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

## TL;DR

This paper introduces a new statistical model to better estimate health issues in populations affected by war and terrorism, using data from multiple sources.

## Contribution

A novel trivariate Bernoulli model with a Monte Carlo-based EM algorithm is proposed for estimating health disorder prevalence.

## Key findings

- The proposed method outperforms existing methods in simulation studies.
- The model was applied to Gulf War veterans and 9/11 survivors, showing increased ALS incidence rates after adjusting for undercount.
- The number of individuals at risk from the WTC attacks increased by 42% after adjusting for undercount.

## 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 studies on monitoring amyotrophic lateral sclerosis (ALS) cases for the Gulf War veterans and the 9/11 terrorist attack survivors at the World Trade Center, USA. The average annual cumulative incidence rate for ALS disease increases by \documentclass[12pt]{minimal}
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\begin{document}$$33\%$$\end{document}33% and \documentclass[12pt]{minimal}
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\begin{document}$$16\%$$\end{document}16% for deployed and no-deployed military personnel, respectively, after adjusting the undercount. The number of individuals exposed to the risk of physical and mental health effects due to WTC terrorist attacks increased by \documentclass[12pt]{minimal}
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\begin{document}$$42\%$$\end{document}42%. These results provide interesting insights that can assist in effective decision-making and policy formulation for monitoring the health status of post-war survivors.