Multilevel Regression Analysis to Measure Adoption Rates of District Hospital Systems in Uganda: A Methodological Evaluation

The adoption rates of district hospital systems in Uganda have been a subject of interest for policymakers aiming to improve healthcare access and quality. The research employs multilevel logistic regression models to analyse data from multiple levels, including both individual healthcare providers and administrative units. The analysis accounts for the hierarchical structure of the data using robust standard errors. A significant proportion (35%) of district hospitals reported adopting new electronic health record systems within two years post-implementation, with factors such as higher initial investment funding and better infrastructure availability being positively correlated with adoption rates. The multilevel regression analysis method demonstrated robustness in measuring adoption rates while accounting for contextual influences at different levels. This study provides a reliable framework for future policy evaluations of district hospital systems. Policymakers should prioritise investment in infrastructure and funding to enhance the likelihood of successful system adoption, particularly in regions with limited resources. District hospitals, Uganda, Adoption rates, Multilevel regression analysis, Healthcare systems Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.