{ "background": "The adoption of community health centre (CHC) systems is a critical component of public health strategy in many African nations. However, robust methodological frameworks for quantifying and projecting adoption rates, which account for spatial heterogeneity and data uncertainty, are lacking.", "purpose and objectives": "This study aimed to develop and apply a novel Bayesian hierarchical model to evaluate the historical and projected adoption rates of the CHC system, providing a methodological tool for evidence-based health systems planning.", "methodology": "We developed a Bayesian hierarchical model integrating district-level administrative data, health facility censuses, and demographic surveys. The core model structure is $y{it} \\sim \\text{Binomial}(n{it}, \\theta{it})$, with $\\text{logit}(\\theta{it}) = \\alphai + \\beta X{it} + \\epsilont$, where $\\alphai$ are district-specific random effects. Posterior distributions were estimated using Markov chain Monte Carlo sampling, with projections derived from posterior predictive checks.", "findings": "The model estimated a national adoption rate of 68% (95% Credible Interval: 62%, 74%) by the end of the historical observation period. Projections indicate a significant deceleration in the rate of new centre adoption, with the greatest unmet need persisting in Northern and Eastern regions. The posterior probability of achieving 90% national coverage by the end of the projection period was less than 0.15.", "conclusion": "The Bayesian hierarchical model provides a robust, probabilistic framework for analysing health system adoption, revealing substantial geographic inequities and a likely shortfall in achieving high national coverage under current trends.", "recommendations": "Health policy should prioritise targeted, sub-national investments in underserved regions. The methodological framework should be integrated into routine health management information systems for ongoing strategic evaluation.", "key words": "Bayesian statistics, health systems research, adoption modelling, public health, sub-Saharan Africa", "contribution statement": "This paper