Estimating the spatial correlation and convergence of China's healthcare resources allocation: evidence from the Yangtze River Delta Region

Shen, Yuqing; Sun, Zesheng

doi:10.1186/s13690-022-00958-4

Cited by 16 publications

(14 citation statements)

References 20 publications

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“…Most research has focused mainly on the spatial layout and accessibility of EMS facilities for optimal allocation (18)(19)(20)(21)(22)(23). Liu et al found that the equity of supply and demand for EMS resources is low based on data from death surveillance from 2008 to 2012 in Chongqing, China (24).…”

Section: Introduction Backgroundmentioning

confidence: 99%

Urban–sub-urban–rural variation in the supply and demand of emergency medical services

Geng

et al. 2023

Front. Public Health

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BackgroundEmergency medical services (EMSs) are a critical component of health systems, often serving as the first point of contact for patients. Understanding EMS supply and demand is necessary to meet growing demand and improve service quality. Nevertheless, it remains unclear whether the EMS supply matches the demand after the 2016 healthcare reform in China. Our objective was to comprehensively investigate EMS supply–demand matching, particularly among urban vs. sub-urban vs. rural areas.MethodsData were extracted from the Tianjin Medical Priority Dispatch System (2017–2021). From supply and demand perspectives, EMS resources and patient characteristics were analyzed. First, we performed a descriptive analysis of characteristics, used Moran's I to explore the spatial layout, and used the Gini coefficient to evaluate the equity of EMS supply and demand. Second, we analyzed urban–sub-urban–rural variation in the characteristics of EMS supply and demand by using the chi-square test. Finally, we examined the association between the EMS health resource density index and the number of patients by using the Spearman correlation and divided supply–demand matching types into four types.ResultsIn 2021, the numbers of medical emergency stations and ambulances were 1.602 and 3.270 per 100,000 population in Tianjin, respectively. There were gradients in the health resource density index of the number of emergency stations (0.260 vs. 0.059 vs. 0.036; P = 0.000) in urban, sub-urban, and rural areas. There was no spatial autocorrelation among medical emergency stations, of which the G values by population, geographical distribution, and the health resource density index were 0.132, 0.649, and 0.473, respectively. EMS demand was the highest in urban areas, followed by sub-urban and rural areas (24.671 vs. 15.081 vs. 3.210 per 1,000 population and per year; P = 0.000). The EMS supply met the demand in most districts (r = 0.701, P = 0.003). The high supply–high demand types with stationary demand trends were distributed in urban areas; the low supply–high demand types with significant demand growth trends were distributed in sub-urban areas; and the low supply–low demand types with the highest speed of demand growth were distributed in rural areas.ConclusionEMS supply quantity and quality were promoted, and the supply met the demand after the 2016 healthcare reform in Tianjin. There was urban–sub-urban–rural variation in EMS supply and demand patterns.

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Section: Introduction Backgroundmentioning

confidence: 99%

Urban–sub-urban–rural variation in the supply and demand of emergency medical services

Geng

et al. 2023

Front. Public Health

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“…This is in line with previous findings that China's healthcare reform has placed more emphasis on physical capital investment at the expense of human capital investment. 51 Since popular belief holds that significant differences exist between various county subsamples, we estimated the allocative efficiency of municipal districts and nonmunicipal counties separately and compared the means with the ANOVA method. Our findings show that nonmunicipal counties have a higher mean for different efficiency indicators than municipal districts.…”

Section: Discussionmentioning

confidence: 99%

“…This is in line with previous findings that China’s healthcare reform has placed more emphasis on physical capital investment at the expense of human capital investment. 51…”

Section: Discussionmentioning

confidence: 99%

Estimating the Distribution and Convergence of County-level Healthcare Resources Allocative Efficiency in China Based on DEA and Spatial Panel Model

Sun

Yang

et al. 2023

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Although China’s 2009 New Healthcare Reform aimed to correct the imbalance in the spatial allocation of healthcare resources with a focus on the county level, its impact on county-level allocative efficiency evolution and convergence remains unclear. This paper for the first time performs a spatial analysis to explore the distribution, evolution, and convergence of the allocative efficiency of healthcare resources with county-level data. This paper uses the sample data of 158 countries in Henan Province, China, to evaluate the evolution and convergence of the allocative efficiency of healthcare resources. Based on the estimated Data Envelopment Analysis (DEA) allocative efficiency, analysis of variance (ANOVA), and spatial descriptive analysis, we explore the county heterogeneity and efficiency evolution; a spatial panel model is then utilized to test the county-level convergence of the allocative efficiency of healthcare resources. Although the number of efficient counties has not increased, the number of inefficient individuals keeps decreasing, and the allocative efficiency of municipal districts is lower than that of nonmunicipal counties. There exists a positive spatial correlation of allocative efficiency in Henan Province, and significant and robust convergence results can be found at the county level after China’s 2009 reform. This study reveals a diversified picture of China’s county-level spatial evolution of allocative efficiency in healthcare resources, showing a more balanced spatial distribution of allocative efficiency since the triggering of China’s 2009 reform. However, long-term investment incentives and a targeted allocation of healthcare resources are still needed to promote further efficiency convergence and increase the number of counties with efficiency.

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“…By using the supply/demand index, we could reduce the dimensions of supply/demand indicators and better measure changes in supply and demand as shown in the existing literature. 39 The entropy weight method can determine the weight coefficients of different indicators based on the amount of information provided by observations, and it enables us to reduce the information lost in the calculation process. After standardizing the supply (input) and demand (output) indicators for each region i with equation (1) , we can calculate the supply index and demand index for each region i using the entropy weight method as follows:…”

Section: Methodsmentioning

confidence: 99%

The Effect of Descending Resources Reform on Reallocating Healthcare Resources: An Analysis Based on the Difference-in-Differences Method

Yang

Sun

Wang

et al. 2023

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Since 2010, China has been exploring descending resources reform in order to correct the imbalanced allocation of healthcare resources and promote coordinated economic development among regions. This paper for the first time estimates the impact this reform has had on the reallocation of healthcare resources by using prefecture-level cities panel data from Zhejiang Province, China, which implemented the reform province-wide in 2013. The time-varying difference-in-differences (DID) method was used to estimate the reform’s policy effects. The data used in this paper is from published statistical yearbooks and local governments, which include panel data from 11 prefecture-level and higher cities in Zhejiang Province as the treated group and 46 prefecture-level cities in Jiangsu, Henan, and Sichuan Province as the control group. The entropy weight method was used to construct the supply index and demand index to incorporate multiple inputs and outputs, and efficiency indicators were constructed using the ratio method. This research found that the reform has had a positive effect on outpatient visits in different prefecture-level cities with vast rural areas. However, this reform exerted no significant impact on inpatient services or supply-side or resource allocation efficiency. Several robust tests support the above conclusions, and one theoretical explanation is provided. The descending health resources reform can be a valuable reform path in promoting more balanced healthcare resource allocation; however, the resultant disparities in its effects should be considered when implementing it.

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Estimating the spatial correlation and convergence of China's healthcare resources allocation: evidence from the Yangtze River Delta Region

Cited by 16 publications

References 20 publications

Urban–sub-urban–rural variation in the supply and demand of emergency medical services

Urban–sub-urban–rural variation in the supply and demand of emergency medical services

Estimating the Distribution and Convergence of County-level Healthcare Resources Allocative Efficiency in China Based on DEA and Spatial Panel Model

The Effect of Descending Resources Reform on Reallocating Healthcare Resources: An Analysis Based on the Difference-in-Differences Method

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