Concentration of Healthcare Resources in China: The Spatial–Temporal Evolution and Its Spatial Drivers

Abstract

This paper estimated and evaluated the spatial–temporal evolution of the concentration of healthcare resources (HCRs), in 31 provinces in China between 2004 and 2017, by using the entropy method. The spatial Durbin model (SDM) was used to further analyze the mechanisms behind the spatial driving forces at the national and regional levels. The findings revealed that: (i) The concentration of HCRs differed significantly among eastern, central, and western regions. The eastern, followed by the central region, had the highest concentration. Going east to west, the concentration of HCRs in the first echelon decreased, while it increased in the second and third echelons; (ii) places with higher concentrations clustered, while those with lower concentrations agglomerated; and (iii) economic development, population size, and urbanization promoted concentration. Education facilitated HCR concentration in the eastern and central regions, income stimulated HCR concentration in the eastern and western regions, and fiscal expenditure on healthcare promoted HCR concentration in the eastern region. Economic development inhibited HCR concentration in neighboring regions, population size restrained HCR concentration in neighboring areas in the western region, urbanization and income curbed HCR concentration in neighboring areas in the eastern and western regions, and fiscal expenditure on healthcare hindered HCR concentration in neighboring areas in the eastern region. Policy recommendations were proposed toward optimizing allocation of healthcare resources, increasing support for healthcare and education, and accelerating urbanization.

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

“…We used the entropy method and drew on the indicators determined by researchers [ 17 ] to measure health resource agglomeration capacities ( Table 1 ). The entropy method mainly determines the objective weight according to the index variability.…”

“…Learning from the research of scholars (Guo, et al, 2019) [17], we selected six factors influencing the spatial correlation network of health resource agglomeration capacities as follows. (1) Level of economic development: the higher the level of economic development, the stronger the residents' demand for health resources.…”

Successful Implementation and Development of a Phase II Cardiac Rehabilitation Program: A China-Wide Cross-Sectional Study Tracking In-service Training Clinical Staff

“…These results indicate that each hospital can achieve the basic requirements of CR and set up the CR program according to their own situation. There is a huge gap in medical insurance policies across China ( 35 ), and preceding studies have found that a lack of medical insurance is an obstacle to CR ( 16 , 17 ). However, this study shows that both the medical insurance coverage and the reimbursement rate do not affect the quality of CR.…”

Is Urbanization Good for the Health of Middle-Aged and Elderly People in China?—Based on CHARLS Data

“…However, medical resources are not only a reflection of these singular indexes, but are also closely related to financial resources, material resources, and manpower for healthcare. Therefore, some researchers have employed the entropy method [34] to comprehensively evaluate healthcare resources, but these methods are mostly applied at the provincial scale in China; the data are not easily available at the municipal level. Thus, taking into account the availability of data and the applicability of the method, this paper employs the fully arrayed polygonal graphical indication method [29] to comprehensively evaluate the level of medical and health resources by selecting three indicators: the number of hospital beds per thousand people, the number of medical and health institutions per thousand people, and the number of hospitals per hundred square kilometers.…”