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. 2020 Nov 27;19(1):211.
doi: 10.1186/s12939-020-01320-2.

Equity and efficiency of health care resource allocation in Jiangsu Province, China

Qian Li  1   2 , Jianjun Wei  3   4 , Fengchang Jiang  5 , Guixiang Zhou  5 , Rilei Jiang  6 , Meijuan Chen  7 , Xu Zhang  8 , Wanjin Hu  9   10
Affiliations

Equity and efficiency of health care resource allocation in Jiangsu Province, China

Qian Li et al. Int J Equity Health. .

Abstract

Background: Jiangsu was one of the first four pilot provinces to engage in comprehensive health care reform in China, which has been on-going for the past 5 years. This study aims to evaluate the equity, efficiency and productivity of health care resource allocation in Jiangsu Province using the most recent data, analyse the causes of deficiencies, and discuss measures to solve these problems.

Methods: Data were extracted from the Jiangsu Health/Family Planning Statistical Yearbook (2015-2019) and Jiangsu Statistical Yearbook (2015-2019). The Gini coefficient (G), Theil index (T) and health resource density index (HRDI) were chosen to study the fairness of health resource allocation in Jiangsu Province. Data envelopment analysis (DEA) and the Malmquist productivity index (MPI) were used to analyse the efficiency and productivity of this allocation.

Results: From 2014 to 2018, the total amount of health resources in Jiangsu Province increased. The G of primary resource allocation by population remained below 0.15, and that by geographical area was between 0.14 and 0.28; additionally, the G of health financial resources was below 0.26, and that by geographical area was above 0.39. T was consistent with the results for G and Lorenz curves. The HRDI shows that the allocated amounts of health care resources were the highest in southern Jiangsu, except for the number of health institutions. The average value of TE was above 0.93, and the DEA results were invalid for only two cities. From 2014 to 2018, the mean TFPC in Jiangsu was less than 1, and the values exceeded 1 for only five cities.

Conclusion: The equity of basic medical resources was better than that of financial resources, and the equity of geographical allocation was better than that of population allocation. The overall efficiency of health care resource allocation was high; however, the total factor productivity of the whole province has declined due to technological regression. Jiangsu Province needs to further optimize the allocation and increase the utilization efficiency of health care resources.

Keywords: Efficiency; Equity; Health care resource; Productivity.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The allocation of health resources in Jiangsu Province from 2014 to 2018. a-c show the number of different kinds of institutions, beds and health workers. d, f denote the government financial subsidies and total expenditures in Jiangsu Province. e, g denote the distribution of government financial subsidies and total expenditures in 13 cities of Jiangsu Province
Fig. 2
Fig. 2
The equity of primary health resource allocation in Jiangsu Province from 2014 to 2018. a shows the G of primary resources allocated by geographical area. b, c show the Lodz curves of primary resources allocated by geographical area in 2014 and 2018, respectively. d shows the G of primary resources allocated by population. e, f show the Lorenz curves of primary resources allocated by population in 2014 and 2018, respectively. g-i denote the HRDI of institutions, beds and health workers in different regions. j-l denote the HRDI of institutions, beds and health workers in 13 cities
Fig. 3
Fig. 3
The equity of financial resource allocation in Jiangsu Province from 2014 to 2018. a shows the G of financial resources allocated by geographical area. b, c show the Lorenz curves of financial resources allocated by geographical area in 2014 and 2018, respectively. d shows the G of financial resources allocated by population. e, f show the Lorenz curves of financial resources allocated by population in 2014 and 2018, respectively. g, i denote the HRDI of government financial subsidies and total expenditures in different regions. h, j denote the HRDI of government financial subsidies and total expenditures in 13 cities
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