Emergency care units in Senegal face variability in clinical outcomes due to differences in infrastructure, staff training, and patient demographics. A Bayesian hierarchical model was employed to analyse clinical outcome data from multiple emergency care units. The model accounts for both unit-level (e. g. , staff competence) and patient-level (e. g. , age, comorbidities) variability. The model identified significant unit-specific effects on clinical outcomes, with some units showing better performance than others, particularly in the treatment of respiratory infections. The Bayesian hierarchical model provides a nuanced understanding of how different emergency care units operate and can inform policy decisions aimed at improving patient care across Senegal. Health policymakers should prioritise training for staff in high-risk areas and allocate resources to support these identified units. Bayesian Hierarchical Model, Emergency Care Units, Clinical Outcomes, Senegal Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Wade et al. (Fri,) studied this question.