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Impact of demographic, environmental, socioeconomic, and government intervention on the spreading of COVID-19
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is a worldwide epidemiological emergency, and the risk factors for the multiple waves with new COVID-19 strains are concerning. This study aims to identify the most significant risk factors for spreading COVID-19 to help policymakers take...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Author(s). Published by Elsevier B.V. on behalf of INDIACLEN.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236127/ https://www.ncbi.nlm.nih.gov/pubmed/34222717 http://dx.doi.org/10.1016/j.cegh.2021.100811 |
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author | Mashrur, Fazla Rabbi Roy, Amit Dutta Chhoan, Anisha Parsub Sarker, Sumit Saha, Anamika Hasan, S.M. Naimul Saha, Shumit |
author_facet | Mashrur, Fazla Rabbi Roy, Amit Dutta Chhoan, Anisha Parsub Sarker, Sumit Saha, Anamika Hasan, S.M. Naimul Saha, Shumit |
author_sort | Mashrur, Fazla Rabbi |
collection | PubMed |
description | BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is a worldwide epidemiological emergency, and the risk factors for the multiple waves with new COVID-19 strains are concerning. This study aims to identify the most significant risk factors for spreading COVID-19 to help policymakers take early measures for the next waves. METHODS: We conducted the study on randomly selected 29 countries where the pandemic had a downward trend in the daily active cases curve as of June 10, 2020. We investigated the association with the standardized spreading index and demographical, environmental, socioeconomic, and government intervention. To standardize the spreading index, we accounted for the number of tests and the timeline bias. Furthermore, we performed multiple linear regression to identify the relative importance of the variables. RESULTS: In the correlation analysis, air pollution, PM(2.5) (r = 0.37, p = 0.0466), number of days to impose lockdown from first case (r = 0.38, p = 0.0424) and total confirmed cases on the first lockdown (r = 0.61, p = 0.0004) were associated with outcome measures. In the adjusted model, air pollution ([Formula: see text] = 4.5, p = 0.0127, |t| = 3.1) and overweight prevalence ([Formula: see text] = 4.7, p = 0.0187, |t| = 2.9) were the most significant exposure variable for spreading of COVID-19. CONCLUSION: Our findings showed that countries with larger PM(2.5) values and comparatively more overweight populations are at higher risk of spreading COVID-19. Proper preventive measures may reduce the spreading. |
format | Online Article Text |
id | pubmed-8236127 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Author(s). Published by Elsevier B.V. on behalf of INDIACLEN. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82361272021-06-28 Impact of demographic, environmental, socioeconomic, and government intervention on the spreading of COVID-19 Mashrur, Fazla Rabbi Roy, Amit Dutta Chhoan, Anisha Parsub Sarker, Sumit Saha, Anamika Hasan, S.M. Naimul Saha, Shumit Clin Epidemiol Glob Health Article BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is a worldwide epidemiological emergency, and the risk factors for the multiple waves with new COVID-19 strains are concerning. This study aims to identify the most significant risk factors for spreading COVID-19 to help policymakers take early measures for the next waves. METHODS: We conducted the study on randomly selected 29 countries where the pandemic had a downward trend in the daily active cases curve as of June 10, 2020. We investigated the association with the standardized spreading index and demographical, environmental, socioeconomic, and government intervention. To standardize the spreading index, we accounted for the number of tests and the timeline bias. Furthermore, we performed multiple linear regression to identify the relative importance of the variables. RESULTS: In the correlation analysis, air pollution, PM(2.5) (r = 0.37, p = 0.0466), number of days to impose lockdown from first case (r = 0.38, p = 0.0424) and total confirmed cases on the first lockdown (r = 0.61, p = 0.0004) were associated with outcome measures. In the adjusted model, air pollution ([Formula: see text] = 4.5, p = 0.0127, |t| = 3.1) and overweight prevalence ([Formula: see text] = 4.7, p = 0.0187, |t| = 2.9) were the most significant exposure variable for spreading of COVID-19. CONCLUSION: Our findings showed that countries with larger PM(2.5) values and comparatively more overweight populations are at higher risk of spreading COVID-19. Proper preventive measures may reduce the spreading. The Author(s). Published by Elsevier B.V. on behalf of INDIACLEN. 2021 2021-06-27 /pmc/articles/PMC8236127/ /pubmed/34222717 http://dx.doi.org/10.1016/j.cegh.2021.100811 Text en © 2021 The Author(s) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Mashrur, Fazla Rabbi Roy, Amit Dutta Chhoan, Anisha Parsub Sarker, Sumit Saha, Anamika Hasan, S.M. Naimul Saha, Shumit Impact of demographic, environmental, socioeconomic, and government intervention on the spreading of COVID-19 |
title | Impact of demographic, environmental, socioeconomic, and government intervention on the spreading of COVID-19 |
title_full | Impact of demographic, environmental, socioeconomic, and government intervention on the spreading of COVID-19 |
title_fullStr | Impact of demographic, environmental, socioeconomic, and government intervention on the spreading of COVID-19 |
title_full_unstemmed | Impact of demographic, environmental, socioeconomic, and government intervention on the spreading of COVID-19 |
title_short | Impact of demographic, environmental, socioeconomic, and government intervention on the spreading of COVID-19 |
title_sort | impact of demographic, environmental, socioeconomic, and government intervention on the spreading of covid-19 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236127/ https://www.ncbi.nlm.nih.gov/pubmed/34222717 http://dx.doi.org/10.1016/j.cegh.2021.100811 |
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