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Measuring the Differences of Public Health Service Facilities and Their Influencing Factors

Author

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  • Shihang Fu

    (School of Resource and Environmental Science, Wuhan University, Wuhan 430072, China)

  • Yaolin Liu

    (School of Resource and Environmental Science, Wuhan University, Wuhan 430072, China)

  • Ying Fang

    (School of Resource and Environmental Science, Wuhan University, Wuhan 430072, China)

Abstract

The equitable distribution of public health facilities is a major concern of urban planners. Previous studies have explored the balance and fairness of various medical resource distributions using the accessibility of in-demand public medical service facilities while ignoring the differences in the supply of public medical service facilities. First aid data with location information and patient preference information can reflect the ability of each hospital and the health inequities in cities. Determining which factors affect the measured differences in public medical service facilities and how to alter these factors will help researchers formulate targeted policies to solve the current resource-balance situation of the Ministry of Public Health. In this study, we propose a method to measure the differences in influence among hospitals based on actual medical behavior and use geographically weighted regression (GWR) to analyze the spatial correlations among the location, medical equipment, medical ability, and influencing factors of each hospital. The results show that Wuhan presents obvious health inequality, with the high-grade hospitals having spatial agglomeration in the city-center area, while the number and quality of hospitals in the peripheral areas are lower than those in the central area; thus, the hospitals in these peripheral areas need to be further improved. The method used in this study can measure differences in the influence of public medical service facilities, and the results are consistent with the measured differences at hospital level. Hospital influence is not only related to the equipment and medical ability of each hospital but is also affected by location factors. This method illustrates the necessity of conducting more empirical research on the public medical service supply to provide a scientific basis for formulating targeted policies from a new perspective.

Suggested Citation

  • Shihang Fu & Yaolin Liu & Ying Fang, 2021. "Measuring the Differences of Public Health Service Facilities and Their Influencing Factors," Land, MDPI, vol. 10(11), pages 1-15, November.
  • Handle: RePEc:gam:jlands:v:10:y:2021:i:11:p:1225-:d:676529
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    References listed on IDEAS

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    4. Hu, Lirong & He, Shenjing & Luo, Yun & Su, Shiliang & Xin, Jing & Weng, Min, 2020. "A social-media-based approach to assessing the effectiveness of equitable housing policy in mitigating education accessibility induced social inequalities in Shanghai, China," Land Use Policy, Elsevier, vol. 94(C).
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    Cited by:

    1. Mengge Du & Shichen Zhao, 2022. "An Equity Evaluation on Accessibility of Primary Healthcare Facilities by Using V2SFCA Method: Taking Fukuoka City, Japan, as a Case Study," Land, MDPI, vol. 11(5), pages 1-22, April.
    2. Xuefeng Tan & Chenggen Guo & Pu Sun, 2023. "Study on Rationality of Public Fitness Service Facilities in Beijing Based on GIS," Sustainability, MDPI, vol. 15(2), pages 1-16, January.
    3. Hong Xu & Jin Zhao & Xincan Yu, 2023. "A Community-Oriented Accessibility Index of Public Health Service Facilities: A Case Study of Wuchang District, Wuhan, China," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    4. Zijing Ye & Ruisi Li & Jing Wu, 2022. "Dynamic Demand Evaluation of COVID-19 Medical Facilities in Wuhan Based on Public Sentiment," IJERPH, MDPI, vol. 19(12), pages 1-22, June.
    5. Jiansheng Wu & Tengyun Yi & Han Wang & Hongliang Wang & Jiayi Fu & Yuhao Zhao, 2022. "Evaluation of Medical Carrying Capacity for Megacities from a Traffic Analysis Zone View: A Case Study in Shenzhen, China," Land, MDPI, vol. 11(6), pages 1-19, June.

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