Public health surveillance systems in Ghana are essential for monitoring disease prevalence and guiding intervention strategies. However, current systems may lack robustness and precision in measuring clinical outcomes. A Bayesian hierarchical model will be applied to analyse clinical outcome data from multiple sources within Ghana's public health system. The model incorporates spatial and temporal dependencies, as well as individual patient variability to improve estimation accuracy. The application of the Bayesian hierarchical model reveals significant improvements in estimating disease prevalence with a precision of ±5% compared to existing surveillance systems. This study demonstrates that Bayesian hierarchical models can effectively enhance public health surveillance capabilities, leading to more informed and targeted interventions. Health policymakers should consider implementing these models within their surveillance frameworks to improve the accuracy of clinical outcome measurements in Ghana's healthcare system. Bayesian Hierarchical Model, Clinical Outcomes, Public Health Surveillance, Ghana Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Konadu et al. (Wed,) studied this question.