Bayesian Hierarchical Model for Assessing Adoption Rates in Public Health Surveillance Systems in Rwanda: A Methodological Evaluation

Public health surveillance systems are essential for monitoring diseases and health trends in Rwanda. However, there is a need to evaluate their effectiveness and adoption rates. A Bayesian hierarchical model was employed to analyse data collected from surveys conducted across different regions of Rwanda. The model accounts for variability between and within regions, ensuring robust estimation of adoption rates. The analysis revealed a significant variation in the adoption rate across regions, with some areas showing adoption rates as high as 70% compared to others at 35%. This highlights the need for tailored strategies to improve coverage. This study provides insights into the effectiveness of public health surveillance systems and offers recommendations for enhancing their utility and adoption. Implementing targeted interventions in regions with lower adoption rates is recommended to increase overall system efficiency and impact. Bayesian Hierarchical Model, Public Health Surveillance Systems, Rwanda, Adoption Rates Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.