Evidence map›Paper›PMID 40203276›Full record

ArticlePloS one2025

Spatio-temporal dynamics of urban medical system carrying capacity and their obstacle factors: A case study of Yangtze River Delta urban agglomeration.

Chengshuang Sun, Ke Zhou, Guangxia Li, Weina Zhu, Dongjun Wan

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Article in PloS one, 2025. 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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1 · What the graph read from it

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Chengshuang SunSchool of Economics and Management Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China.
Ke ZhouSchool of Economics and Management Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China.
Guangxia LiSchool of Economics and Management Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China.
Weina ZhuSchool of Economics and Management Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China.ORCID https://orcid.org/0000-0002-8723-0460
Dongjun WanSchool of Economics and Management Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The frequent occurrence of various urban disasters poses risks to human survival and welfare, and it is of great significance to evaluate the urban medical system carrying capacity (UMSCC), monitor the spatial and temporal patterns of the UMSCC and identify their obstacle factors to improve the public medical system and rationalize medical resource allocation. In this paper, the Yangtze River Delta urban agglomeration (YRDUA), which includes 27 major cities, is taken as a case study. A UMSCC evaluation model is constructed and the gravity center shift trajectory is analyzed by using the ArcGIS software. The results indicate that the UMSCC of the 27 cities in the YRDUA can be classified into 5 levels: the lowers (0.193-0.335), the lows (0.335-0.425), the mediums (0.425-0.489), the highs (0.489-0.549) and the highers (0.549-0.619). From 2011-2021, the UMSCC level in all the 27 cities from the YRDUA increased annually, and during this period, the gravity center of the UMSCC was concentrated in Xuancheng, and the migration trend was southwest. Moreover, there is a positive correlation between the city type and the level of UMSCC: the larger of the city is, the higher of the UMSCC level is; however, the gap between the levels of UMSCC in different city types in the YRDUA gradually narrows and tends to be consistent, which reflects the development trend of medical system integration in the region. Finally, it is concluded that the key obstacle indicators of the UMSCC in the YRDUA can be attributed to the proportion of medical financial expenditure, the number of beds, the number of registered nurses and the urban digital development level. Correspondingly, the suggestions are proposed.

Identifiers

PMID40203276
PMCPMC11981656

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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.