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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Main Authors: Jennifer Pan, Joseph Marie St. Pierre, Trevor A. Pickering, Natalie L. Demirjian, Brandon K.K. Fields, Bhushan Desai, Ali Gholamrezanezhad
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:International Journal of Environmental Research and Public Health
Subjects:
Online Access:https://www.mdpi.com/1660-4601/17/21/8189
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spelling 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
collection 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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