A Class of Latent Marginal Models for Capture–Recapture Data With Continuous Covariates

On the design of closed recapture experiments

We propose a method to plan the number of occasions of recapture experiments for population size estimation. We do so by fixing the smallest number of capture occasions so that the expected length of the profile confidence interval is less than or equal to a fixed threshold. In some cases, we solve the optimization problem in closed form. For more complex models we use numerical optimization. We detail models assuming homogeneous, time-varying, subject-specific capture probabilities, behavioral response to capture, and combining behavioral response with subject-specific effects. The principle we propose can be extended to plan any other model specification. We formally show the validity of the approach by proving distributional convergence. We illustrate with simulations and challenging examples in epidemiology and ecology. We report that in many cases adding as few as two sampling occasions may substantially reduce the length of confidence intervals.

PREDICTING LATENT CLASS SCORES FOR SUBSEQUENT ANALYSIS

Latent class regression models relate covariates and latent constructs such as psychiatric disorders. Though full maximum likelihood estimation is available, estimation is often in three steps: (i) a latent class model is fitted without covariates; (ii) latent class scores are predicted; and (iii) the scores are regressed on covariates. We propose a new method for predicting class scores that, in contrast to posterior probability-based methods, yields consistent estimators of the parameters in the third step. Additionally, in simulation studies the new methodology exhibited only a minor loss of efficiency. Finally, the new and the posterior probability-based methods are compared in an analysis of mobility/exercise.

Incidence of bacterial meningitis (2001-2005) in Lazio, Italy: the results of a integrated surveillance system

Background

Monitoring the incidence of bacterial meningitis is important to plan and evaluate preventive polices. The study's aim was to estimate the incidence of bacterial meningitis by aetiological agent in the period 2001–2005, in Lazio Italy (5.3 mln inhabitants).

Methods

Data collected from four sources – hospital surveillance of bacterial meningitis, laboratory information system, the mandatory infectious diseases notifications, and hospital information system – were combined into a single archive.

Results

944 cases were reported, 89% were classified as community acquired. S. pneumoniae was the most frequent aetiological agent in Lazio, followed by N. meningitis. Incidence of H. influenzae decreased during the period. 17% of the cases had an unknown aetiology and 13% unspecified bacteria. The overall incidence was 3.7/100,000. Children under 1 year were most affected (50.3/100.000), followed by 1–4 year olds (12.5/100,000). The percentage of meningitis due to aetiological agents included in the vaccine targets, not considering age, is 31%. Streptococcus spp. was the primary cause of meningitis in the first three months of life. The capture-recapture model estimated underreporting at 17.2% of the overall incidence.

Conclusion

Vaccine policies should be planned and monitored based on these results. The integrated surveillance system allowed us to observe a drop in H. influenzae b meningitis incidence consequent to the implementation of a mass vaccination of newborns.

A class of latent Markov models for capture-recapture data allowing for time, heterogeneity, and behavior effects

We propose an extension of the latent class model for the analysis of capture-recapture data which allows us to take into account the effect of a capture on the behavior of a subject with respect to future captures. The approach is based on the assumption that the variable indexing the latent class of a subject follows a Markov chain with transition probabilities depending on the previous capture history. Several constraints are allowed on these transition probabilities and on the parameters of the conditional distribution of the capture configuration given the latent process. We also allow for the presence of discrete explanatory variables, which may affect the parameters of the latent process. To estimate the resulting models, we rely on the conditional maximum likelihood approach and for this aim we outline an EM algorithm. We also give some simple rules for point and interval estimation of the population size. The approach is illustrated by applying it to two data sets concerning small mammal populations.