北京市儿科医疗资源空间可及性与优化

Spatial accessibility and optimization of pediatric healthcare resources in Beijing

摘要

目的：评估北京市儿科医疗资源空间可及性，并构建儿科资源优化模型，探索2025年与2030年儿科医疗资源的优化方案。方法：基于北京市2020年儿童人口数据及2022年儿科执业（助理）医师、床位数据，采用改进的两步移动搜寻法测算儿科医疗资源空间可及性。在此基础上，结合2025年与2030年儿童人口预测结果，构建以最小化可及性差异为目标的优化模型，在新增资源总量固定的情况下求解床位分配方案，并与基于人口规模分配的传统方案进行比较。结果：2022 年北京市共配置儿科床位 4704 张、儿科执业（助理）医师 4011 名，儿科床位与儿科执业（助理）医师空间可及性平均值分别为1.17和0.97，床位可及性标准差为2.78，可及性呈现中心城区高、外围地区低的空间格局。2022年北京市每千名儿童儿科执业（助理）医师数达到1.52人，已超过2025年及2030年国家目标，在总量层面不涉及新增。按照2025年目标优化儿科床位后，床位空间可及性平均值提升至1.68，标准差 2.45 ，地区间差异程度明显下降；按照2030年目标优化后，可及性平均值进一步提升至2.31，标准差为2.56，可及性水平持续改善。优化模型显示，大兴区、通州区与门头沟区为新增床位重点配置地区；传统按人口规模分配则将新增资源主要投向大兴区、海淀区和通州区。两种方案在总体布局方向上具有一致性，优化模型可更有效地缓解地区间可及性的不均衡。结论：北京市儿科医疗资源存在空间分布不均衡现象。基于可及性差异最小化的优化方案在资源总量固定条件下，有助于改善空间可及性并缓解地区间差异，可作为传统按人口规模配置资源的有效补充，将可及性指标纳入资源配置过程，为儿科医疗资源精细化规划与动态调整提供量化依据。在医师总量已达标的背景下，应充分发挥医联体协同机制与多点执业政策，推动优质儿科医师资源向可及性不足地区流动与下沉，以实现空间配置的结构性优化。 [关键词]儿科资源；空间可及性；医疗资源配置；布局优化

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 ap- plied 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 com- pared with a conventional population-based allocation approach. Results：In 2022，Beijing had 4704 pe- diatric beds and 4011 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 accessi- bility，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 1000 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 evi- dence 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 consorti- um and multi-site practice policies to promote the mobility of qualified pediatric physicians toward under- served areas represents a promising pathway toward structural optimization of spatial resource distribution.

Key words

Pediatric resources / Spatial accessibility / Resource allocation / Optimization

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基金资助

首都卫生发展科研专项项目(2026-2R-4402)

国家自然科学基金(72174010)