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Unmasking unexpected health care inequalities in China using urban big data: Service-rich and service-poor communities
Geographic accessibility plays a key role in health care inequality but remains insufficiently investigated in China, primarily due to the lack of accurate, broad-coverage data on supply and demand. In this paper, we employ an innovative approach to local supply-and-demand conditions to (1) reveal t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8830721/ https://www.ncbi.nlm.nih.gov/pubmed/35143557 http://dx.doi.org/10.1371/journal.pone.0263577 |
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author | Zheng, Linzi Zhang, Lu Chen, Ke He, Qingsong |
author_facet | Zheng, Linzi Zhang, Lu Chen, Ke He, Qingsong |
author_sort | Zheng, Linzi |
collection | PubMed |
description | Geographic accessibility plays a key role in health care inequality but remains insufficiently investigated in China, primarily due to the lack of accurate, broad-coverage data on supply and demand. In this paper, we employ an innovative approach to local supply-and-demand conditions to (1) reveal the status quo of the distribution of health care provision and (2) examine whether individual households from communities with different housing prices can acquire equal and adequate quality health care services within and across 361 cities in China. Our findings support previous conclusions that quality hospitals are concentrated in cities with high administrative rankings and developmental levels. However, after accounting for the population size an “accessible” hospital serves, we discern “pro-poor” inequality in accessibility to care (denoted as GAPSD) and that GAPSD decreases along with increases in administrative rankings of cities and in community ratings. This paper is significant for both research and policy-making. Our approach successfully reveals an “unexpected” pattern of health care inequality that has not been reported before, and our findings provide a nationwide, detailed benchmark that facilitates the assessment of health and urban policies, as well as associated policy-making. |
format | Online Article Text |
id | pubmed-8830721 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-88307212022-02-11 Unmasking unexpected health care inequalities in China using urban big data: Service-rich and service-poor communities Zheng, Linzi Zhang, Lu Chen, Ke He, Qingsong PLoS One Research Article Geographic accessibility plays a key role in health care inequality but remains insufficiently investigated in China, primarily due to the lack of accurate, broad-coverage data on supply and demand. In this paper, we employ an innovative approach to local supply-and-demand conditions to (1) reveal the status quo of the distribution of health care provision and (2) examine whether individual households from communities with different housing prices can acquire equal and adequate quality health care services within and across 361 cities in China. Our findings support previous conclusions that quality hospitals are concentrated in cities with high administrative rankings and developmental levels. However, after accounting for the population size an “accessible” hospital serves, we discern “pro-poor” inequality in accessibility to care (denoted as GAPSD) and that GAPSD decreases along with increases in administrative rankings of cities and in community ratings. This paper is significant for both research and policy-making. Our approach successfully reveals an “unexpected” pattern of health care inequality that has not been reported before, and our findings provide a nationwide, detailed benchmark that facilitates the assessment of health and urban policies, as well as associated policy-making. Public Library of Science 2022-02-10 /pmc/articles/PMC8830721/ /pubmed/35143557 http://dx.doi.org/10.1371/journal.pone.0263577 Text en © 2022 Zheng et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Zheng, Linzi Zhang, Lu Chen, Ke He, Qingsong Unmasking unexpected health care inequalities in China using urban big data: Service-rich and service-poor communities |
title | Unmasking unexpected health care inequalities in China using urban big data: Service-rich and service-poor communities |
title_full | Unmasking unexpected health care inequalities in China using urban big data: Service-rich and service-poor communities |
title_fullStr | Unmasking unexpected health care inequalities in China using urban big data: Service-rich and service-poor communities |
title_full_unstemmed | Unmasking unexpected health care inequalities in China using urban big data: Service-rich and service-poor communities |
title_short | Unmasking unexpected health care inequalities in China using urban big data: Service-rich and service-poor communities |
title_sort | unmasking unexpected health care inequalities in china using urban big data: service-rich and service-poor communities |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8830721/ https://www.ncbi.nlm.nih.gov/pubmed/35143557 http://dx.doi.org/10.1371/journal.pone.0263577 |
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