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

Area of Science:

• Public Health
• Biostatistics
• Epidemiology

Background:

• Long-term public health surveillance after war and terrorist attacks is challenging.
• Health impacts are often under-reported, necessitating accurate population risk estimation.
• Existing methods lack efficiency in capturing population undercounts.

Purpose of the Study:

• To develop a novel statistical model for estimating population undercounts in health surveillance.
• To assess the long-term health impacts on war veterans and survivors of terrorist attacks.
• To provide data for improved public health policy and decision-making.

Main Methods:

• Development of a novel trivariate Bernoulli model accounting for individual heterogeneity and information source dependence.
• Implementation of a Monte Carlo-based Expectation-Maximization (EM) algorithm for estimation.
• Application to real-world case studies: Gulf War veterans and 9/11 World Trade Center survivors.

Main Results:

• The proposed model demonstrates superior performance and robustness compared to existing methods.
• Adjusted analysis revealed increased annual cumulative incidence rates of amyotrophic lateral sclerosis (ALS) for Gulf War veterans.
• Significant undercounts were identified for individuals exposed to physical and mental health risks from the 9/11 attacks.

Conclusions:

• The novel statistical approach effectively estimates population undercounts in critical public health scenarios.
• Findings highlight the need for comprehensive long-term health monitoring of post-conflict and post-terrorism populations.
• The results offer valuable insights for policy formulation and resource allocation for survivor health management.