Estimating prevalence of post-war health disorders using multiple systems data.

ABSTRACT: Effective surveillance on the long-term public health impact due to war and terrorist attacks remains limited. Such health issues are commonly under-reported, specifically for a large group of individuals. For this purpose, efficient estimation of the size or undercount of the population under the risk of physical and mental health hazards is of utmost necessity. A novel trivariate Bernoulli model is developed allowing heterogeneity among the individuals and dependence between the sources of information, and an estimation methodology using a Monte Carlo-based EM algorithm is proposed. Simulation results show the superiority of the performance of the proposed method over existing competitors and robustness under model mis-specifications. The method is applied to analyse two real case studie

SUBMITTER: Bhuyan P 

PROVIDER: S-EPMC11208173 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature