Background
Method
Study setting
Sampling and data collection
Data envelopment analysis (DEA) model
Tobit regression model
Instruments
Category | Variable | Definition |
---|---|---|
Input | Total expenditure | Capital consumption and defray in the process of service provision and other activities, including healthcare expenditure, drug and medicine expenditure. |
Number of doctors | Registered doctors at the end of year, excluding retirees and temporary staff | |
Number of nurses | Registered nurses at the end of year, excluding retirees and temporary staff | |
Number of open beds | The number of available bed days divided by the number of days in a year | |
Output | Total revenue | Revenue gained from service provision and other activities, including healthcare revenue, drug and medicine sales, financial subsidies. |
Number of outpatients and emergency visits | The number of patients coming for outpatient and emergency diagnostic services | |
Number of discharged patients | The number of discharged patients after hospitalization for various reasons |
Results
Basic information of the input and output indicators
Poor counties | Non-poor counties |
p-value | |
---|---|---|---|
Total revenue (10 thousand, RMB) | 1,660 | 2,421 | 0.07 |
Number of discharged patients | 2,759 | 4,026 | 0.14 |
Number of outpatients and emergency visits | 38,343 | 68,049 | 0.02 |
Total expenditure (10 thousand, RMB) | 1450 | 2128 | 0.12 |
Number of doctors | 21.2 | 26.7 | 0.14 |
Number of nurses | 36.7 | 46 | 0.32 |
Number of open beds | 64.4 | 68 | 0.82 |
Results of the DEA model
Operational efficiency of the 32 county-level MCHHs
DMU | Crste Score | Vrste Score | Scale Efficiency Score | Type of scale inefficiency | DMU | Crste Score | Vrste Score | Scale Efficiency Score | Type of scale inefficiency |
---|---|---|---|---|---|---|---|---|---|
X1a
| 1.000 | 1.000 | 1.000 | - | X17a
| 1.000 | 1.000 | 1.000 | - |
X2a
| 0.945 | 0.966 | 0.978 | drs | X18a
| 1.000 | 1.000 | 1.000 | - |
X3a
| 0.592 | 0.780 | 0.759 | irs | X19a
| 0.691 | 0.702 | 0.985 | drs |
X4a
| 1.000 | 1.000 | 1.000 | - | X20 | 0.602 | 0.625 | 0.963 | drs |
X5a
| 0.906 | 0.954 | 0.949 | irs | X21 | 1.000 | 1.000 | 1.000 | - |
X6a
| 0.675 | 0.715 | 0.944 | irs | X22 | 0.748 | 0.754 | 0.992 | irs |
X7a
| 0.918 | 1.000 | 0.918 | irs | X23a
| 1.000 | 1.000 | 1.000 | - |
X8 | 1.000 | 1.000 | 1.000 | - | X24 | 1.000 | 1.000 | 1.000 | - |
X9a
| 0.880 | 1.000 | 0.880 | irs | X25 | 1.000 | 1.000 | 1.000 | - |
X10 | 1.000 | 1.000 | 1.000 | - | X26a
| 1.000 | 1.000 | 1.000 | - |
X11 | 1.000 | 1.000 | 1.000 | - | X27 | 0.965 | 0.979 | 0.985 | drs |
X12a
| 1.000 | 1.000 | 1.000 | - | X28*
| 0.799 | 0.811 | 0.986 | drs |
X13 | 1.000 | 1.000 | 1.000 | - | X29 | 0.853 | 1.000 | 0.853 | irs |
X14a
| 0.733 | 0.832 | 0.880 | drs | X30 | 1.000 | 1.000 | 1.000 | - |
X15a
| 0.521 | 0.879 | 0.593 | irs | X31 | 1.000 | 1.000 | 1.000 | - |
X16a
| 0.612 | 0.682 | 0.898 | irs | X32 | 0.560 | 0.815 | 0.688 | irs |
mean | 0.875 | 0.922 | 0.945 |
Slack variable analysis
Poor counties | Non-poor counties | |
---|---|---|
Total revenue (10 thousand, RMB) | 1.7 | 6.8 |
Number of discharged patients | 723 | 149.9 |
Number of outpatient and emergency visits | 15455.6 | 29057.6 |
Total expenditure (10 thousand, RMB) | 85.4 | 37.5 |
Number of doctors | 7.6 | 1.8 |
Number of nurses | 11.3 | 4.4 |
Actual number of open beds | 11.5 | 9.2 |
Tobit regression model
crste score variable | Coeficient | Standar d Error |
P value | 95 % Confidence interval | |
---|---|---|---|---|---|
County population | −0.001654 | 0.001156 | 0.164 | (−0.004026 | 0.000719) |
GDP per capita | 0.057936 | 0.028070 | 0.049 | (0.000340 | 0.115531) |
Number of open beds | −0.002823 | 0.001023 | 0.010 | (−0.004922 | −0.000724) |
Number of health professionals | 0.004665 | 0.001176 | 0.000 | (0.002252 | 0.007078) |
Total expenditure | 0.000011 | 0.000030 | 0.722 | (−0.000051 | 0.000072) |
Constant | 0.551086 | 0.074430 | 0.000 | (0.398369 | 0.703803) |