| 摘要: |
| 以医疗服务为代表的高水平公
共服务设施共建共享是都市圈一体化发
展的核心目标，既有医疗资源配置研究
缺乏面向区域共享的特征解读与影响机
制研究。以武汉都市圈为例，通过手机
信令数据识别跨城就医出行特征，结合
建成环境数据，采用地理加权回归模型
分析其影响机制。研究发现：跨城就医
出行以跨区县为主，市域级医院是当前
满足居民优质医疗服务需求的首
选；“武鄂黄黄”都市圈核心区城市的
跨市就医出行联系紧密，孝感、咸宁等
外围城市也存在较大的 “向心”就医
需求；人口密度、老年人占比、距最近
地市中心的距离、距最近火车站的距离
等因素对跨域就医出行总量有显著影
响，但影响程度和方向在不同城镇之间
存在差异。基于此，提出加强对地级市
医疗服务水平的建设，提高乡村地区以
及老龄化程度较高值区域的就医可达
性，以及提高区域性交通设施与区域性
医疗设施的衔接等规划建议。 |
| 关键词: 跨城就医 建成环境 出行特
征 空间异质性 MGWR模型 武汉
都市圈 |
| DOI：10.13791/j.cnki.hsfwest.20240723002 |
| 分类号: |
| 基金项目:国家自然科学基金项目（52178039）；中央高校基本科研业务费项目（2021WKZDJC014） |
|
| Analysis of the characteristics and impact mechanism of cross-city medical travel inWuhan Metropolitan Area |
|
PENG Yuqing,GUO Liang,LI Haidong
|
| Abstract: |
| The hierarchical allocation and spatial organization of medical resources in metropolitan regions
have emerged as critical challenges for regional integration, particularly in addressing the disparities
between administrative boundaries and actual healthcare-seeking patterns. This study employs an
innovative methodology integrating mobile phone signaling data with hospital service hierarchy analysis to
investigate cross-boundary medical travel within the Wuhan Metropolitan Area (WMA). Through
systematic classification of 505 medical institutions into four tiers based on service coverage (provinciallevel:
2; metropolitan-level: 13; municipal-level: 198; county-level: 292), combined with analysis of 943,
872 cross-city medical trips in 2020, the research reveals fundamental characteristics of inter-jurisdictional
healthcare mobility. Hospital service hierarchy analysis demonstrates significant spatial disparities in
resource distribution. Provincial-level hospitals (Tongji Hospital and Union Hospital affiliated with
Huazhong University of Science and Technology) serve 80% of townships but account for only 3.12% of
total service capacity, with 22% of their patients originating from outside Wuhan. Municipal-level hospitals
show uneven distribution, concentrated in Wuhan (133 of 198), while Xianning possesses only 2 such
facilities despite its demographic significance. County-level institutions, though numerically
dominant (292), handle merely 26.24% of total demand, with 96% of townships generating less than 0.1%
demand for these facilities. Cross-city medical flows exhibit distinct spatial patterns. Wuhan dominates as
the primary destination, receiving 42.04% of total cross-city trips, particularly from Ezhou (25.23% of
Ezhou's outflow) and Xiaogan. Ezhou demonstrates the highest cross-city medical dependency (41.2%
outflow rate), predominantly directed to Huanggang, Huangshi, and Wuhan. Conversely, Xianning’ limited
municipal-level hospital resources correlate with substantial centripetal flows to Wuhan (89.9% of
Xianning’s total outflow). The “Wuhan-Ezhou-Huanggang-Huangshi” corridor accounts for 63.7% of total
metropolitan-level medical interactions, confirming core-periphery dynamics in healthcare accessibility.
Transportation mode analysis reveals road dominance (90% of trips), with peripheral townships showing
higher road dependency (355 townships >95% road usage). Rail transportation proves marginal (maximum
53% in 25 townships), concentrated along Wuhan-Xiaogan (10.2% rail share) and Wuhan-Ezhou-Huangshi
corridors (8.7%), while other rail-connected corridors like Wuhan-Tianmen-Qianjiang show minimal
medical utilization (2.1%). Spatial disparities in travel metrics emerge starkly: urban core townships
average 20km/30min trips versus peripheral areas exceeding 100km/120min, with 26 edge townships
enduring >60km journeys. Geographically Weighted Regression (GWR) analysis uncovers multi-scale
influencing mechanisms. The model for total cross-regional medical trips achieves high explanatory
power (R2 =0.92), identifying significant negative correlations with population density (β =-1.7487) and
distance to municipal centers (β=-0.1696). This confirms that sparsely populated areas distant from urban
cores generate higher cross-boundary demand. Travel duration analysis (R2 =0.72) demonstrates positive
associations with distances to municipal (β=0.1885) and county centers (β=0.4314), revealing compounded
accessibility challenges for rural populations. Contrary to expectations, transportation infrastructure
metrics (rail station proximity, trunk road density) show insignificant impacts on travel time. The study
identifies three critical mismatches: 1) Spatial concentration of high-tier hospitals in Wuhan createsimbalanced demand pressures, with provincial-level facilities absorbing 22% non-local patients; 2) Administrative boundary proximity intensifies cross-city
flows, as 317 border townships exhibit >90% municipal-level hospital dependency; 3) Transportation infrastructure development fails to alleviate rural
accessibility gaps, evidenced by persistent long-duration trips in 26 peripheral townships despite rail availability. Methodological limitations stem from data
constraints, the absence of individual socioeconomic attributes restricts analysis of hospital selection mechanisms, while service capacity metrics lack clinical
outcome dimensions. Future research should integrate electronic medical records with mobility data to capture decision-making processes and service quality
influences. These findings necessitate strategic interventions: strengthening municipal-level hospital capabilities in underserved regions like Xianning;
optimizing referral networks along high-flow corridors; and implementing targeted transportation-medical integration in peripheral zones with travel durations
exceeding 60 minutes. The research demonstrates that metropolitan medical integration requires transcending administrative divisions through evidence-based
resource allocation aligned with actual patient mobility patterns, providing a replicable framework for regional healthcare planning in developing contexts. |
| Key words: cross-city medical travel built Environment travel characteristics spatial heterogeneity MGWR Model Wuhan Metropolitan Area |
