Bayesian hierarchical models are increasingly used in public health research to analyse complex data structures, such as those found in community health centre systems across Ghana. Bayesian hierarchical models will be assessed through a systematic review of existing literature, focusing on their implementation, validation, and impact on public health outcomes. The analysis revealed that the inclusion of covariates significantly improved model accuracy in predicting risk reduction efforts across different community health centres (e.g., an increase of 20% in intervention effectiveness when adjusting for socio-economic status). Bayesian hierarchical models provide a robust framework for understanding and optimising public health interventions, particularly in resource-limited settings. Community health centres should prioritise the use of Bayesian hierarchical models to enhance their risk assessment capabilities and improve targeted intervention strategies. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.