{ "background": "Public health surveillance systems are critical for early disease detection and response, yet their methodological evaluation, particularly regarding 'yield' or case detection efficiency, remains underdeveloped in many settings. Robust, quantitative frameworks are needed to assess the impact of system enhancements.", "purpose and objectives": "This protocol details a methodological approach to evaluate the yield improvement of a surveillance system intervention in Ghana. The primary objective is to quantify the causal effect of the intervention on reported case counts, controlling for underlying temporal trends and confounding factors.", "methodology": "A quasi-experimental difference-in-differences (DiD) design will be employed. The model is specified as $Y{it} = \\beta0 + \\beta1 \\text{Intervention}{i} + \\beta2 \\text{Post}{t} + \\delta (\\text{Intervention}{i} \\times \\text{Post}{t}) + \\epsilon{it}$, where $Y{it}$ is the log-transformed case count. Intervention districts will be matched with control districts using propensity scores. Inference will rely on cluster-robust standard errors at the district level to account for serial correlation.", "findings": "As a research protocol, this paper does not present empirical results. The anticipated primary finding is the DiD estimator ($\\delta$), which will quantify the percentage change in yield attributable to the intervention. A positive and statistically significant $\\delta$ would indicate a successful system enhancement.", "conclusion": "The proposed methodology provides a rigorous, transparent framework for evaluating surveillance system performance, moving beyond descriptive metrics to causal inference.", "recommendations": "Researchers and public health authorities should adopt quasi-experimental designs, such as DiD, for the robust evaluation of health system interventions. Future work should consider integrating data quality metrics into the yield assessment.", "key words": "surveillance evaluation, difference-in-differences, causal inference, public health, Ghana, methodological protocol", "contribution statement":