Abstract
{ "background": "Evaluating the performance of community health centres in low-resource settings requires robust statistical methods to handle inherent data complexities, such as clustering and missing observations. Current approaches often lack the flexibility to model these hierarchical structures and quantify uncertainty adequately.", "purpose and objectives": "This study aimed to methodologically evaluate a Bayesian hierarchical modelling framework for analysing clinical outcomes data from a network of community health centres. The objective was to assess its utility for performance measurement and comparison under real-world data constraints.", "methodology": "We developed a three-level hierarchical model where patient outcomes $y{ijk} \\sim \\text{Bernoulli}(\\theta{ijk})$ with $\\text{logit}(\\theta{ijk}) = \\alpha + uj + vk$, and $uj \\sim N(0, \\sigma^2u)$, $vk \\sim N(0, \\sigma^2v)$. The model was fitted using Markov chain Monte Carlo methods to retrospective clinical data. Convergence was assessed using the Gelman-Rubin statistic.", "findings": "The model successfully quantified centre-level variation while providing probabilistic rankings. A key finding was that the posterior probability of a centre being in the bottom quintile of performance exceeded 0.85 for 12% of facilities. Parameter estimates were robust to different prior specifications, with 95% credible intervals for the facility-level variance $\\sigma^2v$ excluding zero.", "conclusion": "The Bayesian hierarchical model offers a statistically rigorous framework for performance evaluation, effectively handling multi-level data and providing interpretable probabilistic outputs for decision-makers.", "recommendations": "Health system managers should adopt similar hierarchical modelling techniques for routine performance analytics. Future research should integrate this approach with cost-effectiveness data and explore dynamic extensions for longitudinal monitoring.", "key words": "Bayesian inference, health systems research, performance measurement, hierarchical model, clinical audit", "contribution statement": "This paper provides a novel methodological application of Bayesian hierarchical modelling to the evaluation of community health centre