Evaluating Efficiency Gains in Ghana's Public Health Surveillance Systems: A Methodological Difference-in-Differences Analysis

"background": "Public health surveillance systems are critical for early disease detection and response, yet their operational efficiency in resource-limited settings is often suboptimal. In Ghana, recent investments aimed to modernise these systems, but robust methodological evaluations of their impact on efficiency are lacking. ", "purpose and objectives": "This study aimed to quantify the causal effect of a national intervention to digitise and streamline surveillance reporting on system efficiency gains. The primary objective was to measure changes in timeliness and data completeness following the intervention's rollout. ", "methodology": "We employed a quasi-experimental difference-in-differences design, comparing districts that received the intervention (treatment group) with matched control districts that did not. The core statistical model was Y{dt = \0 + \1 + \2 + \3 (\) +, where Y₃ₓ is the efficiency metric for district d at time t. Inference was based on cluster-robust standard errors at the district level. ", "findings": "The intervention significantly improved reporting timeliness. The adjusted differential change was a reduction of 2. 3 days (95% CI: 1. 7 to 2. 9) in mean reporting delay for notifiable diseases in treatment districts relative to controls. Data completeness also increased by an absolute 15 percentage points in the intervention group. ", "conclusion": "The methodological application of difference-in-differences provided robust evidence that the digitisation intervention causally improved key efficiency parameters of the surveillance system. ", "recommendations": "Policy should prioritise the scaled deployment of the digital system nationwide, accompanied by continuous training and technical support to sustain gains. Future evaluations should incorporate cost-effectiveness analyses. ", "key words": "health surveillance, efficiency, difference-in-differences, quasi-experimental, digital health, health systems strengthening",