Evaluating Surveillance System Efficiency in Rwanda: A Difference-in-Differences Analysis of Public Health Gains, 2000–2026

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 Yᵢₜ = β₀ + β₁ (Treatᵢ × Postₜ) + γᵢ + δₜ + εᵢₜ, 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.