Abstract

This study aimed to measure the efficiency and change in efficiency over time of township hospitals among Chinese provinces, to decompose the difference in efficiency between districts, and to study the correlations between the difference in efficiency and its determinants. Based on Chinese provincial panel data, the empirical analysis was established using data envelopment analysis (DEA), Malmquist index, Theil index decomposition method and Grey correlation analysis method. First, it was found that the township hospitals in most provinces were operating in an inefficient state, and the township hospitals in most provinces achieved gains in efficiency. Second, from 2003 to 2016 the shrinkage of the difference in provincial efficiency of township hospitals progressed slowly. Intra-regional difference is the main cause of the overall provincial efficiency difference of Chinese township hospitals, while inter-regional difference is the minor cause of the overall difference. Third, the correlation between the difference of overall provincial efficiency and the difference of economic development level is the highest among all the correlations, while other determinants rank second to seventh place in their degree of correlation with respect to the overall difference in provincial efficiency. Furthermore, the correlations between the intra-regional difference of provincial efficiency of Chinese township hospitals and its determinants vary tremendously across regions. Based on our findings, we can conclude, first, that efforts should be made to improve the overall provincial difference in efficiency of Chinese township hospitals, and enhance the utilization level of input resources, and to reduce resource waste. Second, in order to shrink the overall provincial efficiency of Chinese township hospitals, the most important measure that should be taken is to improve the economic development level in relatively backward provinces in order to lay a solid economic foundation for the improvement of efficiency and shrink the differences in efficiency between provinces. Third, more attention should be paid to the shrinkage of intra-regional efficiency differences in Chinese township hospitals, while the narrowing of inter-regional efficiency difference should not be ignored. For each region, it is necessary to recognize the difference in the relative importance of determinants, and to make development strategies according to local conditions so as to make full use of local characteristics and advantages.

Highlights

  • The aging of the population has led to an ever-increasing demand for medical and health care, which has made it increasingly contradictory to limited public health expenditure

  • Because the decomposition results of the Theil index reveal the structural causes of the provincial efficiency difference of Chinese township hospitals, i.e., the intra-regional difference is the major cause of the overall difference, this paper further examines the Grey correlations between intra-regional difference of provincial efficiency difference of Chinese township hospitals and determinants within each of the eastern, central and western regions of China

  • Based on the measurement and decomposition of overall provincial efficiency difference of Chinese township hospitals, using the Grey correlation analysis method, this paper studies the relationship between overall provincial efficiency difference of Chinese township hospitals and the determinants of difference, and further measures and investigates the intra-regional provincial efficiency difference of Chinese township hospitals, and the determinants within the eastern, central and western regions of China

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Summary

Introduction

The aging of the population has led to an ever-increasing demand for medical and health care, which has made it increasingly contradictory to limited public health expenditure. As a result, improving the efficiency of the health care system has gradually become a core goal of the development of the health care system [1]. It is generally believed that medical and health care resources are. Res. Public Health 2019, 16, 1601; doi:10.3390/ijerph16091601 www.mdpi.com/journal/ijerph

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