The inadequate implementation of community health centres (CHCs) in Nigeria has led to significant disparities in healthcare access and quality. A Bayesian hierarchical model was employed to analyse data collected from multiple CHCs across Nigeria, accounting for variability in service delivery and patient demographics. The analysis revealed a 20% reduction in healthcare-associated infections (HAI) rates when implementing the proposed risk mitigation strategies. The Bayesian hierarchical model demonstrated improved accuracy in predicting HAI reductions compared to traditional methods, highlighting its potential as a robust tool for CHC system enhancement. Health authorities should prioritise further validation and implementation of this model within Nigerian CHCs to improve public health outcomes. Bayesian Hierarchical Model, Community Health Centres, Risk Reduction, Healthcare-associated Infections (HAI), Nigeria Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Agbakpan et al. (Tue,) studied this question.
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