Public health surveillance systems are critical for disease control, yet rigorous methodological evaluations of their efficiency and impact on health outcomes are limited, particularly in resource-constrained settings. This case study aims to methodologically evaluate the efficiency gains of a national integrated disease surveillance system. Its objective is to quantify the system's causal effect on key public health indicators. A quasi-experimental difference-in-differences design was employed, comparing longitudinal health outcome data from intervention districts with matched control districts. The core statistical model is $Y_{it} = \beta_0 + \beta_1 (Treat_i \times Post_t) + \gamma_i + \delta_t + \epsilon_{it}$, where robust standard errors were clustered at the district level. The surveillance system's implementation was associated with a statistically significant 18% reduction in reported time-to-outbreak detection (95% CI: 12% to 24%). Analysis further indicated substantial improvements in data completeness and timeliness of reporting across the network. The integrated surveillance system demonstrated a significant, positive causal impact on core efficiency metrics, validating the investment as a key component of public health infrastructure. Policy should focus on sustaining and scaling the integrated system. Future research should apply this analytical framework to evaluate surveillance adaptations for non-communicable diseases. surveillance evaluation, difference-in-differences, public health efficiency, health systems research, quasi-experimental design This study provides a novel application of a robust quasi-experimental design to quantify the causal efficiency gains of a national surveillance system, offering a replicable methodological framework for similar evaluations.