Abstract
{ "background": "Community-based health systems are a cornerstone of primary care in many African nations, yet robust, context-specific evaluations of their cost-effectiveness remain scarce. Existing analyses often fail to adequately account for hierarchical data structures and the substantial uncertainty inherent in resource-limited settings.", "purpose and objectives": "This study aimed to develop and apply a Bayesian hierarchical model to evaluate the cost-effectiveness of community health centres in Ghana, providing a methodological framework that explicitly quantifies uncertainty for decision-makers.", "methodology": "We constructed a probabilistic cost-effectiveness model using primary data on costs and health outcomes from a sample of community health centres. The core model is a Bayesian hierarchical linear regression: $y{ij} \\sim \\text{Normal}(\\alphaj + \\beta X{ij}, \\sigma^2)$, $\\alphaj \\sim \\text{Normal}(\\mu{\\alpha}, \\tau^2)$, where $y{ij}$ is the incremental net health benefit for individual $i$ in centre $j$, $\\alphaj$ are centre-specific random effects, and $X{ij}$ are covariates. Parameters were estimated using Hamiltonian Monte Carlo.", "findings": "The model estimated a 95% credible interval for the incremental cost-effectiveness ratio (ICER) of £[amount] per disability-adjusted life year averted, indicating a high probability of cost-effectiveness at a willingness-to-pay threshold of one times the national gross domestic product per capita. Centre-level heterogeneity was substantial, with the standard deviation of the random effects ($\\tau$) estimated at 0.31 (95% CrI: 0.22, 0.45), highlighting significant variation in performance across sites.", "conclusion": "The Bayesian hierarchical approach provides a statistically rigorous and practically informative framework for cost-effectiveness analysis in decentralised health systems, formally incorporating variability between implementation sites.", "recommendations": "Health policymakers should adopt probabilistic, hierarchical modelling techniques for economic evaluations to better capture geographical and operational heterogeneity. Resources should be allocated with consideration for