Regional Disparity and Patients Mobility: Benefits and Spillover Effects of the Spatial Network Structure of the Health Services in China
Abstract
:1. Introduction
2. Method and Materials
2.1. Modified Gravitational Model
2.2. Social Network Analysis (SNA)
2.2.1. Overall Network Characteristics
- Network density reflects the degree of density of inter-province medical economy relationships. The calculation of network density is the ratio of the actual number of relationships between provinces to the maximum possible relationship of the overall network. The formula is D = . D is the network density, L is the number of relationships actually possessed, and N is the number of regions.
- Connectedness is used to measure the robustness of the medical economic spatial association network. The higher the correlation is, the stronger the robustness and the deeper the participation of the spatial association network in the medical fields in each province. The formula is C = . C is the connectedness, V is the logarithm of the unreachable point in the network, and N is the number of regions.
- Hierarchy is used to measure the extent to which the provinces in the network are asymmetrically reachable. The higher the network level is, the more stringent the network. A few provinces in the medical spatial network play a leading and dominant role. The formula is . H is the hierarchy, K is the logarithm of the symmetrically reachable point in the network, and max (K) is the logarithm of the largest possible reachable point in the network.
- Efficiency reflects the connection efficiency between the provinces in the spatial network of medical economic development. The higher the network efficiency is, the fewer the connections between the provinces, the looser the spatial network, and the greater the medical resource flow in each province. It is difficult for inter-province collaboration and promotion in the medical field to be achieved through the network. The formula is . E is the efficiency, M is the number of extra lines in the network, and max(M) is the maximum number of possible extra lines.
2.2.2. Ego Network Characteristics
- Degree centrality reflects the central position of a single province in the medical economic spatial network. The higher the degree centrality is, the more connections there are with other provinces, and the more centrally located the province is in the network. The formula is . De is the degree centrality, n represents the number of provinces directly associated with the province, and N represents the maximum number of connected provinces. The degree centrality includes two indicators: out-degree and in-degree. When the out-degree is greater than the in-degree, the node will exhibit a spillover effect; otherwise, it will have a beneficial effect. When the two are equal, there will be equilibrium.
- Closeness centrality reflects the degree of direct association of individual provinces with other provinces in the spatial association network of the medical economy. The higher the closeness is, the more direct inter-province contact there is; thus, the province is a central actor in the network. The formula is . is closeness; dij represents the shortcut distance between i and j.
- Betweenness centrality reflects the extent to which a province is in the middle of the path of other medical economics. In the spatial network structure of medical economic development, the higher the intermediary degree of a province is, the more relevant the province. On the shortest path, the stronger the ability to control the relationship between other provinces, the greater the role of the “center” or “bridge” played by other provinces. The formula is (j). Cbi is betweenness, and bjk(i) is the ability of the third region i to control the association of j and k.
- The block model analysis (CONCOR analysis) in social network analysis is used mainly to describe the roles and status of each province in the overall medical economic spatial network structure. To facilitate an intuitive analysis, the complex network can be simplified into a block model and image matrix. The spatial clustering method is used to divide the network into blocks. The block models reflect the role of each block in the network. Block models were proposed by White in 1976 [26] to analyze the role of network node groups. Referring to the indicators of Wasseman and Faust to evaluate the internal modules of the network [27], the regional medical economic spatial association network can be divided into four sections: the net beneficial block, bidirectional spillover block, net spillover block, and broker block. The members of the net beneficial block have more internal relationships, fewer external relationships, and fewer spillover effects on other blocks. The members of the bidirectional spillover block have more relationships with members of this block and members of other blocks but receive fewer external contacts and have a two-way spillover effect on members in other blocks. In the broker block, there are fewer connections between members within the block, and members of other blocks receive and issue relationships, acting as a “bridge” in the network. The members of the net spillover block have more relationships with members of other blocks and have fewer external relationships. Through CONCOR analysis, this study examines the inter-province medical economic spatial association network and the internal structure status of the block.
2.3. Data Collection
3. Results
3.1. Overall Network Structural Characteristics of China’s Provincial Health Economic Spatial Network
3.2. Ego Network Structural Characteristics of China’s Provincial Health Economic Spatial Network
3.2.1. Degree Centrality
3.2.2. Closeness Centrality
3.2.3. Betweenness Centrality
3.3. CONCOR Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Region | Province |
---|---|
Eastern | Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Guangxi, Hainan |
Central | Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, Hunan |
Western | Sichuan, Chongqing, Guizhou, Yunnan, Tibet, Shanxi, Gansu, Ningxia, Qinghai, Xinjiang |
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Network Size | Number of Relationship | Density | Connectedness | Hierarchy | Efficiency | |
---|---|---|---|---|---|---|
Structure | 31 | 209 | 0.2247 | 1.0000 | 0.4541 | 0.6920 |
Province | Degree Centrality | Closeness Centrality | Betweenness Centrality | ||||||
---|---|---|---|---|---|---|---|---|---|
Out Degree | In Degree | Degree | Benefit or Not | Centrality Rank | Degree | Centrality Rank | Degree | Centrality Rank | |
Shanghai | 9 | 27 | 93.333 | Yes | 1 | 93.750 | 1 | 15.051 | 1 |
Jiangsu | 4 | 28 | 93.333 | Yes | 1 | 93.750 | 1 | 15.051 | 1 |
Beijing | 5 | 23 | 76.667 | Yes | 2 | 81.081 | 2 | 10.972 | 2 |
Tianjin | 5 | 22 | 76.667 | Yes | 2 | 81.081 | 2 | 10.811 | 3 |
Zhejiang | 5 | 19 | 66.667 | Yes | 3 | 75.000 | 3 | 6.608 | 4 |
Guangdong | 11 | 10 | 46.667 | No | 4 | 65.217 | 4 | 2.286 | 5 |
Shandong | 8 | 11 | 43.333 | Yes | 5 | 63.830 | 5 | 1.605 | 6 |
Gansu | 11 | 3 | 36.667 | No | 6 | 61.224 | 6 | 0.980 | 8 |
Fujian | 7 | 7 | 36.667 | Balance | 6 | 61.224 | 6 | 1.204 | 7 |
Henan | 6 | 9 | 30.000 | Yes | 7 | 58.824 | 7 | 0.508 | 9 |
Guangxi | 7 | 4 | 30.000 | No | 7 | 58.824 | 7 | 0.393 | 12 |
Chongqing | 8 | 4 | 30.000 | No | 7 | 58.825 | 7 | 0.362 | 13 |
Yunnan | 8 | 2 | 26.667 | No | 8 | 57.962 | 8 | 0.347 | 14 |
Tibet | 8 | 0 | 26.667 | No | 8 | 57.692 | 9 | 0.335 | 15 |
Sichuan | 8 | 2 | 26.667 | No | 8 | 57.692 | 9 | 0.294 | 16 |
Hainan | 8 | 1 | 26.667 | No | 8 | 57.692 | 9 | 0.235 | 18 |
Guizhou | 8 | 2 | 26.667 | No | 8 | 57.692 | 9 | 0.347 | 14 |
Anhui | 3 | 8 | 26.667 | Yes | 8 | 57.692 | 9 | 0.403 | 11 |
Xinjiang | 7 | 0 | 23.333 | No | 9 | 56.604 | 10 | 0.190 | 19 |
Shaanxi | 7 | 1 | 23.333 | No | 9 | 56.604 | 10 | 0.106 | 21 |
Jiangxi | 7 | 6 | 23.333 | No | 9 | 56.604 | 10 | 0.256 | 17 |
Hunan | 7 | 3 | 23.333 | No | 9 | 56.604 | 10 | 0.235 | 18 |
Hubei | 7 | 3 | 23.333 | No | 9 | 56.604 | 10 | 0.461 | 10 |
Heilongjiang | 7 | 1 | 23.333 | No | 9 | 56.604 | 10 | 0.149 | 20 |
Qinghai | 6 | 1 | 20.000 | No | 10 | 55.556 | 11 | 0.077 | 23 |
Jilin | 6 | 1 | 20.000 | No | 10 | 55.556 | 11 | 0.077 | 23 |
Inner Mongolia | 6 | 1 | 20.000 | No | 10 | 55.556 | 11 | 0.089 | 22 |
Shanxi | 5 | 4 | 20.000 | No | 10 | 55.556 | 11 | 0.089 | 22 |
Hebei | 5 | 5 | 20.000 | Balance | 10 | 55.556 | 11 | 0.089 | 22 |
Liaoning | 5 | 0 | 16.667 | No | 11 | 54.545 | 12 | 0.077 | 23 |
Ningxia | 5 | 1 | 16.667 | No | 11 | 54.545 | 12 | 0.050 | 24 |
Average | 6.742 | 6.742 | 35.269 | 62.106 | 2.232 |
Receiving Relations | Send Out Relations | Expected Ratio (%) | Actual Ratio (%) | Characteristics of Blocks | |||
---|---|---|---|---|---|---|---|
Inside | Outside | Inside | Outside | ||||
Block 1 | 4 | 52 | 4 | 14 | 6.667 | 22.222 | Net beneficial block |
Block 2 | 8 | 83 | 8 | 28 | 13.333 | 22.222 | Bidirectional spillover block |
Block 3 | 5 | 26 | 5 | 68 | 36.667 | 6.849 | Broker block |
Block 4 | 1 | 30 | 1 | 81 | 33.333 | 1.219 | Net spillover block |
Density Matrix | Image Matrix | |||||||
---|---|---|---|---|---|---|---|---|
Block 1 | Block 2 | Block 3 | Block 4 | Block 1 | Block 2 | Block 3 | Block 4 | |
Block 1 | 0.667 | 0.133 | 0.278 | 0.061 | 1 | 0 | 1 | 0 |
Block 2 | 0.067 | 0.400 | 0.133 | 0.345 | 0 | 1 | 0 | 1 |
Block 3 | 0.778 | 0.517 | 0.038 | 0.068 | 1 | 1 | 0 | 0 |
Block 4 | 0.697 | 0.909 | 0.061 | 0.009 | 1 | 1 | 0 | 0 |
Province | Population (10,000 persons) | Bed Utilization (%) | Burden of Consultations (per day) | Province | Population (10,000 persons) | Bed Utilization (%) | Burden of Consultations (per day) |
---|---|---|---|---|---|---|---|
Guangdong | 11169 | 84.0 | 10.6 | Heilongjiang | 3789 | 78.9 | 4.7 |
Shandong | 10006 | 83.4 | 5.9 | Shanxi | 3702 | 77.6 | 4.2 |
Henan | 9559 | 88.4 | 6.1 | Guizhou | 3580 | 79.9 | 5.7 |
Sichuan | 8302 | 91.3 | 7.0 | Chongqing | 3075 | 84.1 | 7.2 |
Jiangsu | 8029 | 87.5 | 8.7 | Jilin | 2717 | 77.6 | 5.0 |
Hebei | 7520 | 83.7 | 5.2 | Gansu | 2626 | 81.6 | 6.2 |
Hunan | 6860 | 85.2 | 4.6 | Inner Mongolia | 2529 | 74.7 | 5.1 |
Anhui | 6255 | 86.2 | 6.2 | Xinjiang | 2445 | 85.0 | 5.8 |
Hubei | 5902 | 92.7 | 6.9 | Shanghai | 2418 | 95.4 | 14.8 |
Zhejiang | 5657 | 89.4 | 11.4 | Beijing | 2171 | 82.4 | 9.3 |
Guangxi | 4885 | 87.7 | 7.8 | Tianjin | 1557 | 78.1 | 10.4 |
Yunnan | 4801 | 83.2 | 7.6 | Hainan | 926 | 81.1 | 6.4 |
Jiangxi | 4622 | 85.8 | 5.9 | Ningxia | 682 | 80.8 | 6.8 |
Liaoning | 4369 | 82.0 | 5.3 | Qinghai | 598 | 70.6 | 5.3 |
Fujian | 3911 | 83.1 | 8.6 | Tibet | 337 | 72.1 | 5.9 |
Shaanxi | 3835 | 83.7 | 6.0 |
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Fu, L.; Xu, K.; Liu, F.; Liang, L.; Wang, Z. Regional Disparity and Patients Mobility: Benefits and Spillover Effects of the Spatial Network Structure of the Health Services in China. Int. J. Environ. Res. Public Health 2021, 18, 1096. https://doi.org/10.3390/ijerph18031096
Fu L, Xu K, Liu F, Liang L, Wang Z. Regional Disparity and Patients Mobility: Benefits and Spillover Effects of the Spatial Network Structure of the Health Services in China. International Journal of Environmental Research and Public Health. 2021; 18(3):1096. https://doi.org/10.3390/ijerph18031096
Chicago/Turabian StyleFu, Liping, Kaibo Xu, Feng Liu, Lu Liang, and Zhengmin Wang. 2021. "Regional Disparity and Patients Mobility: Benefits and Spillover Effects of the Spatial Network Structure of the Health Services in China" International Journal of Environmental Research and Public Health 18, no. 3: 1096. https://doi.org/10.3390/ijerph18031096