A CFA-Clustering Framework for Assessing Health System Performance Considering Healthcare Resources, Socioeconomic Conditions, and Accessibility

Traditional evaluations of health system performance often emphasize clinical indicators, overlooking essential social, economic, and accessibility dimensions that are central to achieving universal goals of good health and well-being. This study introduces a multidimensional, theory-driven framework for evaluating regional health system performance in contexts with devolved or locally governed health structures. Using confirmatory factor analysis, 10 input indicators are empirically grouped into three latent variables: healthcare resources, socioeconomic conditions, and accessibility. The resulting model meets established thresholds for fit indices and demonstrates temporal measurement invariance, supporting its reliability and stability across three reference years - 2019, 2021, and 2023. Validated factor scores are subsequently used in cluster analysis. Applied to Indonesia, a geographically diverse country with decentralized health governance, results reveal persistent interprovincial disparities, with 29 provinces maintaining consistent cluster memberships over time. The proposed approach offers a practical tool for identifying regional disparities, informing equitable resource allocation, and supporting data-driven health policy.