Journal of Peking University (Health Sciences) ›› 2026, Vol. 58 ›› Issue (3): 472-478. doi: 10.19723/j.issn.1671-167X.2026.03.005

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Spatial accessibility and optimization of pediatric healthcare resources in Beijing

Jiawei ZHANG1,2,3, Zheng ZHU1,2,3, Chao GONG1,2,3, Runzhi HAN1,2,3, Li YANG1,2,3,*()   

  1. 1. Department of Health Policy and Management, School of Public Health, Peking University, Beijing, 100191, China
    2. Beijing Institute for Health Development, Peking University, Beijing, 100191, China
    3. National Health Commission Key Laboratory of Health System Reform and Governance (Peking University), Beijing, 100191, China
  • Received:2026-02-25 Online:2026-06-18 Published:2026-04-10
  • Contact: Li YANG
  • Supported by:
    Capital Health Research and Development of Special Fund(2026-2R-4402); the National Natural Science Foundation of China(72174010)

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Abstract:

Objective: To assess the spatial accessibility of pediatric healthcare resources in Beijing and to develop an optimization model for resource allocation under a fixed additional resource constraint, with the aim of exploring optimal allocation strategies for 2025 and 2030. Methods: Using communities as the unit of analysis, this study integrated data on Beijing ' s child population in 2020 and pediatric healthcare resources in 2022. An improved two-step floating catchment area (2SFCA) method was applied to measure spatial accessibility. Based on projected child population data for 2025 and 2030, an optimization model was constructed to minimize regional disparities in accessibility. Under the constraint of a fixed total number of additional resources, optimal spatial allocation schemes were derived and compared with a conventional population-based allocation approach. Results: In 2022, Beijing had 4 704 pediatric beds and 4 011 pediatric physicians. The mean spatial accessibility for pediatric beds and pediatric physicians was 1.17 and 0.97, respectively, with a standard deviation of 2.78 for bed accessibility, exhibiting a clear spatial pattern of higher accessibility in central districts and lower accessibility in suburban districts. In the same year, the number of pediatric physicians per 1 000 children in Beijing reached 1.52, already exceeding the targets for 2025 and 2030; therefore, no additional increase in total physician numbers was required. Under the 2025 optimization scenario, the mean accessibility of pediatric beds increased to 1.68, with the standard deviation declining to 2.45, indicating a reduction in regional disparities. Under the 2030 scenario, the mean accessibility further increased to 2. 31, with a standard deviation of 2.56, reflecting continued improvement in accessibility. The optimization model identified Daxing District, Tongzhou District, and Mentougou District as priority districts for additional bed allocation, whereas the conventional population-based approach allocated more resources to Daxing District, Haidian District, and Tongzhou District. While the two approaches showed general consistency in overall spatial allocation, the optimization model more effectively addressed inter-district disparities in accessibility. Conclusion: Significant spatial disparities were identified in the distribution of pediatric healthcare resources in Beijing. The accessibility-oriented optimization approach, under a fixed resource constraint, improved the alignment between supply and demand and reduced regional inequities. It served as a useful complement to conventional population-based allocation methods and provided quantitative evidence to support refined planning and dynamic adjustment of pediatric healthcare resources. Given that the total number of pediatric physicians has already met national targets, leveraging integrated medical consortium and multi-site practice policies to promote the mobility of qualified pediatric physicians toward underserved areas represents a promising pathway toward structural optimization of spatial resource distribution.

Key words: Pediatric resources, Spatial accessibility, Resource allocation, Optimization

CLC Number: 

  • R197.1

Table 1

Description of parameters used in the spatial accessibility model"

Parameters Definition Indicator
Ai Spatial accessibility of residential location i Number of beds per 1 000 children; Number of physicians per 1 000 children
Sj Service capacity of healthcare institution j Pediatric resources of each healthcare institution
Dk Number of population at residential location k Child population at location k
dij Impedance between residential location i and healthcare institution j Travel time by driving
d0 Travel time threshold 120 minutes for bed accessibility; 60 minutes for physician accessibility
n Number of destination points Number of healthcare institutions
m Number of origin points Number of population locations
β Distance decay coefficient 1.5

Figure 1

Spatial accessibility of pediatric (assistant) physicians in Beijing"

Figure 2

Spatial accessibility of pediatric beds in Beijing"

Table 2

Comparison of additional pediatric beds required in 2025 and 2030 predicted by two methods"

District A B B-A ABP
2025 2030 2025 2030 2025 2030
Changping 568 651 723 83 155 9 2
Chaoyang 852 984 927 132 75 153 94
Daxing 225 631 676 406 451 245 207
Dongcheng 245 219 158 / / 57 45
Fangshan 321 436 434 115 113 41 37
Fengtai 219 539 477 320 258 29 20
Haidian 470 847 690 377 220 44 30
Huairou 105 122 104 17 / 138 124
Mentougou 68 112 103 44 35 203 237
Miyun 92 160 159 68 67 198 139
Pinggu 78 153 150 75 72 172 112
Shijingshan 85 163 171 78 86 54 50
Shunyi 281 400 379 119 98 6 2
Tongzhou 214 572 620 358 406 203 186
Xicheng 830 352 256 / / 46 30
Yanqing 51 98 99 47 48 137 107
Total 4 704 6 439 6 126 1 735 1 422 1 735 1 422

Figure 3

Spatial accessibility of pediatric beds in 2025 after optimization"

Figure 4

Spatial accessibility of pediatric beds in 2030 after optimization"

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