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Influence of socio-ecological factors on COVID-19 risk: a cross-sectional study based on 178 countries/regions worldwide

BACKGROUND: The initial outbreak of COVID-19 caused by SARS-CoV-2 in China in 2019 has been severely tested in other countries worldwide. We aimed to describe the spatial distribution of the COVID-19 pandemic worldwide and assess the effects of various socio-ecological factors on COVID-19 risk. METH...

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Autores principales: Su, Dai, Chen, Yingchun, He, Kevin, Zhang, Tao, Tan, Min, Zhang, Yunfan, Zhang, Xingyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7276015/
https://www.ncbi.nlm.nih.gov/pubmed/32511588
http://dx.doi.org/10.1101/2020.04.23.20077545
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author Su, Dai
Chen, Yingchun
He, Kevin
Zhang, Tao
Tan, Min
Zhang, Yunfan
Zhang, Xingyu
author_facet Su, Dai
Chen, Yingchun
He, Kevin
Zhang, Tao
Tan, Min
Zhang, Yunfan
Zhang, Xingyu
author_sort Su, Dai
collection PubMed
description BACKGROUND: The initial outbreak of COVID-19 caused by SARS-CoV-2 in China in 2019 has been severely tested in other countries worldwide. We aimed to describe the spatial distribution of the COVID-19 pandemic worldwide and assess the effects of various socio-ecological factors on COVID-19 risk. METHODS: We collected COVID-19 pandemic infection data and social-ecological data of 178 countries/regions worldwide from three database. We used spatial econometrics method to assess the global and local correlation of COVID-19 risk indicators for COVID-19. To estimate the adjusted incidence rate ratio (IRR), we modelled negative binomial regression analysis with spatial information and socio-ecological factors. FINDINGS: The study indicated that 37, 29 and 39 countries/regions were strongly opposite from the IR, CMR and DCI index "spatial autocorrelation hypothesis", respectively. The IRs were significantly positively associated with GDP per capita, the use of at least basic sanitation services and social insurance program coverage, and were significantly negatively associated with the proportion of the population spending more than 25% of household consumption or income on out-of-pocket health care expenses and the poverty headcount ratio at the national poverty lines. The CMR was significantly positively associated with urban populations, GDP per capita and current health expenditure, and was significantly negatively associated with the number of hospital beds, number of nurses and midwives, and poverty headcount ratio at the national poverty lines. The DCI was significantly positively associated with urban populations, population density and researchers in R&D, and was significantly negatively associated with the number of hospital beds, number of nurses and midwives and poverty headcount ratio at the national poverty lines. We also found that climatic factors were not significantly associated with COVID-19 risk. CONCLUSION: Countries/regions should pay more attention to controlling population flow, improving diagnosis and treatment capacity, and improving public welfare policies.
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spelling pubmed-72760152020-06-07 Influence of socio-ecological factors on COVID-19 risk: a cross-sectional study based on 178 countries/regions worldwide Su, Dai Chen, Yingchun He, Kevin Zhang, Tao Tan, Min Zhang, Yunfan Zhang, Xingyu medRxiv Article BACKGROUND: The initial outbreak of COVID-19 caused by SARS-CoV-2 in China in 2019 has been severely tested in other countries worldwide. We aimed to describe the spatial distribution of the COVID-19 pandemic worldwide and assess the effects of various socio-ecological factors on COVID-19 risk. METHODS: We collected COVID-19 pandemic infection data and social-ecological data of 178 countries/regions worldwide from three database. We used spatial econometrics method to assess the global and local correlation of COVID-19 risk indicators for COVID-19. To estimate the adjusted incidence rate ratio (IRR), we modelled negative binomial regression analysis with spatial information and socio-ecological factors. FINDINGS: The study indicated that 37, 29 and 39 countries/regions were strongly opposite from the IR, CMR and DCI index "spatial autocorrelation hypothesis", respectively. The IRs were significantly positively associated with GDP per capita, the use of at least basic sanitation services and social insurance program coverage, and were significantly negatively associated with the proportion of the population spending more than 25% of household consumption or income on out-of-pocket health care expenses and the poverty headcount ratio at the national poverty lines. The CMR was significantly positively associated with urban populations, GDP per capita and current health expenditure, and was significantly negatively associated with the number of hospital beds, number of nurses and midwives, and poverty headcount ratio at the national poverty lines. The DCI was significantly positively associated with urban populations, population density and researchers in R&D, and was significantly negatively associated with the number of hospital beds, number of nurses and midwives and poverty headcount ratio at the national poverty lines. We also found that climatic factors were not significantly associated with COVID-19 risk. CONCLUSION: Countries/regions should pay more attention to controlling population flow, improving diagnosis and treatment capacity, and improving public welfare policies. Cold Spring Harbor Laboratory 2020-05-04 /pmc/articles/PMC7276015/ /pubmed/32511588 http://dx.doi.org/10.1101/2020.04.23.20077545 Text en http://creativecommons.org/licenses/by-nc-nd/4.0/It is made available under a CC-BY-NC-ND 4.0 International license (http://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Article
Su, Dai
Chen, Yingchun
He, Kevin
Zhang, Tao
Tan, Min
Zhang, Yunfan
Zhang, Xingyu
Influence of socio-ecological factors on COVID-19 risk: a cross-sectional study based on 178 countries/regions worldwide
title Influence of socio-ecological factors on COVID-19 risk: a cross-sectional study based on 178 countries/regions worldwide
title_full Influence of socio-ecological factors on COVID-19 risk: a cross-sectional study based on 178 countries/regions worldwide
title_fullStr Influence of socio-ecological factors on COVID-19 risk: a cross-sectional study based on 178 countries/regions worldwide
title_full_unstemmed Influence of socio-ecological factors on COVID-19 risk: a cross-sectional study based on 178 countries/regions worldwide
title_short Influence of socio-ecological factors on COVID-19 risk: a cross-sectional study based on 178 countries/regions worldwide
title_sort influence of socio-ecological factors on covid-19 risk: a cross-sectional study based on 178 countries/regions worldwide
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7276015/
https://www.ncbi.nlm.nih.gov/pubmed/32511588
http://dx.doi.org/10.1101/2020.04.23.20077545
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