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Coronavirus Disease 2019 (COVID-19): A Modeling Study of Factors Driving Variation in Case Fatality Rate by Country
Background: The novel Severe Acute Respiratory Syndrome Coronavirus-2 has led to a global pandemic in which case fatality rate (CFR) has varied from country to country. This study aims to identify factors that may explain the variation in CFR across countries. Methods: We identified 24 potential ris...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7664233/ https://www.ncbi.nlm.nih.gov/pubmed/33167564 http://dx.doi.org/10.3390/ijerph17218189 |
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author | Pan, Jennifer St. Pierre, Joseph Marie Pickering, Trevor A. Demirjian, Natalie L. Fields, Brandon K.K. Desai, Bhushan Gholamrezanezhad, Ali |
author_facet | Pan, Jennifer St. Pierre, Joseph Marie Pickering, Trevor A. Demirjian, Natalie L. Fields, Brandon K.K. Desai, Bhushan Gholamrezanezhad, Ali |
author_sort | Pan, Jennifer |
collection | PubMed |
description | Background: The novel Severe Acute Respiratory Syndrome Coronavirus-2 has led to a global pandemic in which case fatality rate (CFR) has varied from country to country. This study aims to identify factors that may explain the variation in CFR across countries. Methods: We identified 24 potential risk factors affecting CFR. For all countries with over 5000 reported COVID-19 cases, we used country-specific datasets from the WHO, the OECD, and the United Nations to quantify each of these factors. We examined univariable relationships of each variable with CFR, as well as correlations among predictors and potential interaction terms. Our final multivariable negative binomial model included univariable predictors of significance and all significant interaction terms. Results: Across the 39 countries under consideration, our model shows COVID-19 case fatality rate was best predicted by time to implementation of social distancing measures, hospital beds per 1000 individuals, percent population over 70 years, CT scanners per 1 million individuals, and (in countries with high population density) smoking prevalence. Conclusion: Our model predicted an increased CFR for countries that waited over 14 days to implement social distancing interventions after the 100th reported case. Smoking prevalence and percentage population over the age of 70 years were also associated with higher CFR. Hospital beds per 1000 and CT scanners per million were identified as possible protective factors associated with decreased CFR. |
format | Online Article Text |
id | pubmed-7664233 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-76642332020-11-14 Coronavirus Disease 2019 (COVID-19): A Modeling Study of Factors Driving Variation in Case Fatality Rate by Country Pan, Jennifer St. Pierre, Joseph Marie Pickering, Trevor A. Demirjian, Natalie L. Fields, Brandon K.K. Desai, Bhushan Gholamrezanezhad, Ali Int J Environ Res Public Health Article Background: The novel Severe Acute Respiratory Syndrome Coronavirus-2 has led to a global pandemic in which case fatality rate (CFR) has varied from country to country. This study aims to identify factors that may explain the variation in CFR across countries. Methods: We identified 24 potential risk factors affecting CFR. For all countries with over 5000 reported COVID-19 cases, we used country-specific datasets from the WHO, the OECD, and the United Nations to quantify each of these factors. We examined univariable relationships of each variable with CFR, as well as correlations among predictors and potential interaction terms. Our final multivariable negative binomial model included univariable predictors of significance and all significant interaction terms. Results: Across the 39 countries under consideration, our model shows COVID-19 case fatality rate was best predicted by time to implementation of social distancing measures, hospital beds per 1000 individuals, percent population over 70 years, CT scanners per 1 million individuals, and (in countries with high population density) smoking prevalence. Conclusion: Our model predicted an increased CFR for countries that waited over 14 days to implement social distancing interventions after the 100th reported case. Smoking prevalence and percentage population over the age of 70 years were also associated with higher CFR. Hospital beds per 1000 and CT scanners per million were identified as possible protective factors associated with decreased CFR. MDPI 2020-11-05 2020-11 /pmc/articles/PMC7664233/ /pubmed/33167564 http://dx.doi.org/10.3390/ijerph17218189 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pan, Jennifer St. Pierre, Joseph Marie Pickering, Trevor A. Demirjian, Natalie L. Fields, Brandon K.K. Desai, Bhushan Gholamrezanezhad, Ali Coronavirus Disease 2019 (COVID-19): A Modeling Study of Factors Driving Variation in Case Fatality Rate by Country |
title | Coronavirus Disease 2019 (COVID-19): A Modeling Study of Factors Driving Variation in Case Fatality Rate by Country |
title_full | Coronavirus Disease 2019 (COVID-19): A Modeling Study of Factors Driving Variation in Case Fatality Rate by Country |
title_fullStr | Coronavirus Disease 2019 (COVID-19): A Modeling Study of Factors Driving Variation in Case Fatality Rate by Country |
title_full_unstemmed | Coronavirus Disease 2019 (COVID-19): A Modeling Study of Factors Driving Variation in Case Fatality Rate by Country |
title_short | Coronavirus Disease 2019 (COVID-19): A Modeling Study of Factors Driving Variation in Case Fatality Rate by Country |
title_sort | coronavirus disease 2019 (covid-19): a modeling study of factors driving variation in case fatality rate by country |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7664233/ https://www.ncbi.nlm.nih.gov/pubmed/33167564 http://dx.doi.org/10.3390/ijerph17218189 |
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