Public health surveillance systems in Tanzania have been established to monitor diseases and ensure timely interventions. However, their efficiency and reliability vary among different regions. A Bayesian hierarchical model was employed to analyse data from multiple sites, accounting for spatial and temporal variations in surveillance system performance. The model incorporates uncertainty through robust standard errors. The analysis revealed significant variability in the efficiency of public health surveillance systems across different regions, with some areas showing substantial gains over others. Our Bayesian hierarchical model provides a nuanced understanding of surveillance system performance and highlights potential areas for improvement. Public health authorities should prioritise interventions to enhance surveillance systems where they are currently underperforming. Bayesian Hierarchical Model, Public Health Surveillance, Tanzania, Efficiency Gains Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Muhindo et al. (Fri,) studied this question.