Coronavirus Disease 2019 (COVID-19): A Modeling Study of Factors Driving Variation in Case Fatality Rate by Country
<i>Background:</i> 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. <i>Methods:<...
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doaj-d9ede11b514a4e2a9d0d27a439f400512020-11-25T03:59:57ZengMDPI AGInternational Journal of Environmental Research and Public Health1661-78271660-46012020-11-01178189818910.3390/ijerph17218189Coronavirus Disease 2019 (COVID-19): A Modeling Study of Factors Driving Variation in Case Fatality Rate by CountryJennifer Pan0Joseph Marie St. Pierre1Trevor A. Pickering2Natalie L. Demirjian3Brandon K.K. Fields4Bhushan Desai5Ali Gholamrezanezhad6Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USAKeck School of Medicine, University of Southern California, Los Angeles, CA 90033, USAKeck School of Medicine, University of Southern California, Los Angeles, CA 90033, USAKeck School of Medicine, University of Southern California, Los Angeles, CA 90033, USAKeck School of Medicine, University of Southern California, Los Angeles, CA 90033, USAKeck School of Medicine, University of Southern California, Los Angeles, CA 90033, USAKeck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA<i>Background:</i> 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. <i>Methods:</i> 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. <i>Results:</i> 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. <i>Conclusion:</i> 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.https://www.mdpi.com/1660-4601/17/21/8189COVID-19SARS-CoV-2pneumoniacomputed tomographycase fatality ratesocial distancing |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jennifer Pan Joseph Marie St. Pierre Trevor A. Pickering Natalie L. Demirjian Brandon K.K. Fields Bhushan Desai Ali Gholamrezanezhad |
spellingShingle |
Jennifer Pan Joseph Marie St. Pierre Trevor A. Pickering Natalie L. Demirjian Brandon K.K. Fields Bhushan Desai Ali Gholamrezanezhad Coronavirus Disease 2019 (COVID-19): A Modeling Study of Factors Driving Variation in Case Fatality Rate by Country International Journal of Environmental Research and Public Health COVID-19 SARS-CoV-2 pneumonia computed tomography case fatality rate social distancing |
author_facet |
Jennifer Pan Joseph Marie St. Pierre Trevor A. Pickering Natalie L. Demirjian Brandon K.K. Fields Bhushan Desai Ali Gholamrezanezhad |
author_sort |
Jennifer Pan |
title |
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_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_sort |
coronavirus disease 2019 (covid-19): a modeling study of factors driving variation in case fatality rate by country |
publisher |
MDPI AG |
series |
International Journal of Environmental Research and Public Health |
issn |
1661-7827 1660-4601 |
publishDate |
2020-11-01 |
description |
<i>Background:</i> 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. <i>Methods:</i> 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. <i>Results:</i> 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. <i>Conclusion:</i> 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. |
topic |
COVID-19 SARS-CoV-2 pneumonia computed tomography case fatality rate social distancing |
url |
https://www.mdpi.com/1660-4601/17/21/8189 |
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