{ "background": "Public health surveillance systems are critical for disease control, yet methodological frameworks for evaluating their adoption and impact in low-resource settings remain underdeveloped. Existing assessments often lack robust counterfactuals, limiting causal inference on the effectiveness of system enhancements and policy interventions.", "purpose and objectives": "This protocol details a methodological evaluation to quantify the causal effect of a national integrated disease surveillance and response (IDSR) strategy on its adoption across healthcare facilities. The primary objective is to estimate the strategy's average treatment effect on the treated (ATT) using a quasi-experimental design.", "methodology": "We employ a difference-in-differences model, leveraging the phased rollout of the IDSR strategy. The model is specified as $Y{it} = \\beta0 + \\beta1 (Treati \\times Postt) + \\gammai + \\deltat + \\epsilon{it}$, where $Y_{it}$ is the adoption rate in facility $i$ at time $t$. Facility and time fixed effects are included. Inference will be based on cluster-robust standard errors at the district level to account for spatial correlation.", "findings": "As a research protocol, this paper does not present empirical results. Anticipated findings include a quantitative estimate of the ATT, with a hypothesised positive direction and a magnitude exceeding a 15-percentage-point increase in adoption rates among intervention facilities. The analysis will test for parallel pre-trends as a key model assumption.", "conclusion": "The proposed methodology is designed to provide a rigorous, evidence-based assessment of surveillance system strengthening, moving beyond descriptive metrics to causal attribution.", "recommendations": "We recommend the application of this quasi-experimental framework for evaluating other health system interventions in similar contexts, emphasising the need for careful selection of control groups and validation of modelling assumptions.", "key words": "health surveillance, impact evaluation, difference-in-differences, causal inference, health systems, Tanzania", "contribution statement": "This