Evidence map›Paper›PMID 41822925›Full record

ArticleFrontiers in public health2026

Informing equitable urban health policy: a multi-source geospatial assessment of spatial mismatch in medical facility distribution across Chinese cities.

Zeyu Zhang, Zhengchen Guo, Bingshuai Li, Lin Song

Abstract read
In one paragraph

Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Zeyu ZhangSchool of Philosophy and Social Development, Shandong University, Jinan, China.
Zhengchen GuoLaw School, Shandong University of Technology, Zibo, China.
Bingshuai LiTianjin Research Institute for Water Transport Engineering, M.O.T., Tianjin, China.
Lin SongDepartment of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Equitable distribution of medical facilities is a foundational element of urban health policy, particularly in rapidly urbanizing settings where spatial mismatches between healthcare supply and population demand can exacerbate health inequities. In China, despite national efforts to strengthen primary healthcare, the planning and distribution of medical facilities remain uneven, raising concerns about fairness, efficiency, and social justice in public service provision. Methods: We conducted a multi-city geospatial assessment across four major cities in Shandong Province (Jinan, Qingdao, Yantai, and Weihai) using an integrated framework that combines healthcare Points of Interest (POIs), 100-meter resolution census-based population grids, OpenStreetMap road networks, and official land use records. To evaluate spatial equity, we applied the Gini coefficient, global and local indicators of spatial autocorrelation (Moran's I and LISA), and geographically weighted regression (GWR) to identify disparities and context-specific drivers of medical facility distribution. Results: Our analysis reveals significant over-concentration of medical resources in central urban districts, while peripheral and county-level areas face systemic under-provision. Gini coefficients ranged from 0.59 to 0.73 indicating high levels of intra-urban inequity. GWR results further show that in core areas, facility location aligns with population density and economic activity, whereas in outlying regions, inadequate transport infrastructure and inflexible land-use regulations constrain equitable access. Notably, Qixia, Liuhe, and Rongcheng emerged as critical underserved zones requiring targeted policy intervention. Conclusion: This study provides actionable, spatially explicit evidence for urban health policymakers seeking to advance equity in medical resource allocation. By linking fine-grained geospatial analytics with principles of spatial justice, our findings support the redesign of medical facility planning guidelines, the integration of accessibility metrics into smart city governance, and the prioritization of underserved areas in future health infrastructure investment. The methodological approach offers a scalable model for evidence-informed public health policy in other emerging urban contexts.

Indexed as

Healthcare DisparitiesHealth FacilitiesHealth PolicyHealth Services AccessibilityUrban HealthChinaCitiesHumansSpatial Analysisevidence-informed policygeographically weighted regression (GWR)healthcare accessibilityhealth equitymedical facility distributionpublic health policyspatial mismatchurban planning

Identifiers

PMID41822925
PMCPMC12975932

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.