Has the Efficiency of China’s Healthcare System Improved after Healthcare Reform? A Network Data Envelopment Analysis and Tobit Regression Approach
Abstract
:1. Introduction
2. Materials and Methods
2.1. Network DEA Methodology
2.2. Variables and Data
3. Results
3.1. Results of Network DEA
3.2. Results of Tobit Regression Analysis
4. Discussion
5. Conclusions and Policy Recommendations
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Variables | Definition | Measurement |
---|---|---|
Inputs | ||
Health technicians per 1000 Persons | Health technicians include doctors with authorization, registered nurses, pharmacists, laboratory physician, radiologists, and other medical professionals. | Density per 1000 population |
Beds per 1000 Persons | Beds are the total number of beds that are available in medical institution, excluding beds for observation, extra beds, etc. | Density per 1000 population |
Per capital total health expenditure | The ratio of the total amount of money raised from the whole society for health services to the population of a region in a certain period of time. | Yuan |
Outputs | ||
Maternal mortality rate | The maternal mortality rate is the number of maternal deaths per 100,000 women. | Per 100,000 |
Perinatal survival rate | Perinatal mortality is the number of perinatal deaths, which occurs in the second trimester, during yield and within 7 days of birth. | Per 1000 |
Life expectancy | Life expectancy at birth measures how long, on average, a newborn can be expected to live. | Years |
Intermediates | ||
Outpatients visit | Total number of outpatient visits during the Financial year. | Per 100,000 |
Inpatients visit | Total number of inpatient visits during the Financial year. | Per 100,000 |
Inpatient days | It is calculated by the total number of days discharged patients are in bed divided by the number discharged patients. | Days |
Independent variables | ||
Region | China is divided into the east region, middle region and western region, according to the geographical location and the level of economic development. | |
Per capital GDP | The level of per capital GDP is measured by dividing the gross domestic productof each province by the average population | Billion yuan |
Education | Education level is reflected by comparing the number of higher education students per 100,000 population in each province with the average of higher education students per 100,000 population. | Per 100,0000 population |
Government health expenditure | Government health expenditure are the funds used by governments at all levels for medical and health services, medical security subsidies, health and medical security administration, population and family planning affairs expenditures, and other undertakings. | 10,000 yuan |
Social health expenditure | Social health expenditure is outside government expenditure: all sectors of society invest in health. | Billion yuan |
Personal health expenditure | Personal health expenditure are the cash payment by residents when receiving various health services. | Billion yuan |
public hospital | Number of public hospitals are the total number of hospitals that are owned or controlled by a government unit or other public corporations. | |
private hospital | Number of private hospitals are total number of hospitals that are not owned or controlled by government or other public organizations. |
Provinces | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 |
---|---|---|---|---|---|---|---|---|
Beijing | 0.3500 | 0.3660 | 0.3982 | 0.6715 | 0.6988 | 0.7121 | 0.7186 | 0.7234 |
Tianjin | 0.5623 | 0.5458 | 0.5971 | 0.8854 | 0.8739 | 0.9016 | 0.8840 | 0.9415 |
Hebei province | 0.8305 | 0.7593 | 0.7922 | 0.8798 | 0.8582 | 0.8927 | 0.8772 | 0.8794 |
Shanxi province | 0.6959 | 0.7516 | 0.7675 | 0.7747 | 0.7768 | 0.7678 | 0.8078 | 0.8315 |
Inner Mongolia | 0.7353 | 0.6864 | 0.6750 | 0.7585 | 0.7182 | 0.7299 | 0.7306 | 0.7498 |
Liaoning province | 0.5713 | 0.5606 | 0.6214 | 0.7059 | 0.6804 | 0.7375 | 0.7928 | 0.7467 |
Jilin province | 0.6299 | 0.6259 | 0.6681 | 0.7836 | 0.7678 | 0.7912 | 0.7847 | 0.7802 |
Heilongjiang province | 0.6631 | 0.6232 | 0.6542 | 0.7679 | 0.7388 | 0.7762 | 0.7945 | 0.7940 |
Shanghai | 0.4201 | 0.3619 | 0.3891 | 0.7368 | 0.7247 | 0.7517 | 0.7249 | 0.7575 |
Jiangsu province | 0.7197 | 0.7121 | 0.7033 | 0.7959 | 0.7546 | 0.7561 | 0.7476 | 0.7459 |
Zhejiang province | 0.6891 | 0.6728 | 0.6823 | 0.7877 | 0.7915 | 0.7897 | 0.7634 | 0.7709 |
Anhui province | 0.9454 | 0.9258 | 0.9289 | 0.9271 | 0.9375 | 0.9378 | 0.9455 | 0.9642 |
Fujian province | 0.8401 | 0.7970 | 0.7831 | 0.8901 | 0.8263 | 0.8624 | 0.8648 | 0.9114 |
Jiangxi province | 1 | 0.9493 | 0.9514 | 0.9525 | 0.9459 | 0.9362 | 0.9478 | 0.9457 |
Shandong province | 0.7065 | 0.6909 | 0.6946 | 0.7233 | 0.7225 | 0.7841 | 0.7749 | 0.7879 |
Henan province | 0.8743 | 0.8332 | 0.8586 | 0.8369 | 0.8537 | 0.8640 | 0.8457 | 0.8466 |
Hubei province | 0.8131 | 0.7961 | 0.7562 | 0.7827 | 0.7398 | 0.7662 | 0.8487 | 0.7051 |
Hunan province | 0.8829 | 0.9296 | 0.8903 | 0.8723 | 0.8125 | 0.8551 | 0.7898 | 0.7922 |
Guangdong province | 0.7636 | 0.7310 | 0.7306 | 0.9298 | 0.9299 | 0.9410 | 0.9402 | 0.9306 |
Guangxi province | 1 | 0.9166 | 0.9884 | 0.9676 | 0.9344 | 0.9841 | 0.9872 | 0.9301 |
Hainan province | 0.8138 | 0.8933 | 0.8777 | 0.9825 | 0.9462 | 0.9582 | 0.9130 | 0.9365 |
Chongqing | 0.8627 | 0.8195 | 0.8220 | 0.8089 | 0.8257 | 0.8159 | 0.7976 | 0.7782 |
Sichuan province | 0.8274 | 0.7896 | 0.8145 | 0.8034 | 0.7922 | 0.8013 | 0.7732 | 0.7668 |
Guizhou province | 1 | 1 | 1 | 1 | 0.9847 | 0.9268 | 0.9103 | 0.8748 |
Yunnan province | 0.9280 | 0.8513 | 0.8818 | 0.9474 | 0.8773 | 0.9384 | 0.8840 | 0.8809 |
Shaanxi province | 0.6969 | 0.7670 | 0.6825 | 0.7152 | 0.6956 | 0.7055 | 0.6997 | 0.7016 |
Gansu province | 0.8811 | 0.8374 | 0.8315 | 0.8578 | 0.8355 | 0.8588 | 0.8519 | 0.8501 |
Qinghai province | 0.7128 | 0.7160 | 0.7590 | 0.8901 | 0.9391 | 0.7075 | 0.7190 | 0.7366 |
Ningxia | 0.6981 | 0.7090 | 0.7241 | 0.8402 | 0.7477 | 0.7453 | 0.7557 | 0.7600 |
Sinkiang | 0.6194 | 0.6066 | 0.5629 | 0.6719 | 0.6192 | 0.6360 | 0.6479 | 0.6538 |
Degree of efficiency = 1 | 3 | 1 | 1 | 1 | 0 | 0 | 0 | 0 |
Between mean and 1 | 12 | 15 | 15 | 13 | 14 | 13 | 13 | 13 |
<Mean | 15 | 14 | 14 | 16 | 16 | 17 | 17 | 17 |
mean | 0.7578 | 0.7408 | 0.7495 | 0.8316 | 0.8116 | 0.8210 | 0.8174 | 0.8158 |
Maximum | 1 | 1 | 1 | 1 | 0.9847 | 0.9841 | 0.9872 | 0.9642 |
Minimum | 0.3500 | 0.3619 | 0.3891 | 0.6715 | 0.6192 | 0.6360 | 0.6479 | 0.6538 |
standard deviation | 0.1596 | 0.1546 | 0.1488 | 0.0936 | 0.0959 | 0.0916 | 0.0850 | 0.0860 |
Provinces | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 |
---|---|---|---|---|---|---|---|---|
Beijing | 0.5690 | 0.5972 | 0.5856 | 0.9123 | 0.8532 | 0.8064 | 0.8190 | 0.8041 |
Tianjin | 0.6512 | 0.6333 | 0.6984 | 1 | 1 | 1 | 1 | 1 |
Hebei province | 0.8305 | 0.7593 | 0.8207 | 0.9348 | 0.9243 | 0.9401 | 0.9331 | 0.9236 |
Shanxi province | 0.8408 | 0.8710 | 0.9784 | 0.9742 | 0.9640 | 0.9538 | 1 | 1 |
Inner Mongolia | 0.7850 | 0.7328 | 0.7843 | 0.8764 | 0.8262 | 0.8218 | 0.8438 | 0.8459 |
Liaoning province | 0.7117 | 0.6969 | 0.7788 | 0.8918 | 0.8547 | 0.9099 | 0.9781 | 0.8977 |
Jilin province | 0.6463 | 0.6383 | 0.7039 | 0.8439 | 0.8351 | 0.8561 | 0.8599 | 0.8307 |
Heilongjiang province | 0.7654 | 0.7226 | 0.8116 | 0.9497 | 0.9018 | 0.9802 | 0.9703 | 0.9652 |
Shanghai | 0.5552 | 0.4871 | 0.4872 | 0.8900 | 0.8374 | 0.8634 | 0.8405 | 0.8029 |
Jiangsu province | 0.9872 | 0.9929 | 0.8563 | 0.9711 | 0.8784 | 0.9306 | 0.8540 | 0.8634 |
Zhejiang province | 0.9028 | 0.8992 | 0.8667 | 1 | 0.9411 | 0.9071 | 0.8888 | 0.8785 |
Anhui province | 0.9500 | 0.9303 | 0.9711 | 1 | 1 | 1 | 1 | 0.9995 |
Fujian province | 0.8759 | 0.8304 | 0.7831 | 0.8901 | 0.8263 | 0.8624 | 0.8648 | 0.9114 |
Jiangxi province | 1 | 0.9493 | 0.9707 | 1 | 1 | 0.9796 | 1 | 1 |
Shandong province | 0.7264 | 0.7447 | 0.7239 | 0.79457 | 0.8119 | 0.8636 | 0.8419 | 0.8292 |
Henan province | 1 | 0.9951 | 1 | 1 | 1 | 1 | 1 | 1 |
Hubei province | 0.9150 | 0.9211 | 0.8706 | 0.9150 | 0.8565 | 0.8603 | 1 | 0.8083 |
Hunan province | 0.9360 | 0.9837 | 0.9650 | 0.9346 | 0.8889 | 0.9293 | 0.8639 | 1 |
Guangdong province | 1 | 0.9333 | 0.7658 | 0.9993 | 0.9861 | 1 | 1 | 1 |
Guangxi province | 1 | 0.9166 | 1 | 0.9899 | 0.9523 | 1 | 0.9893 | 0.9301 |
Hainan province | 0.8138 | 0.8933 | 0.9076 | 1 | 1 | 1 | 0.9504 | 0.9365 |
Chongqing | 0.9588 | 0.9108 | 0.9255 | 0.9552 | 0.9311 | 0.8825 | 0.8499 | 0.8194 |
Sichuan province | 0.9780 | 0.9367 | 0.9085 | 0.9244 | 0.9243 | 0.9155 | 0.9219 | 0.9157 |
Guizhou province | 1 | 1 | 1 | 1 | 0.9847 | 0.9268 | 0.9103 | 0.8819 |
Yunnan province | 1 | 0.9291 | 0.8921 | 1 | 0.9356 | 1 | 0.9267 | 0.8809 |
Shaanxi province | 0.7775 | 0.8582 | 0.7913 | 0.8367 | 0.7883 | 0.7839 | 0.7600 | 0.7573 |
Gansu province | 0.9288 | 0.8701 | 0.8960 | 0.9547 | 0.9327 | 0.9639 | 0.9675 | 0.9120 |
Qinghai province | 0.7128 | 0.7160 | 0.7590 | 0.9199 | 0.9391 | 0.7907 | 0.8004 | 0.7921 |
Ningxia | 0.7406 | 0.7538 | 0.7986 | 0.9067 | 0.8420 | 0.8339 | 0.9190 | 0.7991 |
Sinkiang | 0.6194 | 0.6066 | 0.5629 | 0.6719 | 0.6192 | 0.6360 | 0.6479 | 0.6538 |
Degree of efficiency = 1 | 6 | 1 | 3 | 8 | 5 | 7 | 7 | 6 |
Between mean and 1 | 10 | 17 | 12 | 9 | 11 | 11 | 13 | 9 |
<Mean | 14 | 12 | 15 | 13 | 14 | 12 | 10 | 15 |
mean | 0.8393 | 0.8237 | 0.8288 | 0.9312 | 0.9011 | 0.9066 | 0.9067 | 0.8880 |
Maximum | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Minimum | 0.5552 | 0.4871 | 0.4872 | 0.6719 | 0.6192 | 0.6360 | 0.6479 | 0.6538 |
standard deviation | 0.1431 | 0.1394 | 0.1329 | 0.0749 | 0.0846 | 0.0861 | 0.0863 | 0.0880 |
Provinces | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 |
---|---|---|---|---|---|---|---|---|
Beijing | 0.6151 | 0.6129 | 0.6800 | 0.8190 | 0.7385 | 0.8830 | 0.8774 | 0.8997 |
Tianjin | 0.8634 | 0.8619 | 0.8549 | 0.8739 | 0.7881 | 0.9016 | 0.8840 | 0.9415 |
Hebei province | 1 | 1 | 0.9653 | 0.9285 | 0.8457 | 0.9495 | 0.9401 | 0.9521 |
Shanxi province | 0.8276 | 0.8629 | 0.7845 | 0.8058 | 0.7802 | 0.8051 | 0.8078 | 0.8315 |
Inner Mongolia | 0.9367 | 0.9367 | 0.8606 | 0.8694 | 0.8440 | 0.8882 | 0.8658 | 0.8864 |
Liaoning province | 0.8028 | 0.8045 | 0.7978 | 0.7961 | 0.7398 | 0.8105 | 0.8105 | 0.8318 |
Jilin province | 0.9746 | 0.9805 | 0.9492 | 0.9193 | 0.8487 | 0.9241 | 0.9126 | 0.9392 |
Heilongjiang province | 0.8664 | 0.8625 | 0.8060 | 0.8192 | 0.7771 | 0.7919 | 0.8188 | 0.8227 |
Shanghai | 0.7568 | 0.7429 | 0.7986 | 0.8654 | 0.7837 | 0.8706 | 0.8625 | 0.9435 |
Jiangsu province | 0.7290 | 0.7172 | 0.8213 | 0.8591 | 0.8315 | 0.8124 | 0.8753 | 0.8639 |
Zhejiang province | 0.7633 | 0.7482 | 0.7872 | 0.8410 | 0.8045 | 0.8705 | 0.8589 | 0.8775 |
Anhui province | 0.9951 | 0.9951 | 0.9565 | 0.9375 | 0.9241 | 0.9378 | 0.9455 | 0.9647 |
Fujian province | 0.9592 | 0.9598 | 1 | 1 | 0.9720 | 1 | 1 | 1 |
Jiangxi province | 1 | 1 | 0.9802 | 0.9459 | 0.9285 | 0.9557 | 0.9478 | 0.9457 |
Shandong province | 0.9727 | 0.9278 | 0.9595 | 0.8898 | 0.8534 | 0.9080 | 0.9204 | 0.9502 |
Henan province | 0.8743 | 0.8374 | 0.8586 | 0.8537 | 0.8509 | 0.8640 | 0.8457 | 0.8466 |
Hubei province | 0.8886 | 0.8642 | 0.8686 | 0.8637 | 0.8501 | 0.8906 | 0.8487 | 0.8723 |
Hunan province | 0.9434 | 0.9450 | 0.9226 | 0.9141 | 0.8943 | 0.9201 | 0.9142 | 0.7922 |
Guangdong province | 0.7636 | 0.7832 | 0.9540 | 0.9430 | 0.9148 | 0.9410 | 0.9402 | 0.9306 |
Guangxi province | 1 | 1 | 0.9884 | 0.9812 | 0.9198 | 0.9841 | 0.9979 | 1 |
Hainan province | 1 | 1 | 0.9670 | 0.9462 | 0.9045 | 0.9582 | 0.9607 | 1 |
Chongqing | 0.8997 | 0.8997 | 0.8882 | 0.8868 | 0.8589 | 0.9245 | 0.9385 | 0.9497 |
Sichuan province | 0.8460 | 0.8430 | 0.8966 | 0.8570 | 0.8252 | 0.8752 | 0.8387 | 0.8374 |
Guizhou province | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0.9919 |
Yunnan province | 0.9280 | 0.9163 | 0.9884 | 0.9376 | 0.9384 | 0.9384 | 0.9538 | 1 |
Shaanxi province | 0.8964 | 0.8938 | 0.8625 | 0.8825 | 0.8699 | 0.9000 | 0.9206 | 0.9265 |
Gansu province | 0.9487 | 0.9624 | 0.9280 | 0.8958 | 0.8763 | 0.8909 | 0.8805 | 0.9321 |
Qinghai province | 1 | 1 | 1 | 1 | 1 | 0.8948 | 0.8984 | 0.9298 |
Ningxia | 0.9426 | 0.9406 | 0.9067 | 0.8880 | 0.8476 | 0.8938 | 0.8223 | 0.9511 |
Sinkiang | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Degree of efficiency = 1 | 7 | 7 | 4 | 4 | 3 | 3 | 3 | 5 |
Between mean and 1 | 9 | 10 | 11 | 9 | 10 | 16 | 10 | 14 |
<Mean | 14 | 13 | 15 | 17 | 17 | 11 | 17 | 11 |
mean | 0.8998 | 0.8966 | 0.9010 | 0.9007 | 0.8670 | 0.9062 | 0.9029 | 0.9204 |
Maximum | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Minimum | 0.6151 | 0.6129 | 0.6800 | 0.7961 | 0.7385 | 0.7919 | 0.8078 | 0.7922 |
standard deviation | 0.1001 | 0.1013 | 0.0835 | 0.0597 | 0.0730 | 0.0563 | 0.0589 | 0.0610 |
Parameter Regions | Coef. | Std. Error | p | 95% [Conf. Interval] | |
---|---|---|---|---|---|
Regions | 0.055156 | 0.010163 | 0.000 | 0.035237 | 0.075074 |
Per capital GDP | −0.006501 | 0.005211 | 0.212 | −0.016714 | 0.003711 |
Number of high education enrollment | −0.000070 | 0.000009 | 0.000 | −0.000087 | -0.000053 |
Governmen health expenditure | 0.000821 | 0.000101 | 0.000 | 0.000624 | 0.001019 |
Social health expenditure | −0.000233 | 0.000050 | 0.000 | −0.000330 | -0.000136 |
Personal health expenditure | −0.000171 | 0.000087 | 0.050 | −0.000341 | 0.000000 |
Number of public hospitals | −0.000212 | 0.000046 | 0.000 | −0.000301 | -0.000123 |
Number of private hospitals | −0.000063 | 0.000033 | 0.057 | −0.000127 | 0.000002 |
cons | 0.904079 | 0.029746 | 0.000 | 0.845779 | 0.962379 |
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Share and Cite
Gong, G.; Chen, Y.; Gao, H.; Su, D.; Chang, J. Has the Efficiency of China’s Healthcare System Improved after Healthcare Reform? A Network Data Envelopment Analysis and Tobit Regression Approach. Int. J. Environ. Res. Public Health 2019, 16, 4847. https://doi.org/10.3390/ijerph16234847
Gong G, Chen Y, Gao H, Su D, Chang J. Has the Efficiency of China’s Healthcare System Improved after Healthcare Reform? A Network Data Envelopment Analysis and Tobit Regression Approach. International Journal of Environmental Research and Public Health. 2019; 16(23):4847. https://doi.org/10.3390/ijerph16234847
Chicago/Turabian StyleGong, Guangwen, Yingchun Chen, Hongxia Gao, Dai Su, and Jingjing Chang. 2019. "Has the Efficiency of China’s Healthcare System Improved after Healthcare Reform? A Network Data Envelopment Analysis and Tobit Regression Approach" International Journal of Environmental Research and Public Health 16, no. 23: 4847. https://doi.org/10.3390/ijerph16234847
APA StyleGong, G., Chen, Y., Gao, H., Su, D., & Chang, J. (2019). Has the Efficiency of China’s Healthcare System Improved after Healthcare Reform? A Network Data Envelopment Analysis and Tobit Regression Approach. International Journal of Environmental Research and Public Health, 16(23), 4847. https://doi.org/10.3390/ijerph16234847