"background": "Public health surveillance systems are critical for disease control, yet rigorous, longitudinal evaluations of their cost-effectiveness in low-resource settings are scarce. Existing assessments often lack robust counterfactuals and longitudinal rigour, limiting evidence for resource allocation. ", "purpose and objectives": "This study aims to develop and apply a novel longitudinal difference-in-differences (DiD) model to evaluate the cost-effectiveness of integrated public health surveillance systems, using Uganda as a case study. The primary objective is to quantify the causal impact of surveillance enhancements on key health outcomes relative to their economic cost. ", "methodology": "A longitudinal study design was employed, analysing panel data from health facilities. The core econometric model is a two-way fixed effects DiD specification: Y{it = \ + \ (Treatmentit) + \ + \ +, where Yit is the outcome for facility i in period t. Treatment assignment was staggered. Inference was based on cluster-robust standard errors at the district level. Cost data were integrated to calculate incremental cost-effectiveness ratios. ", "findings": "The analysis indicates a statistically significant positive effect of enhanced surveillance on outbreak detection timeliness. Preliminary model estimates suggest a reduction in median detection delay by approximately 40% (95% CI: 32% to 48%) in treated districts compared to controls. Full cost-effectiveness results are pending finalisation of longitudinal cost data. ", "conclusion": "The proposed longitudinal DiD framework provides a methodologically robust approach for causal inference in surveillance system evaluation. Initial findings support the effectiveness of system enhancements, though final cost-effectiveness conclusions await complete economic analysis. ", "recommendations": "Health ministries should adopt longitudinal, counterfactual-based models for surveillance investment decisions. Future research should integrate real-time data streams and explore heterogeneity in treatment effects across different system components. ", "key words": "cost-effectiveness analysis, difference-in-differences, health economics, longitudinal study, public health
Muwanga et al. (Thu,) studied this question.