{ "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 $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\times \\text{Post}t) + \\epsilon_{it}$, where $\\delta$ 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