Abstract
{ "background": "The adoption of health information systems in community health centres is critical for improving service delivery and health outcomes. In Uganda, efforts to scale up these systems have been ongoing, yet a comprehensive methodological assessment of how their adoption is quantitatively measured using longitudinal data is lacking.", "purpose and objectives": "This systematic review aims to identify, evaluate, and synthesise methodological frameworks used in panel-data estimation for measuring health systems adoption rates in Ugandan community health centres, assessing their robustness and applicability.", "methodology": "A systematic search of multiple electronic databases was conducted following PRISMA guidelines. Studies employing panel-data methods (e.g., fixed/random effects, difference-in-differences) to analyse adoption in the specified context were included. Quality and methodological rigour were appraised using a bespoke tool focusing on model specification, handling of unobserved heterogeneity, and inference techniques.", "findings": "Of 42 included studies, a dominant theme was the reliance on static linear models, with 65% employing a basic two-way fixed effects specification: $y{it} = \\alphai + \\lambdat + \\beta x{it} + \\epsilon_{it}$. Fewer than 20% accounted for spatial autocorrelation or used robust standard errors clustered at the facility level, raising concerns about underestimated uncertainty. The methodological sophistication did not significantly increase over time.", "conclusion": "The methodological approaches in extant literature are often insufficient to address the complex, multi-level determinants of systems adoption, potentially leading to biased estimates and overstated policy conclusions.", "recommendations": "Future research should employ more dynamic panel models (e.g., system GMM) to account for state dependence, explicitly test for and model spatial interdependence, and consistently report cluster-robust inference. Funders should require detailed methodological appendices.", "key words": "health information systems, panel data, fixed effects, adoption metrics, health systems research, longitudinal analysis", "contribution statement": "This review provides the first dedicated methodological critique of panel