"background": "Public health surveillance systems are critical for disease control, yet their methodological evaluation, particularly regarding efficiency, remains underdeveloped in many low-resource settings. There is a recognised need for robust quantitative frameworks to assess the impact of system enhancements. ", "purpose and objectives": "This study aimed to develop and apply a novel quasi-experimental framework to quantify efficiency gains from a nationwide digital integration intervention within Uganda's public health surveillance architecture. ", "methodology": "We employed a difference-in-differences (DiD) design, analysing longitudinal, facility-level data from sentinel surveillance sites. The core model was specified as Y{it = \0 + \1 + \2 + \ (\) + \₈ₓ, where \ captures the causal effect. Inference was based on cluster-robust standard errors at the district level. ", "findings": "The digital integration intervention significantly reduced mean reporting latency by 4. 2 days (95% CI: 2. 8 to 5. 6; p<0. 001). This represented a 38% improvement relative to control facilities, which showed no statistically significant change over the same period. ", "conclusion": "The applied DiD model provides a rigorous methodological proof-of-concept for evaluating surveillance system performance, demonstrating substantial and significant efficiency gains from digital integration. ", "recommendations": "Policy makers should prioritise investment in integrated digital reporting infrastructures. Future evaluations of public health systems should adopt quasi-experimental designs to strengthen causal inference. ", "key words": "surveillance evaluation, health systems, digital health, quasi-experimental design, causal inference, Sub-Saharan Africa", "contribution statement": "This paper provides the first application of a difference-in-differences framework to quantify the causal impact of a digital intervention on surveillance efficiency metrics in a low-resource setting, offering
Nakato Nalwadda (Sat,) studied this question.