"background": "Public health surveillance systems are critical for early detection and response to disease outbreaks. However, rigorous methodological frameworks for evaluating their operational reliability in low-resource settings are lacking, limiting evidence-based improvements. ", "purpose and objectives": "This study aimed to develop and apply a novel quasi-experimental design to quantitatively assess the reliability of integrated disease surveillance and response (IDSR) systems at the district level in Tanzania. ", "methodology": "A controlled interrupted time series analysis was employed, comparing surveillance metrics from intervention districts implementing a new data verification protocol with matched control districts. System reliability was operationalised as the consistency of case reporting completeness. The primary analysis used a generalised estimating equations model: Y{it = \0 + \1 Tt + \2 Xi + \3 (Tt \ Xi) +, where Yit is completeness for district i at time t, Tt is time, and Xᵢ is group assignment. Robust standard errors were clustered at the district level. ", "findings": "The intervention was associated with a significant increase in mean reporting completeness. Specifically, completeness in intervention districts improved by 22. 4 percentage points (95% CI: 18. 1 to 26. 7) relative to controls post-implementation. The reliability coefficient, measured by the intraclass correlation coefficient of reported events, also showed a statistically significant improvement. ", "conclusion": "The applied quasi-experimental design provides a valid and feasible method for quantifying surveillance system reliability. The findings demonstrate that targeted data quality interventions can substantially enhance the consistency of reporting within existing IDSR frameworks. ", "recommendations": "National health authorities should adopt similar methodological evaluations to identify and prioritise investments in surveillance strengthening. The data verification protocol tested here should be considered for scale-up, accompanied by continuous reliability monitoring. ", "key words": "health surveillance, system reliability, quasi-experimental design, interrupted time series
Kipanga et al. (Tue,) studied this question.