A Bayesian Hierarchical Modelling Approach to Evaluating Clinical Outcomes in Rwandan Community Health Centres: An Intervention Study

"background": "Community health centres are critical for delivering primary care in Rwanda, yet robust methods for evaluating their clinical performance across diverse settings are lacking. Existing approaches often fail to account for hierarchical data structures and inherent uncertainty in outcome measurement. ", "purpose and objectives": "This study aimed to develop and apply a novel Bayesian hierarchical model to evaluate clinical outcomes across a network of community health centres, quantifying the impact of a structured support intervention on key performance indicators. ", "methodology": "We conducted an intervention study across multiple centres. The core methodological innovation is a Bayesian hierarchical model specified as y{ij \ (, nij), () = \ + \ Xij + ui + vj, with ui \ N (0, \²) and vj \ N (0, \ᵥ²) representing centre and temporal random effects. Parameters were estimated using Hamiltonian Monte Carlo, with inference based on posterior credible intervals. ", "findings": "The model successfully quantified intervention effects while partitioning variance components. The posterior median for the intervention coefficient (\) was 0. 42, with a 95% credible interval of 0. 18, 0. 67, indicating a positive effect. The model attributed approximately 15% of the total variance in outcomes to differences between individual centres. ", "conclusion": "The Bayesian hierarchical modelling approach provides a statistically rigorous framework for evaluating clinical outcomes in decentralised community health systems, offering superior handling of uncertainty and multi-level data compared to conventional methods. ", "recommendations": "Health systems researchers should adopt Bayesian hierarchical models for performance evaluation where data are clustered. Programme implementers should utilise such models to identify centres requiring targeted support, moving beyond aggregate averages. ", "key words": "Bayesian hierarchical model, health systems evaluation, clinical outcomes, community health, primary care, Rwanda", "contribution statement":