Concentration of Healthcare Resources in China: The Spatial–Temporal Evolution and Its Spatial Drivers
Abstract
:1. Introduction
2. Methodology
2.1. Measuring HCR Concentration in China
2.2. Verification of Spatial Correlation
2.3. Types of Spatial Econometric Models and Model Construction
2.4. Variable Selection
2.5. Data Source
3. Empirical Analysis
3.1. The Spatial–Temporal Evolution of HCR Concentration in China
3.2. Spatial Correlation Analysis of HCR concentration in China
3.3. Determining the Spatial Econometric Model for HCR concentration in China
3.4. Robustness Test
4. Conclusions and Suggestions
Author Contributions
Funding
Conflicts of Interest
References
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Primal Indicator | Secondary Indicators | Weights |
---|---|---|
Concentration of Healthcare Resources | Number of Hospitals | 0.126 |
Number of Community Health Service Centers/Stations | 0.175 | |
Number of Certified Physician Assistants | 0.083 | |
Number of Certified Physicians | 0.081 | |
Number of Registered Nurses | 0.079 | |
Number of Managers in Medical Institutions | 0.076 | |
Number of Workers in Medical Institutions | 0.085 | |
Number of Healthcare Practitioners/1000 People | 0.085 | |
Total Assets of Health Institutions (RMB 1000) | 0.093 | |
Number of Hospital Beds/1000 People | 0.118 |
Variables | Unit | Indicator | Observations | Mean | Max | Min | SD |
---|---|---|---|---|---|---|---|
Concentration of HCRs | / | / | 434 | 0.309 | 0.752 | 0.01 | 0.171 |
Economic Development (GDP) | RMB 1 Billion | Regional GDP | 434 | 1562.597 | 8970.523 | 22.034 | 1517.425 |
Population Size (Pop) | Million | Year-end population | 434 | 43.0715 | 111.6900 | 2.7635 | 27.2375 |
Urbanization Level (Urb) | % | % of urban land to total land area | 434 | 51.55 | 89.60 | 20.85 | 14.82 |
Education (Stu) | Number | Number of students in colleges and universities in the region | 434 | 525,563.11 | 2,015,345.00 | 55.00 | 478,440.99 |
Annual Salary (Wag) | RMB | Average annual salary in the healthcare industry | 434 | 36,225.85 | 183,362.23 | 3648.97 | 20,969.68 |
Fiscal Expenditure on Healthcare (Exp) | RMB 1 Billion | Fiscal expenditure on health care | 434 | 20.177 | 130.756 | 0.435 | 19.698 |
Data Range Region | China | Eastern China | Central China | Western China | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
2004 | 2010 | 2017 | 2004 | 2010 | 2017 | 2004 | 2010 | 2017 | 2004 | 2010 | 2017 | |
>0.4 | 35.5 | 29.0 | 25.8 | 25.8 | 22.6 | 19.4 | 6.5 | 3.2 | 3.2 | 3.2 | 3.2 | 3.2 |
0.2–0.4 | 41.9 | 45.2 | 48.4 | 6.5 | 9.7 | 9.7 | 19.4 | 19.4 | 22.6 | 16.1 | 16.1 | 16.1 |
<0.2 | 22.6 | 25.8 | 25.8 | 3.2 | 3.2 | 6.5 | 0 | 3.2 | 0 | 19.4 | 19.4 | 19.4 |
Objectives | Methods | Results | p-value |
---|---|---|---|
Form of Spatial Dependence (lag, error, or both) | LM test no spatial lag | 11.5026 *** | 0.001 |
Robust LM test no spatial lag | 29.2864 *** | 0.000 | |
LM test no spatial error | 186.0900 *** | 0.000 | |
Robust LM test no spatial error | 1.3849 | 0.680 | |
Selection of Spatial Econometric Model (SAR, SEM, and SDM) | Wald spatial lag | 203.8738 *** | 0.000 |
LR spatial lag | 136.0359 *** | 0.000 | |
Wald spatial error | 220.1195 *** | 0.000 | |
LR spatial error | 354.2586 *** | 0.000 |
Statistics | Random Effect | Spatial Fixed Effect | Temporal Fixed Effect | Spatiotemporal Fixed Effects |
---|---|---|---|---|
R2 | 0.6806 | 0.7574 | 0.7535 | 0.8512 |
Log-likelihood | 437.6806 | 471.3144 | 487.4108 | 520.7912 |
Observation | 434 | 434 | 434 | 434 |
Variable | China | Eastern China | Central China | Western China | |
---|---|---|---|---|---|
Direct Effect | GDP | 0.000002 *** (4.339) | 0.000003 ** (2.427) | 0.000007 ** (2.047) | 0.000007 *** (5.777) |
Pop | 0.000037 *** (11.220) | 0.000027 *** (3.417) | 0.000034 ** (2.411) | 0.000034 *** (8.809) | |
Urb | 0.004388 *** (9.049) | 0.001679 * (1.669) | 0.009106 ** (2.296) | 0.001228 *** (2.695) | |
Stu | 0.000000 * (1.881) | 0.000000 * (1.910) | 0.000000 * (1.678) | 0.000000 (1.604) | |
Wag | 0.000002 *** (3.488) | 0.000007 *** (4.401) | 0.000003 (0.732) | 0.000001 ** (2.082) | |
Exp | 0.000000 *** (3.606) | 0.000000 *** (3.865) | −0.000000 (−0.392) | 0.000000 (0.222) | |
Indirect Effect | GDP | −0.000002 ** (−2.152) | −0.000004 *** (−2.730) | −0.000009 *** (−2.601) | −0.000011 *** (−6.824) |
Pop | −0.000012 ** (−2.156) | 0.000015 (1.473) | 0.000001 (0.039) | −0.000020 *** (3.607) | |
Urb | 0.000152 (0.203) | 0.002032 * (1.673) | −0.005185 (−1.303) | 0.003092 *** (4.878) | |
Stu | −0.000000 (−1.103) | 0.000000 (0.191) | 0.000000 (0.435) | −0.000000 (−0.318) | |
Wag | −0.000004 *** (−3.640) | −0.000008 *** (3.880) | 0.000003 (0.696) | −0.000002 *** (−2.754) | |
Exp | −0.000000 ** (−2.486) | −0.000000*** (−2.730) | 0.000000 (0.183) | 0.000000 (0.068) | |
Overall Effect | GDP | 0.000001 (0.855) | −0.000001 (−0.927) | −0.000002** (−2.027) | −0.000004 *** (−3.354) |
Pop | 0.000025 *** (5.035) | 0.000043 *** (6.075) | 0.000035 *** (8.568) | 0.000014 *** (3.142) | |
Urb | 0.004540 *** (8.999) | 0.003711 *** (5.464) | 0.003921 *** (7.468) | 0.004320*** (9.868) | |
Stu | 0.000000 (0.577) | 0.000000* (1.945) | 0.000000 * (1.860) | −0.000000 (−0.895) | |
Wag | −0.000001 * (1.949) | −0.000001(−0.669) | 0.000000 (0.040) | −0.000001 * (−1.747) | |
Exp | −0.000000 (−0.319) | 0.000000 (0.237) | −0.000000 (−0.586) | 0.000000 (0.279) |
Variables | China | Eastern China | Central China | Western China | |
---|---|---|---|---|---|
Direct Effect | GDP | 0.000002 *** (4.554) | 0.000002 * (1.900) | 0.000006 ** (2.349) | 0.000006 *** (6.743) |
Pop | 0.000032 *** (14.189) | 0.000022 *** (3.978) | 0.000029 ** (2.500) | 0.000030 *** (8.023) | |
Urb | 0.003965 *** (10.057) | 0.001408 * (1.805) | 0.006763 ** (2.348) | 0.002035 *** (2.709) | |
Stu | 0.000000 * (1.775) | 0.000000 * (1.952) | 0.000000 (1.605) | 0.000000 (1.357) | |
Wag | 0.000002 *** (4.987) | 0.000006 *** (5.237) | 0.000003 (0.895) | 0.000001 ** (2.226) | |
Exp | 0.000000 *** (3.025) | 0.000000 *** (4.345) | −0.000000 (−0.674) | 0.000000 (0.451) | |
Indirect Effect | GDP | −0.000002 ** (−2.235) | −0.000003 *** (−3.793) | −0.000007 *** (−2.987) | −0.000009 *** (−9.856) |
Pop | −0.000010 ** (−2.038) | 0.000012 (1.008) | 0.000001 (0.821) | −0.000017 *** (−3.998) | |
Urb | 0.000137 (0.508) | 0.001907 (1.409) | −0.004925 (−1.465) | 0.002645 *** (5.458) | |
Stu | −0.000000 (−0.934) | 0.000000 (0.803) | 0.000000 (0.653) | −0.000000 (−0.985) | |
Wag | −0.000005 *** (−4.578) | −0.000007*** (−4.936) | 0.000003 (0.803) | −0.000002 *** (−3.783) | |
Exp | −0.000000 ** (−2.079) | −0.000000 *** (−3.785) | 0.000000 (0.907) | 0.000000 (0.708) | |
Overall Effect | GDP | 0.000001 (0.923) | −0.000000 (−1.005) | −0.000001 ** (−2.154) | −0.000003 *** (−3.674) |
Pop | 0.000023 *** (5.872) | 0.000037 *** (9.008) | 0.000029 *** (9.654) | 0.000010 *** (2.985) | |
Urb | 0.003908 *** (9.356) | 0.002901 *** (6.459) | 0.004528 *** (8.485) | 0.003958 *** (7.592) | |
Stu | 0.000000 (0.782) | 0.000000 * (1.788) | 0.000000 * (1.900) | −0.000000 (−0.706) | |
Wag | −0.000001 * (−1.687) | −0.000001 (−0.892) | 0.000000 (0.140) | −0.000001 * (−1.876) | |
Exp | −0.000000 (−0.875) | 0.000000 (0.450) | −0.000000 (−0.765) | 0.000000 (0.682) |
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Share and Cite
Guo, Q.; Luo, K. Concentration of Healthcare Resources in China: The Spatial–Temporal Evolution and Its Spatial Drivers. Int. J. Environ. Res. Public Health 2019, 16, 4606. https://doi.org/10.3390/ijerph16234606
Guo Q, Luo K. Concentration of Healthcare Resources in China: The Spatial–Temporal Evolution and Its Spatial Drivers. International Journal of Environmental Research and Public Health. 2019; 16(23):4606. https://doi.org/10.3390/ijerph16234606
Chicago/Turabian StyleGuo, Qingbin, and Kang Luo. 2019. "Concentration of Healthcare Resources in China: The Spatial–Temporal Evolution and Its Spatial Drivers" International Journal of Environmental Research and Public Health 16, no. 23: 4606. https://doi.org/10.3390/ijerph16234606