Introduction
Literature review
Authors | Inputs | Outputs |
---|---|---|
Hamidi [37] | Number of beds, doctors, nurses, and non-medical staff | Number of treated inpatients and outpatients |
Lee, Chun et al. [38] | Number of beds, doctors, and nurses | Number of inpatient and outpatient visits |
Afonso and St. Aubyn [39] | Doctors, nurses, acute care beds, and MRI | Life expectancy, infant mortality, and potential years of life lost |
Chen, Wu et al. [40] | Number of doctors, nurses, and beds | Number of outpatient visits and inpatient cases |
Asandului, Roman et al. [41] | Number of doctors, hospital beds, and public health expenditures as percentage of gross domestic product (GDP) | Life expectancy at birth, health adjusted life expectancy, and infant mortality rate |
Yang [42] | Population into three groups by year | Doctors, hospital beds, and medical expenditures |
Ng [43] | Number of doctors, nurses, pharmacists, other staff, and beds | Number of outpatient and inpatient cases |
Kontodimopoulos, Nanos et al. [44] | Number of doctors, nurses, and beds | Outpatient visits, admissions, and preventive medical services |
Materials and methods
Data sources and description
Input and output variables
Independent variables
Input: medical service (I) | Output: residents’ health status (O) | Independent variables (x) |
---|---|---|
Outpatient visits (I1) | Infant mortality (O1) | Licensed (assistant) doctors (x1) |
Inpatient visits (I2) | under-five mortality (O2) | Registered nurses (x2) |
Number of surgeries (I3) | Maternal mortality (O3) | Pharmacists (x3) |
Life expectancy (O4) | Technicians (x4) | |
Trainees (x5) |
Correlation analysis of input and output variables
Input-output correlation | Infant mortality | Under-five mortality | Maternal mortality | Life expectancy | |
---|---|---|---|---|---|
Outpatient visits | Pearson correlation | .944a | .954a | .952a | .985a |
significance | 0.000 | 0.000 | 0.000 | 0.000 | |
Inpatient visits | Pearson correlation | .982a | .983a | .993a | .989a |
significance | 0.000 | 0.000 | 0.000 | 0.000 | |
Number of surgeries | Pearson correlation | .956a | .954a | .914a | .916a |
significance | 0.000 | 0.000 | 0.000 | 0.000 |
DEA-Malmquist model
Tobit regression
Theil index
Results
Static analysis of medical services efficiency at the provincial level
Province | 2007 | 2019 | ||||||
---|---|---|---|---|---|---|---|---|
crste | vrste | scale | rts | crste | vrste | scale | rts | |
Beijing | 0.394 | 0.985 | 0.400 | drs | 1.000 | 1.000 | 1.000 | – |
Tianjin | 1.000 | 1.000 | 1.000 | – | 1.000 | 1.000 | 1.000 | – |
Hebei | 0.199 | 0.940 | 0.211 | drs | 0.155 | 0.935 | 0.165 | drs |
Shanxi | 0.275 | 0.956 | 0.288 | drs | 0.381 | 0.950 | 0.401 | drs |
Inner Mongolia | 0.367 | 0.939 | 0.391 | drs | 0.454 | 0.954 | 0.476 | drs |
Liaoning | 0.285 | 0.954 | 0.299 | drs | 0.224 | 0.959 | 0.234 | drs |
Jilin | 0.340 | 0.974 | 0.350 | drs | 0.789 | 1.000 | 0.789 | drs |
Heilongjiang | 0.318 | 0.958 | 0.332 | drs | 0.328 | 0.968 | 0.339 | drs |
Shanghai | 0.604 | 1.000 | 0.604 | drs | 0.853 | 1.000 | 0.853 | drs |
Jiangsu | 0.185 | 0.946 | 0.196 | drs | 0.133 | 0.955 | 0.140 | drs |
Zhejiang | 0.256 | 0.973 | 0.263 | drs | 0.245 | 0.968 | 0.253 | drs |
Anhui | 0.261 | 0.946 | 0.276 | drs | 0.183 | 0.936 | 0.196 | drs |
Fujian | 0.230 | 0.953 | 0.242 | drs | 0.279 | 0.945 | 0.295 | drs |
Jiangxi | 0.316 | 0.914 | 0.345 | drs | 0.325 | 0.971 | 0.334 | drs |
Shandong | 0.164 | 0.946 | 0.174 | drs | 0.100 | 0.953 | 0.105 | drs |
Henan | 0.117 | 0.916 | 0.127 | drs | 0.094 | 0.929 | 0.102 | drs |
Hubei | 0.216 | 0.922 | 0.234 | drs | 0.210 | 0.933 | 0.225 | drs |
Hunan | 0.179 | 0.928 | 0.193 | drs | 0.207 | 0.931 | 0.223 | drs |
Guangdong | 0.082 | 0.938 | 0.088 | drs | 0.084 | 0.953 | 0.088 | drs |
Guangxi | 0.194 | 0.932 | 0.208 | drs | 0.174 | 0.936 | 0.186 | drs |
Hainan | 1.000 | 1.000 | 1.000 | – | 1.000 | 1.000 | 1.000 | – |
Chongqing | 0.371 | 0.972 | 0.382 | drs | 0.310 | 0.954 | 0.325 | drs |
Sichuan | 0.102 | 0.911 | 0.112 | drs | 0.098 | 0.931 | 0.105 | drs |
Guizhou | 0.287 | 0.891 | 0.323 | drs | 0.219 | 0.895 | 0.245 | drs |
Yunnan | 0.138 | 0.863 | 0.160 | drs | 0.139 | 0.867 | 0.160 | drs |
Tibet | 1.000 | 1.000 | 1.000 | – | 1.000 | 1.000 | 1.000 | – |
shaanxi | 0.226 | 0.927 | 0.244 | drs | 0.289 | 0.937 | 0.309 | drs |
Gansu | 0.233 | 0.909 | 0.256 | drs | 0.357 | 0.915 | 0.390 | drs |
Qinghai | 0.824 | 0.965 | 0.853 | drs | 1.000 | 1.000 | 1.000 | – |
Ningxia | 0.676 | 0.981 | 0.690 | drs | 1.000 | 1.000 | 1.000 | – |
Sinkiang | 0.152 | 0.898 | 0.169 | drs | 0.275 | 0.918 | 0.299 | drs |
mean | 0.355 | 0.946 | 0.368 | 0.416 | 0.955 | 0.427 |
Dynamic analysis of medical services efficiency at the provincial level
Year | effch | techch | pech | sech | tfpch |
---|---|---|---|---|---|
2007–2008 | 1.087 | 0.946 | 1.050 | 1.035 | 1.028 |
2008–2009 | 0.916 | 0.528 | 0.904 | 1.013 | 0.483 |
2009–2010 | 1.161 | 0.863 | 1.132 | 1.026 | 1.003 |
2010–2011 | 0.997 | 1.158 | 1.031 | 0.967 | 1.154 |
2011–2012 | 0.966 | 0.985 | 0.928 | 1.042 | 0.952 |
2012–2013 | 1.017 | 0.931 | 1.131 | 0.899 | 0.946 |
2013–2014 | 0.999 | 0.941 | 1.060 | 0.943 | 0.940 |
2014–2015 | 0.957 | 1.084 | 0.947 | 1.010 | 1.037 |
2015–2016 | 1.253 | 0.744 | 1.090 | 1.150 | 0.932 |
2016–2017 | 0.764 | 1.258 | 0.765 | 0.999 | 0.961 |
2017–2018 | 1.018 | 1.037 | 1.026 | 0.992 | 1.056 |
2018–2019 | 1.025 | 0.953 | 1.004 | 1.022 | 0.977 |
mean | 1.007 | 0.932 | 1.000 | 1.006 | 0.938 |
DMU | effch | techch | pech | sech | tfpch |
---|---|---|---|---|---|
Beijing | 1.081 | 0.851 | 1.034 | 1.045 | 0.919 |
Tianjin | 1.000 | 0.866 | 1.000 | 1.000 | 0.866 |
Hebei | 0.979 | 0.969 | 1.029 | 0.952 | 0.949 |
Shanxi | 1.030 | 0.947 | 1.029 | 1.001 | 0.975 |
Inner Mongolia | 1.018 | 0.915 | 1.012 | 1.006 | 0.931 |
Liaoning | 0.981 | 0.946 | 0.980 | 1.001 | 0.928 |
Jilin | 1.073 | 0.890 | 1.083 | 0.991 | 0.954 |
Heilongjiang | 1.002 | 0.937 | 1.002 | 1.000 | 0.939 |
Shanghai | 1.029 | 0.865 | 1.000 | 1.029 | 0.891 |
Jiangsu | 0.973 | 0.963 | 0.946 | 1.029 | 0.937 |
Zhejiang | 0.996 | 0.943 | 0.947 | 1.051 | 0.940 |
Anhui | 0.971 | 0.946 | 0.997 | 0.974 | 0.918 |
Fujian | 1.016 | 0.957 | 1.020 | 0.996 | 0.973 |
Jiangxi | 1.002 | 0.951 | 1.038 | 0.966 | 0.953 |
Shandong | 0.960 | 0.956 | 0.937 | 1.024 | 0.918 |
Henan | 0.982 | 0.949 | 0.954 | 1.030 | 0.933 |
Hubei | 0.998 | 0.951 | 0.968 | 1.031 | 0.949 |
Hunan | 1.013 | 0.930 | 1.007 | 1.005 | 0.942 |
Guangdong | 1.002 | 0.955 | 0.990 | 1.012 | 0.957 |
Guangxi | 0.989 | 0.956 | 0.991 | 0.998 | 0.946 |
Hainan | 1.000 | 0.966 | 1.000 | 1.000 | 0.966 |
Chongqing | 0.985 | 0.922 | 0.919 | 1.072 | 0.908 |
Sichuan | 0.997 | 0.937 | 0.997 | 1.000 | 0.934 |
Guizhou | 0.978 | 0.932 | 0.967 | 1.011 | 0.911 |
Yunnan | 0.998 | 0.950 | 1.000 | 0.997 | 0.948 |
Tibet | 1.000 | 0.918 | 1.000 | 1.000 | 0.918 |
shaanxi | 1.021 | 0.942 | 1.044 | 0.978 | 0.961 |
Gansu | 1.040 | 0.918 | 1.036 | 1.004 | 0.955 |
Qinghai | 1.016 | 0.941 | 1.014 | 1.002 | 0.956 |
Ningxia | 1.035 | 0.912 | 1.034 | 1.001 | 0.944 |
Sinkiang | 1.054 | 0.930 | 1.050 | 1.004 | 0.980 |
mean | 1.007 | 0.932 | 1.000 | 1.006 | 0.938 |
The medical services efficiency in China at the national level
Year | Overall efficiency | Pure technical efficiency | Scale efficiency | Return to scale |
---|---|---|---|---|
2007 | 1 | 1 | 1 | – |
2008 | 0.97 | 0.994 | 0.976 | drs |
2009 | 0.881 | 0.989 | 0.89 | drs |
2010 | 0.839 | 0.986 | 0.851 | drs |
2011 | 0.797 | 0.982 | 0.811 | drs |
2012 | 0.75 | 0.99 | 0.758 | drs |
2013 | 0.722 | 0.994 | 0.727 | drs |
2014 | 0.706 | 0.985 | 0.716 | drs |
2015 | 0.715 | 1 | 0.715 | drs |
2016 | 0.705 | 0.992 | 0.711 | drs |
2017 | 0.702 | 0.995 | 0.706 | drs |
2018 | 0.713 | 1 | 0.713 | drs |
2019 | 0.697 | 1 | 0.697 | drs |
The impact of HRH on the provincial efficiency
Variable | Crste model | Vrste model | Scale model | ||||
---|---|---|---|---|---|---|---|
Coefficient | Prob. | Coefficient | Prob. | Coefficient | Prob. | ||
doctors | x1 | 1.099 | 0.412 | 1.093 | 0.000 | 1.071 | 0.418 |
nurses | x2 | −3.503 | 0.022 | 0.464 | 0.001 | −3.413 | 0.024 |
pharmacists | x3 | 20.588 | 0.029 | 3.860 | 0.000 | 20.040 | 0.031 |
technicians | x4 | 8.046 | 0.589 | 1.998 | 0.131 | 8.049 | 0.585 |
trainees | x5 | 1.409 | 0.647 | 0.578 | 0.032 | 1.508 | 0.620 |
The impact of HRH on the National Efficiency
Variable | Crste model | Vrste model | Scale model | ||||
---|---|---|---|---|---|---|---|
Coefficient | Prob. | Coefficient | Prob. | Coefficient | Prob. | ||
doctors | x1 | 0.129 | 0.889 | 2.096 | 0.000 | −0.179 | 0.837 |
nurses | x2 | 0.935 | 0.044 | 1.265 | 0.000 | 0.684 | 0.119 |
pharmacists | x3 | 63.558 | 0.000 | 7.013 | 0.152 | 57.472 | 0.000 |
technicians | x4 | −38.221 | 0.004 | −12.399 | 0.015 | −29.823 | 0.018 |
trainees | x5 | −9.444 | 0.000 | −0.200 | 0.726 | −8.563 | 0.000 |