Cargando…

Spatial effects of public health laboratory emergency testing institutions under COVID-19 in China

BACKGROUND: The transmission of 2019 novel coronavirus (COVID-19) has caused global panic in the past three years. Countries have learned an important lesson in the practice of responding to COVID-19 pandemic: timely and accurate diagnosis is critical. As an important technology of virus diagnosis,...

Descripción completa

Detalles Bibliográficos
Autores principales: Shi, Baoguo, Wang, Yanjie, Bai, Xiaodan, Lai, Yongqiang, Xiang, Wenjing, Wu, Bing, Xia, Qi, Liu, Xinwei, Li, Ye
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10184634/
https://www.ncbi.nlm.nih.gov/pubmed/37189135
http://dx.doi.org/10.1186/s12939-023-01871-0
_version_ 1785042184898084864
author Shi, Baoguo
Wang, Yanjie
Bai, Xiaodan
Lai, Yongqiang
Xiang, Wenjing
Wu, Bing
Xia, Qi
Liu, Xinwei
Li, Ye
author_facet Shi, Baoguo
Wang, Yanjie
Bai, Xiaodan
Lai, Yongqiang
Xiang, Wenjing
Wu, Bing
Xia, Qi
Liu, Xinwei
Li, Ye
author_sort Shi, Baoguo
collection PubMed
description BACKGROUND: The transmission of 2019 novel coronavirus (COVID-19) has caused global panic in the past three years. Countries have learned an important lesson in the practice of responding to COVID-19 pandemic: timely and accurate diagnosis is critical. As an important technology of virus diagnosis, nucleic acid testing (NAT) is also widely used in the identification of other infectious diseases. However, geographic factors often constrain the provision of public health services such as NAT services, and the spatial nature of their resource allocation is a significant problem. METHODS: We used OLS, OLS-SAR, GWR, GWR-SAR, MGWR, and MGWR-SAR models to identify the determinants of spatial difference and spatial heterogeneity affecting NAT institutions in China. RESULTS: Firstly, we identify that the distribution of NAT institutions in China shows a clear spatial agglomeration, with an overall trend of increasing distribution from west to east. There is significant spatial heterogeneity in Chinese NAT institutions. Secondly, the MGWR-SAR model results show that city level, population density, number of tertiary hospitals and number of public health emergency outbreaks are important factors influencing the spatial heterogeneity of NAT institutions in China. CONCLUSIONS: Therefore, the government should allocate health resources rationally, optimise the spatial layout of testing facilities, and improve the ability to respond to public health emergencies. Meanwhile, third-party testing facilities need to focus on their role in the public health emergency response system as a market force to alleviate the inequitable allocation of health resources between regions. By taking these measures to prepare adequately for possible future public health emergencies.
format Online
Article
Text
id pubmed-10184634
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-101846342023-05-16 Spatial effects of public health laboratory emergency testing institutions under COVID-19 in China Shi, Baoguo Wang, Yanjie Bai, Xiaodan Lai, Yongqiang Xiang, Wenjing Wu, Bing Xia, Qi Liu, Xinwei Li, Ye Int J Equity Health Research BACKGROUND: The transmission of 2019 novel coronavirus (COVID-19) has caused global panic in the past three years. Countries have learned an important lesson in the practice of responding to COVID-19 pandemic: timely and accurate diagnosis is critical. As an important technology of virus diagnosis, nucleic acid testing (NAT) is also widely used in the identification of other infectious diseases. However, geographic factors often constrain the provision of public health services such as NAT services, and the spatial nature of their resource allocation is a significant problem. METHODS: We used OLS, OLS-SAR, GWR, GWR-SAR, MGWR, and MGWR-SAR models to identify the determinants of spatial difference and spatial heterogeneity affecting NAT institutions in China. RESULTS: Firstly, we identify that the distribution of NAT institutions in China shows a clear spatial agglomeration, with an overall trend of increasing distribution from west to east. There is significant spatial heterogeneity in Chinese NAT institutions. Secondly, the MGWR-SAR model results show that city level, population density, number of tertiary hospitals and number of public health emergency outbreaks are important factors influencing the spatial heterogeneity of NAT institutions in China. CONCLUSIONS: Therefore, the government should allocate health resources rationally, optimise the spatial layout of testing facilities, and improve the ability to respond to public health emergencies. Meanwhile, third-party testing facilities need to focus on their role in the public health emergency response system as a market force to alleviate the inequitable allocation of health resources between regions. By taking these measures to prepare adequately for possible future public health emergencies. BioMed Central 2023-05-15 /pmc/articles/PMC10184634/ /pubmed/37189135 http://dx.doi.org/10.1186/s12939-023-01871-0 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
Shi, Baoguo
Wang, Yanjie
Bai, Xiaodan
Lai, Yongqiang
Xiang, Wenjing
Wu, Bing
Xia, Qi
Liu, Xinwei
Li, Ye
Spatial effects of public health laboratory emergency testing institutions under COVID-19 in China
title Spatial effects of public health laboratory emergency testing institutions under COVID-19 in China
title_full Spatial effects of public health laboratory emergency testing institutions under COVID-19 in China
title_fullStr Spatial effects of public health laboratory emergency testing institutions under COVID-19 in China
title_full_unstemmed Spatial effects of public health laboratory emergency testing institutions under COVID-19 in China
title_short Spatial effects of public health laboratory emergency testing institutions under COVID-19 in China
title_sort spatial effects of public health laboratory emergency testing institutions under covid-19 in china
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10184634/
https://www.ncbi.nlm.nih.gov/pubmed/37189135
http://dx.doi.org/10.1186/s12939-023-01871-0
work_keys_str_mv AT shibaoguo spatialeffectsofpublichealthlaboratoryemergencytestinginstitutionsundercovid19inchina
AT wangyanjie spatialeffectsofpublichealthlaboratoryemergencytestinginstitutionsundercovid19inchina
AT baixiaodan spatialeffectsofpublichealthlaboratoryemergencytestinginstitutionsundercovid19inchina
AT laiyongqiang spatialeffectsofpublichealthlaboratoryemergencytestinginstitutionsundercovid19inchina
AT xiangwenjing spatialeffectsofpublichealthlaboratoryemergencytestinginstitutionsundercovid19inchina
AT wubing spatialeffectsofpublichealthlaboratoryemergencytestinginstitutionsundercovid19inchina
AT xiaqi spatialeffectsofpublichealthlaboratoryemergencytestinginstitutionsundercovid19inchina
AT liuxinwei spatialeffectsofpublichealthlaboratoryemergencytestinginstitutionsundercovid19inchina
AT liye spatialeffectsofpublichealthlaboratoryemergencytestinginstitutionsundercovid19inchina