A Quasi-Experimental Evaluation of Public Health Surveillance System Reliability in Rwanda, 2000–2025

"background": "Public health surveillance systems are critical for disease control, yet robust methodological frameworks for evaluating their long-term operational reliability in low-resource settings remain underdeveloped. ", "purpose and objectives": "This study aimed to develop and apply a quasi-experimental design to quantitatively assess the reliability of the national integrated disease surveillance and response (IDSR) system, focusing on data completeness, timeliness, and consistency. ", "methodology": "We employed a controlled interrupted time series analysis, comparing surveillance metrics from intervention districts implementing an enhanced electronic reporting protocol with matched control districts using the legacy system. Reliability was modelled using a generalised estimating equations approach: Y{it = \0 + \1 Tt + \2 Xit + \3 (Tt \ Xit) +, where Yit is the composite reliability score for district i at time t, Tt is time, and X₈ₓ is the intervention indicator. Robust standard errors were clustered at the district level. ", "findings": "The enhanced reporting protocol significantly improved system reliability. The intervention was associated with a 22. 4 percentage point increase (95% CI: 18. 1 to 26. 7) in the composite reliability score, driven predominantly by improvements in reporting timeliness. ", "conclusion": "Targeted enhancements to surveillance infrastructure, when deployed within a structured support framework, can substantially improve the operational reliability of public health surveillance in resource-limited contexts. ", "recommendations": "National health authorities should institutionalise the quasi-experimental evaluation framework for ongoing system assessment and consider scaling up the enhanced electronic reporting protocol, with dedicated training and infrastructural support. ", "key words": "surveillance evaluation, health information systems, interrupted time series, health systems strengthening, data quality", "contribution statement": "This paper provides a novel, replicable quasi-experimental methodology for quantifying surveillance system reliability, generating robust causal