Effective surveillance on the long-term public health impact due to war and terrorist attacks remain limited. Such health issues are commonly under-reported, specifically for a large group of individuals. For this purpose, efficient estimation of the size of the population under the risk of physical and mental health hazards is of utmost necessity. In this context, multiple system estimation is a potential strategy that has recently been applied to quantify under-reported events allowing heterogeneity among the individuals and dependence between the sources of information. To model such complex phenomena, a novel trivariate Bernoulli model is developed, and an estimation methodology using Monte Carlo based EM algorithm is proposed. Simulation results show superiority of the performance of the proposed method over existing competitors and robustness under model mis-specifications. The method is applied to analyze real case studies on the Gulf War and 9/11 Terrorist Attack at World Trade Center, US. The results provide interesting insights that can assist in effective decision making and policy formulation for monitoring the health status of post-war survivors.

翻译：关于战争和恐怖袭击对公众健康长期影响的监测仍十分有限。此类健康问题常存在漏报现象，尤其是在大规模受影响群体中。因此，有效估计面临身心健康风险的人口规模至关重要。在此背景下，多重系统估计作为一种潜在策略，近期已被用于量化存在个体异质性与信息来源依赖性的漏报事件。为建模此类复杂现象，本文提出一种新型三元伯努利模型，并开发了基于蒙特卡洛EM算法的估计方法。模拟结果显示，该方法的性能优于现有竞争方法，且在模型误设下具有稳健性。该方法被应用于分析美国海湾战争及世贸中心9/11恐怖袭击的真实案例研究。研究结果提供了富有洞见的启示，有助于制定有效的决策与政策，以监测战后幸存者的健康状况。