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The Spatial Correlations of Health Resource Agglomeration Capacities and Their Influencing Factors: Evidence from China

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  • Qingbin Guo

    (School of Economics, Hainan University, Haikou 570228, China)

  • Kang Luo

    (School of Economics & Management, Nanchang University, Nanchang 330031, China)

  • Ruodi Hu

    (School of Economics, Hainan University, Haikou 570228, China)

Abstract

We measured the health resource agglomeration capacities of 31 Chinese provinces (or municipalities) in 2004–2018 based on the entropy weight method. Using a modified spatial gravity model, we constructed and analyzed the spatial correlation network of these health resource agglomeration capacities and their influencing factors through social network analysis. We found that: (i) China’s health resource agglomeration capacity had a gradual strengthening trend, with capacity weakening from east to west (strongest in the eastern region, second strongest in the central region, and weakest in the western region). (ii) The spatial network of such capacities became more densely connected, and the network density and level (efficiency) showed an upward (downward) trend. (iii) In terms of centrality, the high-ranking provinces (or municipalities) were Beijing, Shanghai, Jiangsu, Zhejiang, Guangdong, Shandong, Hunan, Hubei, Fujian, Anhui, Jiangxi, and Tianjin, while the low-ranking were Tibet, Qinghai, Gansu, Ningxia, Inner Mongolia, Heilongjiang, Yunnan, Guizhou, Xinjiang, Hainan, Shaanxi, and Shanxi. (iv) Block 1 (eight provinces or municipalities), including Beijing, Tianjin, and Hebei, had a “net spillover” effect in the spatial network of health resource agglomeration capacities; Block 2, (seven provinces or municipalities), including Shanghai, Jiangsu, and Zhejiang, had a “bidirectional spillover” effect in the spatial network; Block 3 (seven provinces or municipalities), including Anhui, Hubei, and Hunan, had a “mediator” effect in the network; and Block 4, (nine provinces or municipalities), including Sichuan, Guizhou, and Tibet, had a “net beneficial” effect in the network. (v) The economic development, urbanization wage, and financial health expenditure levels, and population size had significant positive correlations with the spatial network of health resource agglomeration capacities. Policy recommendations to enhance the radiating role of health resources in core provinces (or municipalities), rationally allocate health resources, and transform ideas to support public health resource services were provided.

Suggested Citation

  • Qingbin Guo & Kang Luo & Ruodi Hu, 2020. "The Spatial Correlations of Health Resource Agglomeration Capacities and Their Influencing Factors: Evidence from China," IJERPH, MDPI, vol. 17(22), pages 1-18, November.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:22:p:8705-:d:449765
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    References listed on IDEAS

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    1. Qingbin Guo & Kang Luo, 2019. "Concentration of Healthcare Resources in China: The Spatial–Temporal Evolution and Its Spatial Drivers," IJERPH, MDPI, vol. 16(23), pages 1-14, November.
    2. Wenhua Wang & Ekaterina (Katya) Loban & Emilie Dionne, 2019. "Public Hospitals in China: Is There a Variation in Patient Experience with Inpatient Care," IJERPH, MDPI, vol. 16(2), pages 1-12, January.
    3. Teresa Romanillos & Roser Maneja & Diego Varga & Llorenç Badiella & Martí Boada, 2018. "Protected Natural Areas: In Sickness and in Health," IJERPH, MDPI, vol. 15(10), pages 1-19, October.
    4. Weon-Young Lee & Ian Shaw, 2014. "The Impact of Out-of-Pocket Payments on Health Care Inequity: The Case of National Health Insurance in South Korea," IJERPH, MDPI, vol. 11(7), pages 1-15, July.
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    1. Mingyue Wen & Liao Liao & Yilin Wang & Xunzhi Zhou, 2022. "Effects of Healthcare Policies and Reforms at the Primary Level in China: From the Evidence of Shenzhen Primary Care Reforms from 2018 to 2019," IJERPH, MDPI, vol. 19(4), pages 1-19, February.

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