Geographical Pattern Evolution of Health Resources in China: Spatio-Temporal Dynamics and Spatial Mismatch
Abstract
:1. Introduction
2. Literature Review
2.1. Medical Resource Change and Allocation Spatial Inequality Analysis Are the Mainstream, and the Research on Geospatial Health Is Insufficient
2.2. Qualitative, Statistical and Regression Analysis Are the Mainstream, and Lack of Support for Evidence-Based Decision Making
3. Materials and Methods
3.1. Study Area
3.2. Research Steps and Data Sources
3.3. Research Methods
3.3.1. Boston Consulting Group Matrix
3.3.2. Spatial Mismatch Index
3.3.3. Spatial Econometric Model
3.3.4. Geodetector
4. Results
4.1. Spatio-Temporal Dynamics
4.1.1. Spatial Pattern
4.1.2. Change Process
4.1.3. Evolution Trend
4.2. Spatial Mismatch Analysis
4.2.1. Demand: Population Potential Consumption
- (1)
- Spatial Mismatch Type of Population
- (2)
- Mismatch Index Contribution Rate of Population
4.2.2. Supply: Economic Carrying Capacity
- (1)
- Spatial Mismatch Type of GDP
- (2)
- Mismatch Index Contribution Rate of GDP
4.3. Driving Mechanism
4.3.1. Influence Factor
4.3.2. Interaction Effect
5. Discussion
5.1. Extended Thinking and External Evidence
5.2. Sustainable Development Spatial Strategies
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
City | Hospitals | Doctors | Beds | GDP | Population | |||||
---|---|---|---|---|---|---|---|---|---|---|
2010 | 2020 | 2010 | 2020 | 2010 | 2020 | 2010 | 2020 | 2010 | 2020 | |
Beijing | 0.0100 | 0.0104 | 0.0273 | 0.0258 | 0.0194 | 0.0140 | 0.0323 | 0.0357 | 0.0147 | 0.0155 |
Tianjin | 0.0048 | 0.0057 | 0.0118 | 0.0106 | 0.0102 | 0.0075 | 0.0211 | 0.0139 | 0.0097 | 0.0098 |
Hebei | 0.0869 | 0.0850 | 0.0534 | 0.0501 | 0.0522 | 0.0486 | 0.0467 | 0.0358 | 0.0539 | 0.0529 |
Shanxi | 0.0439 | 0.0402 | 0.0337 | 0.0261 | 0.0326 | 0.0246 | 0.0211 | 0.0174 | 0.0268 | 0.0248 |
Inner Mongolia | 0.0241 | 0.0240 | 0.0206 | 0.0189 | 0.0195 | 0.0178 | 0.0267 | 0.0171 | 0.0185 | 0.0170 |
Liaoning | 0.0371 | 0.0334 | 0.0386 | 0.0298 | 0.0427 | 0.0346 | 0.0422 | 0.0248 | 0.0328 | 0.0302 |
Jilin | 0.0207 | 0.0250 | 0.0228 | 0.0203 | 0.0240 | 0.0190 | 0.0198 | 0.0122 | 0.0206 | 0.0170 |
Heilongjiang | 0.0236 | 0.0200 | 0.0320 | 0.0231 | 0.0334 | 0.0278 | 0.0237 | 0.0135 | 0.0287 | 0.0225 |
Shanghai | 0.0050 | 0.0058 | 0.0210 | 0.0194 | 0.0220 | 0.0167 | 0.0393 | 0.0382 | 0.0173 | 0.0176 |
Jiangsu | 0.0330 | 0.0349 | 0.0560 | 0.0611 | 0.0563 | 0.0588 | 0.0948 | 0.1015 | 0.0590 | 0.0601 |
Zhejiang | 0.0320 | 0.0336 | 0.0430 | 0.0490 | 0.0385 | 0.0397 | 0.0634 | 0.0638 | 0.0408 | 0.0459 |
Anhui | 0.0245 | 0.0287 | 0.0377 | 0.0374 | 0.0393 | 0.0448 | 0.0283 | 0.0382 | 0.0447 | 0.0433 |
Fujian | 0.0288 | 0.0275 | 0.0243 | 0.0261 | 0.0236 | 0.0238 | 0.0337 | 0.0434 | 0.0277 | 0.0295 |
Jiangxi | 0.0364 | 0.0359 | 0.0282 | 0.0273 | 0.0260 | 0.0314 | 0.0216 | 0.0254 | 0.0335 | 0.0320 |
Shandong | 0.0715 | 0.0830 | 0.0788 | 0.0763 | 0.0799 | 0.0711 | 0.0896 | 0.0722 | 0.0719 | 0.0721 |
Henan | 0.0808 | 0.0730 | 0.0721 | 0.0698 | 0.0684 | 0.0733 | 0.0528 | 0.0543 | 0.0705 | 0.0705 |
Hubei | 0.0366 | 0.0347 | 0.0426 | 0.0400 | 0.0419 | 0.0452 | 0.0365 | 0.0429 | 0.0429 | 0.0407 |
Hunan | 0.0634 | 0.0548 | 0.0452 | 0.0456 | 0.0488 | 0.0571 | 0.0367 | 0.0413 | 0.0493 | 0.0471 |
Guangdong | 0.0479 | 0.0546 | 0.0723 | 0.0747 | 0.0627 | 0.0621 | 0.1053 | 0.1094 | 0.0783 | 0.0895 |
Guangxi | 0.0349 | 0.0331 | 0.0325 | 0.0351 | 0.0300 | 0.0325 | 0.0219 | 0.0219 | 0.0346 | 0.0356 |
Hainan | 0.0050 | 0.0060 | 0.0063 | 0.0070 | 0.0054 | 0.0064 | 0.0047 | 0.0055 | 0.0065 | 0.0072 |
Chongqing | 0.0187 | 0.0205 | 0.0195 | 0.0224 | 0.0216 | 0.0259 | 0.0181 | 0.0247 | 0.0216 | 0.0228 |
Sichuan | 0.0793 | 0.0809 | 0.0570 | 0.0612 | 0.0629 | 0.0714 | 0.0393 | 0.0480 | 0.0603 | 0.0594 |
Guizhou | 0.0271 | 0.0282 | 0.0188 | 0.0272 | 0.0220 | 0.0304 | 0.0105 | 0.0176 | 0.0261 | 0.0274 |
Yunnan | 0.0244 | 0.0260 | 0.0253 | 0.0341 | 0.0328 | 0.0357 | 0.0165 | 0.0242 | 0.0345 | 0.0335 |
Tibet | 0.0053 | 0.0068 | 0.0020 | 0.0030 | 0.0018 | 0.0020 | 0.0012 | 0.0019 | 0.0023 | 0.0026 |
Shaanxi | 0.0381 | 0.0342 | 0.0317 | 0.0331 | 0.0297 | 0.0299 | 0.0232 | 0.0259 | 0.0280 | 0.0280 |
Gansu | 0.0285 | 0.0256 | 0.0168 | 0.0170 | 0.0189 | 0.0189 | 0.0094 | 0.0089 | 0.0192 | 0.0177 |
Qinghai | 0.0062 | 0.0063 | 0.0043 | 0.0048 | 0.0043 | 0.0045 | 0.0031 | 0.0030 | 0.0042 | 0.0042 |
Ningxia | 0.0044 | 0.0045 | 0.0048 | 0.0053 | 0.0049 | 0.0045 | 0.0039 | 0.0039 | 0.0047 | 0.0051 |
Xinjiang | 0.0171 | 0.0178 | 0.0194 | 0.0182 | 0.0243 | 0.0199 | 0.0124 | 0.0136 | 0.0164 | 0.0184 |
City | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
Beijing | 0.0550 | 0.0751 | 0.0548 | 0.0350 | 0.0443 | 0.0124 | 0.0597 | 0.0321 | 0.0591 | 0.0593 |
Tianjin | 0.0165 | 0.0463 | 0.0192 | 0.0091 | 0.0429 | 0.0088 | 0.0211 | 0.0093 | 0.0446 | 0.0162 |
Hebei | 0.0340 | 0.0221 | 0.0382 | 0.0324 | 0.0304 | 0.0574 | 0.0381 | 0.0433 | 0.0292 | 0.0413 |
Shanxi | 0.0164 | 0.0230 | 0.0229 | 0.0172 | 0.0317 | 0.0230 | 0.0267 | 0.0230 | 0.0328 | 0.0208 |
Inner Mongolia | 0.0154 | 0.0328 | 0.0205 | 0.0121 | 0.0342 | 0.0147 | 0.0200 | 0.0199 | 0.0321 | 0.0147 |
Liaoning | 0.0244 | 0.0268 | 0.0265 | 0.0229 | 0.0365 | 0.0274 | 0.0380 | 0.0219 | 0.0394 | 0.0298 |
Jilin | 0.0117 | 0.0231 | 0.0108 | 0.0098 | 0.0317 | 0.0148 | 0.0211 | 0.0159 | 0.0349 | 0.0130 |
Heilongjiang | 0.0123 | 0.0194 | 0.0115 | 0.0130 | 0.0332 | 0.0186 | 0.0227 | 0.0213 | 0.0348 | 0.0197 |
Shanghai | 0.0515 | 0.0710 | 0.0704 | 0.0407 | 0.0452 | 0.0146 | 0.0510 | 0.0289 | 0.0506 | 0.0495 |
Jiangsu | 0.0981 | 0.0552 | 0.0905 | 0.0946 | 0.0372 | 0.0599 | 0.0727 | 0.0534 | 0.0343 | 0.0754 |
Zhejiang | 0.0655 | 0.0459 | 0.0724 | 0.0680 | 0.0365 | 0.0388 | 0.0524 | 0.0444 | 0.0333 | 0.0639 |
Anhui | 0.0360 | 0.0289 | 0.0321 | 0.0468 | 0.0295 | 0.0471 | 0.0352 | 0.0404 | 0.0262 | 0.0350 |
Fujian | 0.0379 | 0.0482 | 0.0307 | 0.0475 | 0.0348 | 0.0285 | 0.0284 | 0.0277 | 0.0271 | 0.0264 |
Jiangxi | 0.0225 | 0.0259 | 0.0250 | 0.0265 | 0.0306 | 0.0344 | 0.0223 | 0.0340 | 0.0250 | 0.0264 |
Shandong | 0.0712 | 0.0329 | 0.0655 | 0.0746 | 0.0319 | 0.0775 | 0.0644 | 0.0554 | 0.0330 | 0.0693 |
Henan | 0.0487 | 0.0253 | 0.0416 | 0.0574 | 0.0281 | 0.0820 | 0.0464 | 0.0575 | 0.0279 | 0.0526 |
Hubei | 0.0405 | 0.0339 | 0.0251 | 0.0459 | 0.0318 | 0.0402 | 0.0406 | 0.0540 | 0.0299 | 0.0371 |
Hunan | 0.0393 | 0.0287 | 0.0300 | 0.0415 | 0.0297 | 0.0514 | 0.0325 | 0.0391 | 0.0349 | 0.0380 |
Guangdong | 0.1137 | 0.0402 | 0.1291 | 0.1026 | 0.0375 | 0.0778 | 0.0854 | 0.0939 | 0.0283 | 0.0889 |
Guangxi | 0.0209 | 0.0202 | 0.0171 | 0.0200 | 0.0274 | 0.0404 | 0.0227 | 0.0331 | 0.0267 | 0.0289 |
Hainan | 0.0061 | 0.0251 | 0.0081 | 0.0050 | 0.0305 | 0.0069 | 0.0064 | 0.0117 | 0.0240 | 0.0062 |
Chongqing | 0.0240 | 0.0356 | 0.0209 | 0.0301 | 0.0352 | 0.0238 | 0.0222 | 0.0230 | 0.0364 | 0.0223 |
Sichuan | 0.0463 | 0.0265 | 0.0425 | 0.0531 | 0.0287 | 0.0622 | 0.0482 | 0.0546 | 0.0328 | 0.0527 |
Guizhou | 0.0165 | 0.0211 | 0.0178 | 0.0200 | 0.0269 | 0.0309 | 0.0198 | 0.0300 | 0.0227 | 0.0205 |
Yunnan | 0.0230 | 0.0237 | 0.0211 | 0.0250 | 0.0253 | 0.0322 | 0.0248 | 0.0377 | 0.0276 | 0.0272 |
Tibet | 0.0017 | 0.0239 | 0.0022 | 0.0019 | 0.0181 | 0.0025 | 0.0020 | 0.0076 | 0.0109 | 0.0016 |
Shaanxi | 0.0228 | 0.0302 | 0.0225 | 0.0245 | 0.0317 | 0.0273 | 0.0330 | 0.0270 | 0.0367 | 0.0223 |
Gansu | 0.0090 | 0.0164 | 0.0087 | 0.0093 | 0.0264 | 0.0180 | 0.0162 | 0.0196 | 0.0274 | 0.0135 |
Qinghai | 0.0028 | 0.0232 | 0.0030 | 0.0022 | 0.0304 | 0.0039 | 0.0040 | 0.0091 | 0.0349 | 0.0046 |
Ningxia | 0.0036 | 0.0248 | 0.0042 | 0.0033 | 0.0329 | 0.0049 | 0.0058 | 0.0063 | 0.0333 | 0.0047 |
Xinjiang | 0.0129 | 0.0244 | 0.0148 | 0.0078 | 0.0286 | 0.0176 | 0.0166 | 0.0250 | 0.0292 | 0.0182 |
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Type | Code | Indicator | Data Source |
---|---|---|---|
Dependent variable | Hospital | China statistical yearbook China health statistical yearbook | |
Doctor | |||
Bed | |||
Independent variable | Service industry added value | China statistical yearbook Provincial statistical yearbook | |
Per capita GDP | |||
Government revenue | |||
Social consumption | |||
Urbanization rate | |||
Children and elderly population | |||
High quality (university or above) population | |||
Health care government investment | |||
Residents’ medical services consumption | |||
Medical insurance fund expenditure | |||
Demand and supply variable | Population | ||
GDP |
Indicator Name | Global Moran’s I | p | Z | ||
---|---|---|---|---|---|
Evolution trend | Hospitals | 0.14 | 0.10 | 1.39 | |
doctors | 0.15 | 0.08 | 1.45 | ||
beds | 0.23 | 0.02 | 2.23 | ||
Spatial mismatch type of population | Hospitals | 2010 | 0.21 | 0.03 | 2.06 |
2020 | 0.28 | 0.00 | 2.69 | ||
Doctors | 2010 | 0.15 | 0.06 | 1.67 | |
2020 | −0.10 | 0.30 | −0.55 | ||
Beds | 2010 | 0.18 | 0.05 | 1.72 | |
2020 | 0.07 | 0.21 | 0.80 | ||
Spatial mismatch type of GDP | Hospitals | 2010 | 0.23 | 0.03 | 2.24 |
2020 | 0.24 | 0.02 | 2.42 | ||
Doctors | 2010 | 0.30 | 0.01 | 2.81 | |
2020 | 0.46 | 0.00 | 4.25 | ||
Beds | 2010 | 0.15 | 0.07 | 1.49 | |
2020 | 0.19 | 0.04 | 1.94 |
Indicator | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Hospital | q | 0.54 | 0.03 | 0.48 | 0.33 | 0.06 | 0.80 | 0.34 | 0.58 | 0.10 | 0.36 | 0.52 |
P | 0.01 | 0.34 | 0.05 | 0.08 | 0.17 | 0.00 | 0.04 | 0.02 | 0.09 | 0.04 | 0.05 | |
Doctor | q | 0.74 | 0.03 | 0.66 | 0.83 | 0.00 | 0.92 | 0.73 | 0.87 | 0.04 | 0.76 | 0.79 |
P | 0.00 | 0.57 | 0.01 | 0.00 | 0.92 | 0.00 | 0.00 | 0.00 | 0.29 | 0.00 | 0.05 | |
Bed | q | 0.71 | 0.08 | 0.59 | 0.75 | 0.01 | 0.95 | 0.60 | 0.83 | 0.07 | 0.64 | 0.72 |
P | 0.00 | 0.66 | 0.02 | 0.00 | 0.65 | 0.00 | 0.01 | 0.00 | 0.16 | 0.00 | 0.05 |
0.54 | ||||||||||
0.69 | 0.03 | |||||||||
0.72 | 0.73 | 0.48 | ||||||||
0.63 | 0.61 | 0.74 | 0.33 | |||||||
0.72 | 0.09 | 0.73 | 0.64 | 0.06 | ||||||
0.85 | 0.88 | 0.88 | 0.91 | 0.87 | 0.80 | |||||
0.59 | 0.64 | 0.60 | 0.48 | 0.64 | 0.84 | 0.34 | ||||
0.73 | 0.69 | 0.76 | 0.64 | 0.69 | 0.87 | 0.67 | 0.58 | |||
0.64 | 0.16 | 0.67 | 0.51 | 0.14 | 0.83 | 0.57 | 0.63 | 0.10 | ||
0.66 | 0.69 | 0.61 | 0.45 | 0.71 | 0.85 | 0.47 | 0.72 | 0.63 | 0.36 |
0.74 | ||||||||||
0.81 | 0.03 | |||||||||
0.81 | 0.81 | 0.66 | ||||||||
0.91 | 0.89 | 0.92 | 0.83 | |||||||
0.85 | 0.07 | 0.83 | 0.91 | 0.00 | ||||||
0.97 | 0.96 | 0.97 | 0.98 | 0.96 | 0.92 | |||||
0.83 | 0.84 | 0.88 | 0.92 | 0.85 | 0.97 | 0.73 | ||||
0.95 | 0.89 | 0.93 | 0.93 | 0.89 | 0.97 | 0.96 | 0.87 | |||
0.83 | 0.21 | 0.81 | 0.85 | 0.10 | 0.93 | 0.87 | 0.88 | 0.04 | ||
0.93 | 0.92 | 0.90 | 0.93 | 0.93 | 0.98 | 0.80 | 0.94 | 0.91 | 0.76 |
0.71 | ||||||||||
0.88 | 0.08 | |||||||||
0.78 | 0.83 | 0.59 | ||||||||
0.87 | 0.95 | 0.89 | 0.75 | |||||||
0.85 | 0.17 | 0.79 | 0.91 | 0.01 | ||||||
0.97 | 0.99 | 0.97 | 0.98 | 0.97 | 0.95 | |||||
0.81 | 0.90 | 0.86 | 0.86 | 0.83 | 0.97 | 0.60 | ||||
0.93 | 0.92 | 0.90 | 0.94 | 0.89 | 0.98 | 0.91 | 0.83 | |||
0.79 | 0.27 | 0.74 | 0.80 | 0.10 | 0.96 | 0.81 | 0.87 | 0.07 | ||
0.90 | 0.90 | 0.87 | 0.91 | 0.90 | 0.97 | 0.68 | 0.93 | 0.89 | 0.64 |
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Zhou, Y.; Zhao, K.; Han, J.; Zhao, S.; Cao, J. Geographical Pattern Evolution of Health Resources in China: Spatio-Temporal Dynamics and Spatial Mismatch. Trop. Med. Infect. Dis. 2022, 7, 292. https://doi.org/10.3390/tropicalmed7100292
Zhou Y, Zhao K, Han J, Zhao S, Cao J. Geographical Pattern Evolution of Health Resources in China: Spatio-Temporal Dynamics and Spatial Mismatch. Tropical Medicine and Infectious Disease. 2022; 7(10):292. https://doi.org/10.3390/tropicalmed7100292
Chicago/Turabian StyleZhou, Yong, Kaixu Zhao, Junling Han, Sidong Zhao, and Jingyuan Cao. 2022. "Geographical Pattern Evolution of Health Resources in China: Spatio-Temporal Dynamics and Spatial Mismatch" Tropical Medicine and Infectious Disease 7, no. 10: 292. https://doi.org/10.3390/tropicalmed7100292