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Spatial differences, dynamic evolution and influencing factors of the coupling and coordination relationship between health resources allocation and health service utilization in China
Spatial differences, dynamic evolution and influencing factors of the coupling and coordination relationship between health resources allocation and health service utilization in China
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Spatial differences, dynamic evolution and influencing factors of the coupling and coordination relationship between health resources allocation and health service utilization in China
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Spatial differences, dynamic evolution and influencing factors of the coupling and coordination relationship between health resources allocation and health service utilization in China
Spatial differences, dynamic evolution and influencing factors of the coupling and coordination relationship between health resources allocation and health service utilization in China
Journal Article

Spatial differences, dynamic evolution and influencing factors of the coupling and coordination relationship between health resources allocation and health service utilization in China

2025
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Overview
Background With 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. Methods Based 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. Conclusions This 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.