Public health surveillance systems are critical for early detection and response to disease threats, yet their performance in reducing population-level risk is often inadequately quantified. In many settings, evaluations remain cross-sectional or descriptive, lacking longitudinal rigour. This case study aims to methodologically evaluate the performance of integrated disease surveillance systems in an East African context. Its objective is to estimate the causal effect of surveillance system enhancements on district-level public health risk reduction over time. We employ a panel-data econometric approach using annual district-level data. The core specification is a two-way fixed effects model: $Risk_{it} = \beta_0 + \beta_1 Surveillance_{it} + \theta_i + \lambda_t + \epsilon_{it}$, where $\theta_i$ and $\lambda_t$ represent district and year fixed effects. Inference is based on cluster-robust standard errors at the district level. A one-standard-deviation improvement in the surveillance performance index was associated with a statistically significant 18% reduction in the composite public health risk score (95% CI: 12% to 24%). The effect was heterogeneous, with stronger associations observed in predominantly rural districts. The application of panel-data methods provides robust, longitudinal evidence that strengthening surveillance infrastructure directly contributes to measurable reductions in population health risk. Investment should prioritise integrated, electronic surveillance platforms with real-time analytics. Evaluation frameworks must adopt longitudinal, quantitative designs to better capture system performance and guide resource allocation. public health surveillance, panel data, fixed effects, risk assessment, health systems evaluation, East Africa This study provides a novel methodological application of econometric panel models to quantify the longitudinal impact of surveillance system performance on population health risk, generating evidence for targeted investment.