Evaluating the Reliability of Public Health Surveillance Systems in South Africa: A Methodological Application of the Difference-in-Differences Model

{ "background": "Public health surveillance systems are critical for timely disease detection and response, yet their reliability is often assumed rather than rigorously evaluated. In many settings, including South Africa, methodological frameworks for quantifying surveillance performance are underdeveloped.", "purpose and objectives": "This study aimed to develop and apply a quasi-experimental methodology to assess the reliability of a national notifiable disease surveillance system. The primary objective was to estimate the causal effect of a major system intervention on reporting completeness.", "methodology": "We employed a difference-in-differences (DiD) model, using a control group of diseases unaffected by the intervention. The core specification was Ydt = \β0 + \β1 + \β2 + \δ ( \× ) + \εdt, where Ydt is the log reporting rate for disease d at time t. Inference was based on heteroskedasticity-robust standard errors clustered at the disease level.", "findings": "The intervention significantly increased reporting completeness for targeted diseases by an estimated 18.4 percentage points (95% CI: 12.1 to 24.7). The parallel trends assumption, tested via event-study analysis, was not violated in the pre-intervention period.", "conclusion": "The applied DiD model provides a robust methodological framework for evaluating surveillance system reliability, demonstrating that the specific intervention had a substantial, positive causal impact on performance.", "recommendations": "Health authorities should adopt quasi-experimental evaluation designs for routine surveillance system assessment. Future investments should target the integration of data streams that showed the greatest improvement potential.", "key words": "surveillance evaluation, difference-in-differences, health systems, causal inference, notifiable diseases", "contribution statement": "This paper provides a novel application of the DiD model to public health surveillance evaluation