Global Multi-Cancer Environmental Causal Engine (GMCE) Simulation Study in Hodeidah, Yemen

Background: Air pollution, particularly PM2.5, is a known risk factor for multiple types of cancer. Yemen has limited air quality monitoring and cancer registry data, particularly in Hodeidah Governorate. Objective: To estimate cumulative multi-cancer risk due to environmental exposure using a mechanistic, Bayesian hierarchical, and causally identifiable framework (GMCE). Methods: PM2.5 exposure data were obtained from public monitoring sources (Weather.com) and averaged for Hodeidah (~17 µg/m³). GMCE was applied for 10 cancer types (Lung, Bladder, Breast, Colon, Leukemia, Kidney, Liver, Pancreas, Stomach, Esophagus), using tissue-specific parameters from published literature. Bayesian hierarchical modeling provided 95% Credible Intervals for cumulative risk estimates. Scenario analyses simulated changes in PM2.5 levels. Results: Estimated cumulative risk was highest for lung cancer (R ≈ 0.71, 95% CI: 0.65–0.77) and bladder cancer (R ≈ 0.52, 95% CI: 0.45–0.59). Scenario analysis showed a 20% reduction in PM2.5 decreased lung cancer risk to R ≈ 0.57 (0.51– 0.63). Conclusion: GMCE demonstrates feasibility for estimating multi-cancer risk in data-limited regions. Findings highlight the importance of PM2.5 reduction strategies in Hodeidah to lower population cancer risk.