A Bayesian Hierarchical Modelling Approach to the Cost-Effectiveness of Community Health Centre Systems in Uganda

"background": "Community health centres are a cornerstone of primary healthcare delivery in many African nations, yet robust, data-driven 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 novel Bayesian hierarchical model to evaluate the cost-effectiveness of community health centre systems, using Uganda as a case study. The primary objective was to quantify the incremental cost per disability-adjusted life year (DALY) averted, while formally propagating uncertainty from multiple data levels. ", "methodology": "We conducted an intervention study analysing operational and health outcome data from a network of community health centres. The core methodological innovation is a Bayesian hierarchical model specified as: -Effectiveness{ij \ (\ + \ Xij, \²), \ \ (\\, \\²), where i indexes patients and j indexes centres. Parameters were estimated using Hamiltonian Monte Carlo, with cost-effectiveness acceptability curves derived from the posterior distributions. ", "findings": "The model estimated a median incremental cost-effectiveness ratio (ICER) of US 42. 50 (95% credible interval: 28. 10 to 72. 30) per DALY averted. The probability of the system being cost-effective at a willingness-to-pay threshold of US 100 per DALY was 0. 92. Substantial heterogeneity was identified between centres, with the random effects standard deviation \\ estimated at 0. 31 on the log scale. ", "conclusion": "The community health centre system in the studied context represents a cost-effective intervention according to common benchmarks. The Bayesian hierarchical approach provided a statistically coherent framework for handling multi-level uncertainty and centre variation, offering a superior alternative to deterministic or single-level analyses. ",