Public health surveillance systems are critical for disease control and health policy, yet methodological rigour in evaluating their adoption, particularly in low-resource settings, remains inconsistent. Rwanda has implemented several such systems, but a comprehensive, quantitative synthesis of adoption drivers is lacking. This meta-analysis aims to methodologically evaluate studies on public health surveillance systems in Rwanda and to estimate the key determinants of their adoption rates using panel-data econometric techniques. A systematic search identified relevant studies. Methodological quality was assessed using a modified Downs and Black checklist. Quantitative data were synthesised via a random-effects meta-analysis of regression coefficients. The core panel-data model estimated was: Adoption₈ₓ = ₀ + ₁ X₈ₓ + ᵢ + ₜ + ₈ₓ, where ᵢ denotes district-level fixed effects and robust standard errors were clustered at the district level. The methodological review revealed significant heterogeneity in study design and reporting quality. The panel-data estimation identified a strong positive association between healthcare worker training intensity and system adoption, with a pooled odds ratio of 2. 45 (95% CI 1. 88 to 3. 19). Infrastructure limitations were a consistently reported barrier. Adoption of surveillance systems is significantly influenced by targeted human resource investments, while physical infrastructure constraints persist. Methodological standardisation in future primary research is urgently needed. Policymakers should prioritise sustained training programmes alongside infrastructure development. Researchers must adopt more consistent, longitudinal study designs and report key statistical parameters to facilitate future synthesis. health surveillance, meta-regression, adoption models, health systems, econometric evaluation This study provides the first quantitative synthesis of adoption drivers for public health surveillance in Rwanda, introducing a novel application of panel-data meta-analysis to this interdisciplinary field.
Kayitesi et al. (Sat,) studied this question.