Research on Urban Medical and Health Services Efficiency and Its Spatial Correlation in China: Based on Panel Data of 13 Cities in Jiangsu Province
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Areas
2.2. Methods
2.2.1. Data Envelopment Analysis
- (1)
- DEA-SE-SBM Model
- (2)
- DEA-Malmquist Model
2.2.2. Exploratory Spatial Data Analysis
- (1)
- Global Spatial Autocorrelation
- (2)
- Local Spatial Autocorrelation
2.3. Selection of Indicators and Data Sources
2.4. Medical and Health Input-Output in Jiangsu Province
3. Results of the Comprehensive Evaluation of the Efficiency of Medical and Health Services
3.1. Results of the DEA-SE-SBM Model
3.2. Results of the DEA-Malmquist Model
3.3. Results of the Comprehensive Evaluation
4. Results of Spatial Correlation Analysis of the Efficiency of Medical and Health Services
4.1. Results of Global Autocorrelation Analysis
4.2. Results of Local Autocorrelation Analysis
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
DEA | Data Envelopment Analysis; |
SFA | Stochastic Frontier Analysis; |
BCC | Banker, Charnes, and Cooper model; |
CCR | Charnes, Cooper, and Rhodes model; |
SBM | slack-based measure model; |
SE-SBM | super efficiency slack-based measure model; |
ESDA | Exploratory Spatial Data Analysis; |
DMUs | Decision-making units; |
TFPCH | Total Factor Productivity Change index; |
EFFCH | Technical Efficiency Change index; |
TECHCH | Technical Change index; |
VRS | variable returns to scale; |
PECH | Pure Efficiency Change index; |
SECH | Scale Efficiency Change index; |
LISA | Local Indicator of Spatial Association; |
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Region | Prefecture-Level City |
---|---|
southern Jiangsu | Zhenjiang, Nanjing, Changzhou, Suzhou, Wuxi |
central Jiangsu | Yangzhou, Taizhou, Nantong |
northern Jiangsu | Lianyungang, Suqian, Huaian, Yancheng, Xuzhou |
Items | Specific Indicators | Explanation of Indicators | Significance of Indicators | References |
---|---|---|---|---|
Input | X1: Number of health technicians (person) | Health technicians mainly include registered nurses, licensed (assistant) physicians, pharmacists, health supervisors, technicians, and other health technicians. | Reflecting the investment scale of medical and health human resources in each year in the study area. | [5,9,21,48] |
X2: Number of Health Institutions (unit) | Health Institutions mainly include hospitals, professional public health institutions, primary medical and health institutions, and other institutions. | Reflecting the input of medical and health material resources in each year in the study area. | [20,24,29,30,47] | |
X3: Number of Beds (unit) | The number of beds is the sum of the number of beds in various medical and health institutions each year. | [9,20,27,28,46,47] | ||
Output | Y1: Number of Outpatients and Emergency Visits (10,000 person) | Outpatient and emergency visits are the sum of outpatient visits and emergency visits. | Reflecting the supply of medical and health services of the research subjects in that year. | [9,23,28,48] |
Y2: Utilization Rate of Beds (%) | The utilization rate of beds indicates the utilization of beds in all medical and health institutions in each region. | [5,24,27] |
Region | Cities | 2015 | 2019 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
X1 | X2 | X3 | Y1 | Y2 | X1 | X2 | X3 | Y1 | Y2 | ||
southern Jiangsu | Nanjing | 65,139 | 2337 | 46,643 | 7010.00 | 89.84 | 93,856 | 3242 | 59,046 | 9070.70 | 91.17 |
Wuxi | 44,707 | 2243 | 37,366 | 4856.00 | 86.82 | 59,303 | 2770 | 50,478 | 5786.16 | 79.42 | |
Changzhou | 29,616 | 1196 | 24,263 | 2804.00 | 98.06 | 37,086 | 1458 | 28,322 | 3223.91 | 90.53 | |
Suzhou | 68,179 | 3121 | 59,304 | 8968.00 | 88.34 | 91,047 | 3720 | 71,657 | 10,085.47 | 86.52 | |
Zhenjiang | 18,985 | 943 | 14,637 | 2326.00 | 77.90 | 21,691 | 1013 | 15,844 | 2484.23 | 84.99 | |
central Jiangsu | Nantong | 41,067 | 3147 | 36,031 | 3908.00 | 93.53 | 50,329 | 3357 | 46,375 | 4253.57 | 87.99 |
Yangzhou | 24,326 | 1780 | 20,121 | 2592.00 | 90.62 | 29,406 | 1890 | 24,994 | 2588.01 | 92.00 | |
Taizhou | 24,215 | 1953 | 21,838 | 2405.00 | 87.32 | 31,673 | 2118 | 29,885 | 2508.03 | 86.45 | |
northern Jiangsu | Xuzhou | 51,567 | 4601 | 47,949 | 5880.00 | 99.74 | 70,767 | 4594 | 60,988 | 6245.99 | 87.19 |
Lianyungang | 23,056 | 2708 | 19,035 | 2729.00 | 75.90 | 31,117 | 2740 | 28,101 | 2859.41 | 75.85 | |
Huaian | 30,475 | 2228 | 25,966 | 2776.00 | 91.01 | 35,963 | 2200 | 30,376 | 2731.71 | 88.64 | |
Yancheng | 39,494 | 3242 | 37,169 | 3986.00 | 80.67 | 44,358 | 3270 | 40,301 | 4767.89 | 81.99 | |
Suqian | 26,179 | 2426 | 23,290 | 2929.00 | 77.74 | 36,749 | 2424 | 29,548 | 3344.65 | 78.21 |
Cities | 2015 | 2016 | 2017 | 2018 | 2019 | Mean |
---|---|---|---|---|---|---|
Nanjing | 1.028 | 1.070 | 1.089 | 1.063 | 1.063 | 1.063 |
Wuxi | 0.859 | 0.837 | 0.820 | 0.799 | 0.786 | 0.820 |
Suzhou | 1.123 | 1.084 | 1.079 | 1.064 | 1.064 | 1.083 |
Changzhou | 1.096 | 1.080 | 1.050 | 1.041 | 1.018 | 1.057 |
Zhenjiang | 1.495 | 1.547 | 1.521 | 1.555 | 1.600 | 1.544 |
Taizhou | 0.780 | 0.740 | 0.717 | 0.664 | 0.632 | 0.706 |
Nantong | 0.724 | 0.685 | 0.650 | 0.647 | 0.663 | 0.674 |
Yangzhou | 1.016 | 0.838 | 1.000 | 1.022 | 1.024 | 0.980 |
Lianyungang | 0.741 | 0.665 | 0.667 | 0.607 | 0.593 | 0.655 |
Xuzhou | 1.034 | 1.024 | 1.012 | 0.714 | 0.678 | 0.893 |
Huaian | 0.745 | 0.726 | 0.739 | 0.719 | 0.718 | 0.729 |
Yancheng | 0.649 | 0.661 | 0.675 | 0.746 | 0.759 | 0.698 |
Suqian | 0.718 | 0.690 | 0.687 | 0.698 | 0.670 | 0.692 |
Mean | 0.924 | 0.896 | 0.900 | 0.872 | 0.867 | 0.892 |
Year | EFFCH | TECHCH | PECH | SECH | TFPCH |
---|---|---|---|---|---|
2015—2016 | 1.009 | 0.849 | 1.029 | 0.981 | 0.857 |
2016—2017 | 1.004 | 1.004 | 0.996 | 1.009 | 1.009 |
2017—2018 | 0.995 | 0.894 | 0.984 | 1.011 | 0.890 |
2018—2019 | 0.941 | 1.082 | 0.963 | 0.977 | 1.017 |
Mean | 0.987 | 0.953 | 0.993 | 0.994 | 0.940 |
Cities | EFFCH | TECHCH | PECH | SECH | TFPCH |
---|---|---|---|---|---|
Wuxi | 1.032 | 1.067 | 1.027 | 1.005 | 1.101 |
Suzhou | 1.000 | 1.070 | 1.000 | 1.000 | 1.070 |
Zhenjiang | 1.000 | 1.001 | 1.000 | 1.000 | 1.001 |
Yancheng | 1.000 | 0.997 | 1.000 | 1.000 | 0.997 |
Nanjing | 0.969 | 1.018 | 0.974 | 0.995 | 0.986 |
Yangzhou | 0.991 | 0.959 | 0.991 | 1.000 | 0.950 |
Huaian | 1.004 | 0.932 | 1.010 | 0.993 | 0.935 |
Taizhou | 0.986 | 0.939 | 0.960 | 1.028 | 0.926 |
Xuzhou | 0.973 | 0.944 | 0.968 | 1.005 | 0.918 |
Suqian | 0.953 | 0.934 | 1.000 | 0.953 | 0.890 |
Changzhou | 0.993 | 0.871 | 1.039 | 0.955 | 0.864 |
Lianyungang | 1.000 | 0.853 | 1.000 | 1.000 | 0.853 |
Nantong | 0.934 | 0.837 | 0.940 | 0.994 | 0.782 |
Mean | 0.987 | 0.953 | 0.993 | 0.994 | 0.940 |
Year | 2015 | 2016 | 2017 | 2018 | 2019 |
---|---|---|---|---|---|
Moran’s I | 0.200 | 0.195 | 0.220 | 0.288 | 0.272 |
Z | 1.8455 | 1.8902 | 1.9777 | 2.4557 | 2.3843 |
P | 0.051 | 0.046 | 0.039 | 0.017 | 0.021 |
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Lin, L.; Wu, F.; Chen, W.; Zhu, C.; Huang, T. Research on Urban Medical and Health Services Efficiency and Its Spatial Correlation in China: Based on Panel Data of 13 Cities in Jiangsu Province. Healthcare 2021, 9, 1167. https://doi.org/10.3390/healthcare9091167
Lin L, Wu F, Chen W, Zhu C, Huang T. Research on Urban Medical and Health Services Efficiency and Its Spatial Correlation in China: Based on Panel Data of 13 Cities in Jiangsu Province. Healthcare. 2021; 9(9):1167. https://doi.org/10.3390/healthcare9091167
Chicago/Turabian StyleLin, Lingling, Fang Wu, Wei Chen, Chenming Zhu, and Tao Huang. 2021. "Research on Urban Medical and Health Services Efficiency and Its Spatial Correlation in China: Based on Panel Data of 13 Cities in Jiangsu Province" Healthcare 9, no. 9: 1167. https://doi.org/10.3390/healthcare9091167