Public health surveillance systems are essential for monitoring disease prevalence and guiding public policy in South Africa. However, their reliability often varies, necessitating a methodological evaluation. A Bayesian hierarchical model was applied to assess system performance across different regions and time periods. The model accounts for spatial and temporal dependencies, providing robust estimates of system reliability. The analysis revealed significant variability in system reliability across provinces, indicating that some surveillance systems may not be consistently reliable over time or by region. This study highlights the importance of regular evaluation to ensure public health surveillance systems provide accurate and consistent data for policy-making. Public health officials should consider revising surveillance strategies in regions with unreliable performance, aiming to improve overall system reliability. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.