Author:
Qin Afei,Qin Wenzhe,Hu Fangfang,Wang Meiqi,Yang Haifeng,Li Lei,Chen Chiqi,Bao Binghong,Xin Tianjiao,Xu Lingzhong
Abstract
Abstract
Background
There is a dearth of research combining geographical big data on medical resource allocation and growth with various statistical data. Given the recent achievements of China in economic development and healthcare, this study takes China as an example to investigate the dynamic geographical distribution patterns of medical resources, utilizing data on healthcare resources from 290 cities in China, as well as economic and population-related data. The study aims to examine the correlation between economic growth and spatial distribution of medical resources, with the ultimate goal of providing evidence for promoting global health equity.
Methods
The data used in this study was sourced from the China City Statistical Yearbook from 2001 to 2020. Two indicators were employed to measure medical resources: the number of doctors per million population and the number of hospital and clinic beds per million population. We employed dynamic convergence model and fixed-effects model to examine the correlation between economic growth and the spatial distribution of medical resources. Ordinary least squares (OLS) were used to estimate the β values of the samples.
Results
The average GDP for all city samples across all years was 36,019.31 ± 32,029.36, with an average of 2016.31 ± 1104.16 doctors per million people, and an average of 5986.2 ± 6801.67 hospital beds per million people. In the eastern cities, the average GDP for all city samples was 47,672.71 ± 37,850.77, with an average of 2264.58 ± 1288.89 doctors per million people, and an average of 3998.92 ± 1896.49 hospital beds per million people. Cities with initially low medical resources experienced faster growth (all β < 0, P < 0.001). The long-term convergence rate of the geographic distribution of medical resources in China was higher than the short-term convergence rate (|βi + 1| > |βi|, i = 1, 2, 3, …, 9, all β < 0, P < 0.001), and the convergence speed of doctor density exceeded that of bed density (bed: |βi| >doc: |βi|, i = 3, 4, 5, …, 10, P < 0.001). Economic growth significantly affected the convergence speed of medical resources, and this effect was nonlinear (doc: βi < 0, i = 1, 2, 3, …, 9, P < 0.05; bed: βi < 0, i = 1, 2, 3, …, 10, P < 0.01). The heterogeneity between provinces had a notable impact on the convergence of medical resources.
Conclusions
The experiences of China have provided significant insights for nations worldwide. Governments and institutions in all countries worldwide, should actively undertake measures to actively reduce health inequalities. This includes enhancing healthcare standards in impoverished regions, addressing issues of unequal distribution, and emphasizing the examination of social determinants of health within the domain of public health research.
Publisher
Springer Science and Business Media LLC
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