{ "background": "Public health surveillance is a cornerstone of effective disease control, yet rigorous methodological evaluations of its cost-effectiveness in resource-limited settings are scarce. In Tanzania, multiple surveillance modalities have been implemented, but their comparative economic efficiency remains inadequately synthesised.", "purpose and objectives": "This meta-analysis aimed to methodologically evaluate the cost-effectiveness of diverse public health surveillance systems in Tanzania by synthesising evidence from studies employing difference-in-differences (DiD) modelling, and to assess the robustness of this econometric approach in the surveillance context.", "methodology": "A systematic search identified studies evaluating surveillance interventions. Studies utilising a DiD design to estimate cost-effectiveness were included. Data on intervention characteristics, cost metrics, health outcomes, and DiD model specifications were extracted. The primary synthesis focused on the pooled cost-effectiveness ratio. The core DiD model was specified as $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\cdot \\text{Post}t) + \\epsilon_{it}$, where $\\delta$ is the average treatment effect. Heterogeneity was assessed using the I² statistic, with robust standard errors clustered at the study level.", "findings": "The synthesis of eight qualifying studies indicated that integrated community-based surveillance was the most cost-effective modality, with a pooled incremental cost-effectiveness ratio of US$ 150 (95% CI: 90, 310) per disability-adjusted life year averted compared to facility-based reporting. However, significant heterogeneity was observed (I² = 78%). The methodological appraisal revealed that only 25% of DiD applications adequately tested the parallel trends assumption.", "conclusion": "While certain surveillance approaches demonstrate favourable cost-effectiveness, the methodological rigour of economic evaluations in this field requires strengthening. The DiD model is a potent tool for causal inference in surveillance evaluation but is often applied without necessary validation.", "recommendations