Methodological Evaluation of Public Health Surveillance Systems in Tanzania Using a Difference-in-Differences Approach to Assess Efficiency Gains

Public health surveillance systems in Tanzania are critical for monitoring disease trends and responding to public health threats efficiently. A DID model was applied to assess changes in surveillance accuracy before and after implementing new methodologies. Uncertainty in findings is addressed through robust standard errors. Improved data quality led to a 20% reduction in reporting delays, with an estimated effect size of -0.15 days per week for each additional month post-intervention (95% CI: -0.21 to -0.08). The DID model successfully highlighted efficiency gains from new surveillance methods. Continued monitoring and refinement of data collection processes are recommended to sustain these improvements. Public health, Surveillance systems, Difference-in-Differences, Efficiency gains, Tanzania Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.