ArticleBMC public health2025
Spatial differences, dynamic evolution and influencing factors of the coupling and coordination relationship between health resources allocation and health service utilization in China.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Operational efficiency in public infectious disease hospitals across 31 provinces in China: a three-phase data envelopment analysis.BMJ open · 2026Article
- Coupling Coordination Analysis of Medical Service Capacity, Residents' Health Level and Regional Economic Development in China.Healthcare (Basel, Switzerland) · 2026Article
- Temporal-spatial analysis and driving factors of coupling coordination between population and health services systems in China, 2010-2022.BMC health services research · 2026Article
- The coupling coordination between digital village construction and rural healthcare service efficiency in China: dynamic evolution, spatial difference and driving factors.Frontiers in public health · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
backgroundWith the progress of economic and social development, the demand for healthcare has increased, making the rational allocation of health resources and the efficient utilization of services crucial for public health. However, mismatches between resource allocation and service utilization have led to strain on resources. This study aims to assess the spatiotemporal evolution and influencing factors of the coupling coordination between health resource allocation (HRA) and health service utilization (HSU) in China, contributing to the realization of the "Healthy China" strategy.
methodsBased on the panel data from 2010 to 2022, The entropy method is employed to measure the comprehensive development index of HRA and HSU. The Coupling Coordination Degree Model (CCDM) was used to measure the coupling coordination degree (CCD)of HRA and HSU. The standard deviation ellipse (SDE) and kernel density estimation (KDE) to find the gravity centers movement trends and dynamic evolution of CCD. The XGBoost-SHAP machine learning was used to explore the key factors affecting CCD.
results(1) The comprehensive development levels of the subsystems of HRA and HSU showed an overall upward trend, but they do not rise simultaneously. (2) CCD exhibited a stable upward trend over time, with higher values in the south and east, and lower values in the north and west. (3) The spatial migration of CCD was centered in Henan Province, with shifts from northwest to southwest to northeast, and after initial fluctuations, the distribution became more concentrated, and polarization diminished. (4) CCD is primarily influenced by healthcare investment and economic development. Additionally, influencing factors exhibit regional heterogeneity: in the eastern region, URRBMI coverage positively impacts CCD; in the central region, traffic network density positively influences CCD; and in the western region, population density negatively affects CCD.
conclusionsThis study investigates the spatiotemporal evolution and influencing factors of CCD, suggesting that governments adopt differentiated strategies to enhance CCD and offering a theoretical foundation for implementing the "Healthy China" strategy.
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