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Analysing the global and local spatial associations of medical resources across Wuhan city using POI data
BACKGROUND: There is a sharp contradiction between the supply and demand of medical resources in the provincial capitals of China. Understanding the spatial patterns of medical resources and identifying their spatial association and heterogeneity is a prerequisite to ensuring that limited resources...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9883876/ https://www.ncbi.nlm.nih.gov/pubmed/36709274 http://dx.doi.org/10.1186/s12913-023-09051-0 |
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author | Chen, Qiao Cheng, Jianquan Tu, Jianguang |
author_facet | Chen, Qiao Cheng, Jianquan Tu, Jianguang |
author_sort | Chen, Qiao |
collection | PubMed |
description | BACKGROUND: There is a sharp contradiction between the supply and demand of medical resources in the provincial capitals of China. Understanding the spatial patterns of medical resources and identifying their spatial association and heterogeneity is a prerequisite to ensuring that limited resources are allocated fairly and optimally, which, along with improvements to urban residents’ quality of life, is a key aim of healthy city planning. However, the existing studies on medical resources pattern mainly focus on their spatial distribution and evolution characteristics, and lack the analyses of the spatial co-location between medical resources from the global and local perspectives. It is worth noting that the research on the spatial relationship between medical resources is an important way to realize the spatial equity and operation efficiency of urban medical resources. METHODS: Localized colocation quotient (LCLQ) analysis has been used successfully to measure directional spatial associations and heterogeneity between categorical point data. Using point of interest (POI) data and the LCLQ method, this paper presents the first analysis of spatial patterns and directional spatial associations between six medical resources across Wuhan city. RESULTS: (1) Pharmacies, clinics and community hospitals show “multicentre + multicircle”, “centre + axis + dot” and “banded” distribution characteristics, respectively, but specialized hospitals and general hospitals present “single core” and “double core” modes. (2) Overall, medical resources show agglomeration characteristics. The degrees of spatial agglomeration of the five medical resources, are ranked from high to low as follows: pharmacy, clinic, community hospital, special hospital, general hospital and 3A hospital. (3) Although pharmacies, clinics, and community hospitals of basic medical resources are interdependent, specialized hospitals, general hospitals and 3A hospitals of professional medical resources are also interdependent; furthermore, basic medical resources and professional medical resources are mutually exclusive. CONCLUSIONS: Government and urban planners should pay great attention to the spatial distribution characteristics and association intensity of medical resources when formulating relevant policies. The findings of this study contribute to health equity and health policy discussions around basic medical services and professional medical services. |
format | Online Article Text |
id | pubmed-9883876 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-98838762023-01-29 Analysing the global and local spatial associations of medical resources across Wuhan city using POI data Chen, Qiao Cheng, Jianquan Tu, Jianguang BMC Health Serv Res Research BACKGROUND: There is a sharp contradiction between the supply and demand of medical resources in the provincial capitals of China. Understanding the spatial patterns of medical resources and identifying their spatial association and heterogeneity is a prerequisite to ensuring that limited resources are allocated fairly and optimally, which, along with improvements to urban residents’ quality of life, is a key aim of healthy city planning. However, the existing studies on medical resources pattern mainly focus on their spatial distribution and evolution characteristics, and lack the analyses of the spatial co-location between medical resources from the global and local perspectives. It is worth noting that the research on the spatial relationship between medical resources is an important way to realize the spatial equity and operation efficiency of urban medical resources. METHODS: Localized colocation quotient (LCLQ) analysis has been used successfully to measure directional spatial associations and heterogeneity between categorical point data. Using point of interest (POI) data and the LCLQ method, this paper presents the first analysis of spatial patterns and directional spatial associations between six medical resources across Wuhan city. RESULTS: (1) Pharmacies, clinics and community hospitals show “multicentre + multicircle”, “centre + axis + dot” and “banded” distribution characteristics, respectively, but specialized hospitals and general hospitals present “single core” and “double core” modes. (2) Overall, medical resources show agglomeration characteristics. The degrees of spatial agglomeration of the five medical resources, are ranked from high to low as follows: pharmacy, clinic, community hospital, special hospital, general hospital and 3A hospital. (3) Although pharmacies, clinics, and community hospitals of basic medical resources are interdependent, specialized hospitals, general hospitals and 3A hospitals of professional medical resources are also interdependent; furthermore, basic medical resources and professional medical resources are mutually exclusive. CONCLUSIONS: Government and urban planners should pay great attention to the spatial distribution characteristics and association intensity of medical resources when formulating relevant policies. The findings of this study contribute to health equity and health policy discussions around basic medical services and professional medical services. BioMed Central 2023-01-28 /pmc/articles/PMC9883876/ /pubmed/36709274 http://dx.doi.org/10.1186/s12913-023-09051-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Chen, Qiao Cheng, Jianquan Tu, Jianguang Analysing the global and local spatial associations of medical resources across Wuhan city using POI data |
title | Analysing the global and local spatial associations of medical resources across Wuhan city using POI data |
title_full | Analysing the global and local spatial associations of medical resources across Wuhan city using POI data |
title_fullStr | Analysing the global and local spatial associations of medical resources across Wuhan city using POI data |
title_full_unstemmed | Analysing the global and local spatial associations of medical resources across Wuhan city using POI data |
title_short | Analysing the global and local spatial associations of medical resources across Wuhan city using POI data |
title_sort | analysing the global and local spatial associations of medical resources across wuhan city using poi data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9883876/ https://www.ncbi.nlm.nih.gov/pubmed/36709274 http://dx.doi.org/10.1186/s12913-023-09051-0 |
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