Addressing the maldistribution of health resources in Sichuan Province, China: A county-level analysis

Ding, Lanlin; Zhang, Ning; Mao, Ying

doi:10.1371/journal.pone.0250526

Cited by 16 publications

(14 citation statements)

References 36 publications

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“…Yue Zhang et al analyzed equity and efficiency of primary health care resource allocation in mainland China by the Lorenz curve, Gini coefficient, Theil index and health resource density index [22]. In addition, there are also studies that analyzed differences and inequalities in regional distribution of health resources in selected Chinese provinces [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Regional disparities and influencing factors of high quality medical resources distribution in China

Lei

Cao

Wang

et al. 2023

Int J Equity Health

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Background With the gradual increase of residents’ income and the continuous improvement of medical security system, people’s demand for pursuing higher quality and better medical and health services has been released. However, so far little research has been published on China's high quality medical resources (HQMR). This study aims to understand the spatiotemporal variation trend of HQMR from 2006 to 2020, analyze regional disparity of HQMR in 2020, and further explore the main factors influencing the distribution of HQMR in China. Methods The study selected Class III level A hospitals (the highest level medical institutions in China) to represent HQMR. Descriptive statistical methods were used to address the changes in the distribution of HQMR from 2006 to 2020. Lorentz curve, Gini coefficient (G), Theil index (T) and High-quality health resource density index (HHRDI) were used to calculate the degree of inequity. The geographical detector method was used to reveal the key factors influencing the distribution of HQMR. Results The total amount of HQMR in China had increased year by year, from 647 Class III level A hospitals in 2006 to 1580 in 2020. In 2020, G for HQMR by population was 0.166, while by geographic area was 0.614. T was consistent with the results for G, and intra-regional contribution rates were higher than inter-regional contribution rates. HHRDI showed that Beijing, Shanghai, and Tianjin had the highest allocated amounts of HQMR. The results of the geographical detector showed that total health costs, government health expenditure, size of resident populations, GDP, number of medical colleges had a significant impact on the spatial distribution of HQMR and the q values were 0.813, 0.781, 0.719, 0.661, 0.492 respectively. There was an interaction between the influencing factors. Conclusions China's total HQMR is growing rapidly but is relatively inadequate. The distribution of HQMR by population is better than by geography, and the distribution by geography is less equitable. Population size and geographical area both need to be taken into account when formulating policies, rather than simply increasing the number of HQMR.

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Section: Introductionmentioning

confidence: 99%

Regional disparities and influencing factors of high quality medical resources distribution in China

Lei

Cao

Wang

et al. 2023

Int J Equity Health

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“…In the early 21st century, Chinese scholars began to quantitatively study the allocation of medical and health resources by using the Lorenz curve, Gini coefficient, concentration index, Theil index and other methods, and the research scope was mostly at the national and provincial scale [ 36 , 37 ] and less involved at the municipal and below [ 38 ]. The findings show that the inequitable allocation of medical and health resources exists at all scales studied, with geographic allocation being less equitable and differences in medical and health resources allocation being caused between regions and within regional differences [ 39 , 40 , 41 , 42 , 43 , 44 ].…”

Section: Introductionmentioning

confidence: 99%

Research on the Equity and Influencing Factors of Medical and Health Resources Allocation in the Context of COVID-19: A Case of Taiyuan, China

Zhang

Guo

2022

Healthcare

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COVID-19 has killed millions of people worldwide. As a result, medical and health resources continue to be strained, posing a great threat to people’s safety and economic and social development. This paper built the index system of influencing factors of medical and health resources containing the economy, population and society, and then classified Taiyuan into three types of regions by cluster analysis. The Gini coefficient, Theil index and agglomeration degree were then used to analyze the spatial distribution of medical and health resources allocation, and its influencing factors were studied by grey relational analysis. It was found that the population allocation of medical and health resources in Taiyuan was better than area allocation. Population has the greatest influence on the allocation of medical and health resources, followed by society and the economy. The more developed the regional economy, the more diversified the main influencing factors, and the more adjustment and control choices of medical and health resources allocation. Suggestions for optimal allocation were put forward in order to fully utilize the limited medical and health resources, effectively respond to the epidemic needs, promote the sustainable development of resources, protect the health of residents, and improve social benefits.

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“…For example, Li [ 108 ], Zheng [ 109 ], and Guo [ 110 ] found that economic development, urbanization wage, population ageing, financial health expenditure levels, and population size are key factors affecting the geographical distribution of medical resources in China. Song [ 111 ], Ding [ 112 ], and Guo [ 113 ] found that social, economic, and environmental factors have great influence on the geographical clustering and spatio-temporal evolution trends of medical resources by leveraging the Bayesian local spatiotemporal regression model and spatial econometric model. In this paper, per capita GDP, urbanization rate and residents’ medical services consumption are found to be statistically insignificant in their direct influence, but their interaction influence, when combined with other factors, cannot be ignored.…”

Section: Discussionmentioning

confidence: 99%

Geographical Pattern Evolution of Health Resources in China: Spatio-Temporal Dynamics and Spatial Mismatch

et al. 2022

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(1) Background: The rational allocation of limited medical resources is the premise of safeguarding the public health. Especially since the outbreak of COVID-19, the evolution dynamics and spatial mismatch of medical resources have been a focal and frontier issue in academic discussions. (2) Methods: Based on the competitive state model and spatial mismatch index, this paper uses GIS and Geodetector spatial analysis methods and three typical indicators of hospitals, doctors, and beds to conduct an empirical study on the evolutionary characteristics and degree of mismatch in the geographic pattern of health resources in China from 2010 to 2020 (the data are from official publications issued by the National Bureau of statistics in China), in two dimensions of resource supply (economic carrying capacity) and demand (potential demand or need of residents). (3) Results: The spatial pattern of health resources at the provincial level in China has been firmly established for a long time, and the children and elderly population, health care government investment, and service industry added value are the key factors influencing the geographical distribution of health resources. The interaction between the different influence factors is dominated by bifactor enhancement, and about 30–40% of the factor pairs are in a nonlinear enhancement relationship. Hospital, doctor, and bed evolution trends and the magnitude and speed of their changes vary widely in spatial differentiation, but all are characterized by a high level of geographic agglomeration, heterogeneity, and gradient. Dynamic matching is the mainstream of development, while the geographical distribution of negative and positive mismatch shows strong spatial agglomeration and weak spatial autocorrelation. The cold and hot spots with evolution trend and space mismatch are highly clustered, shaping a center-periphery or gradient-varying spatial structure. (4) Conclusions: Despite the variability in the results of the analyses by different dimensions and indicators, the mismatch of health resources in China should not be ignored. According to the mismatch types and change trend, and following the geographic differentiation and spatial agglomeration patterns, this paper constructs a policy design framework of “regionalized governance-classified management”, in line with the concept of spatial adaptation and spatial justice, in order to provide a decision making basis for the government to optimize the allocation of health resources and carry out health spatial planning.

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Addressing the maldistribution of health resources in Sichuan Province, China: A county-level analysis

Cited by 16 publications

References 36 publications

Regional disparities and influencing factors of high quality medical resources distribution in China

Regional disparities and influencing factors of high quality medical resources distribution in China

Research on the Equity and Influencing Factors of Medical and Health Resources Allocation in the Context of COVID-19: A Case of Taiyuan, China

Geographical Pattern Evolution of Health Resources in China: Spatio-Temporal Dynamics and Spatial Mismatch

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