The multiple-record systems estimator when registrations refer to different but overlapping populations

Bayesian latent class models for capture-recapture in the presence of missing data

We propose a method for estimating the size of a population in a multiple record system in the presence of missing data. The method is based on a latent class model where the parameters and the latent structure are estimated using a Gibbs sampler. The proposed approach is illustrated through the analysis of a data set already known in the literature, which consists of five registrations of neural tube defects.

A log-linear multidimensional Rasch model for capture-recapture

In this paper, a log-linear multidimensional Rasch model is proposed for capture-recapture analysis of registration data. In the model, heterogeneity of capture probabilities is taken into account, and registrations are viewed as dichotomously scored indicators of one or more latent variables that can account for correlations among registrations. It is shown how the probability of a generic capture profile is expressed under the log-linear multidimensional Rasch model and how the parameters of the traditional log-linear model are derived from those of the log-linear multidimensional Rasch model. Finally, an application of the model to neural tube defects data is presented.

Multilist population estimation with incomplete and partial stratification

Multilist capture-recapture methods are commonly used to estimate the size of elusive populations. In many situations, lists are stratified by distinguishing features, such as age or sex. Stratification has often been used to reduce biases caused by heterogeneity in the probability of list membership among members of the population; however, it is increasingly common to find lists that are structurally not active in all strata. We develop a general method to deal with cases when not all lists are active in all strata using an expectation maximization (EM) algorithm. We use a flexible log-linear modeling framework that allows for list dependencies and differential probabilities of ascertainment in each list. Finally, we apply our method of estimating population size to two examples.

Analysing capture–recapture data when some variables of heterogeneous catchability are not collected or asked in all registrations

Registrations in epidemiological studies suffer from incompleteness, thus a general consensus is to use capture-recapture models. Lately, there has been a thrust to incorporate covariates which relate to the capture probabilities in order to improve the estimate of population size. Current approaches utilize covariates observed in all the pertinent registrations. In epidemiological applications, one often encounters capture-recapture data where some covariates are measured by some but not by all of the registrations under scrutiny. In this article, the usefulness of these types of covariates is evaluated and furthermore a methodology for analysing such capture-recapture data is presented. The methodology is illustrated by applying it to data on neural tube defects in the Netherlands.

A case study of capture–recapture methodology using scrapie surveillance data in Great Britain

We applied capture-recapture methodology (CRC) to data from three surveillance sources (statutory notification, abattoir survey (AS) and fallen stock (FS) survey) to estimate the number of holdings infected with scrapie in Great Britain and to assess the sensitivity of the surveillance network. Between January 1, 2002 and March 31, 2003, 144 holdings were identified by the three sources. Using CRC modelling techniques, we estimated a minimum lower bound for the total number of holdings infected as 642. A biologically plausible positive dependence between the statutory reporting and the fallen stock survey was found statistically significant. The sensitivity of the three sources combined was very low. The integration of the three overlapping sources provided a better understanding of the interactions within the surveillance network. However, the scarcity of the data and reduced overlapping among sources only allowed for very cautious inferences to be drawn about the true proportion of scrapie affected holdings in the national population. Future surveys and surveillance activities should be planned such that the resulting data can be used more effectively as part of CRC modelling approaches.