"background": "Evaluating the impact of community health centre (CHC) systems on population health outcomes requires robust statistical methods that can handle complex, longitudinal data and inherent uncertainties. Existing approaches often fail to adequately account for spatial and temporal heterogeneity in risk, limiting the precision of impact assessments. ", "purpose and objectives": "This study aimed to develop and validate a novel Bayesian hierarchical modelling framework to quantify the reduction in all-cause mortality risk attributable to the national CHC system. The objective was to provide a methodological advance for causal inference in health systems evaluation. ", "methodology": "We constructed a spatio-temporal model using district-level panel data. The core model is specified as y{it \ (), \ () = \ + \ CHCit + ui + vt +, where yit is the mortality count, CHCit is the treatment variable (CHC coverage), ui and vt are structured random effects, and is an unstructured interaction effect. We used Hamiltonian Monte Carlo for inference, with posterior probability intervals reported. ", "findings": "The model estimated a 22. 4% reduction (95% credible interval: 18. 1% to 26. 5%) in all-cause mortality risk associated with full CHC coverage. The spatial random effects term uᵢ was significant, indicating substantial unobserved district-level heterogeneity. The methodological assessment confirmed the model's superiority in bias reduction compared to standard difference-in-differences estimators. ", "conclusion": "The proposed Bayesian hierarchical model offers a rigorous and flexible tool for evaluating health system interventions, effectively isolating programme effect from confounding spatial and temporal trends. It represents a significant methodological contribution to health policy analytics. ", "recommendations": "We recommend the adoption of this modelling framework for the ongoing evaluation of the CHC system and for similar evaluations in
Jean de Dieu Uwimana (Fri,) studied this question.
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