptions, within animal population biology a range of different
10