Coronavirus Disease 2019 (COVID-19): A Modeling Study of Factors Driving Variation in Case Fatality Rate by Country

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. We identified 24 potential risk factors affecting C...

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Bibliographic Details
Published in:	International journal of environmental research and public health Vol. 17; no. 21; p. 8189
Main Authors:	Pan, Jennifer, St Pierre, Joseph Marie, Pickering, Trevor A, Demirjian, Natalie L, Fields, Brandon K K, Desai, Bhushan, Gholamrezanezhad, Ali
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 05-11-2020 MDPI
Subjects:	Age Distribution Betacoronavirus Communicable Disease Control - trends Computed tomography Coronavirus Infections - mortality Coronaviruses COVID-19 Disease control Fatalities Hospital Bed Capacity Humans Internationality Models, Statistical Pandemics Pneumonia, Viral - mortality Population density Risk analysis Risk factors SARS-CoV-2 Severe acute respiratory syndrome Severe acute respiratory syndrome coronavirus 2 Smoking Tomography Scanners, X-Ray Computed - supply & distribution China COVID-19 computed tomography SARS-CoV-2 pneumonia case fatality rate smoking social distancing
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Summary:	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. 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. 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. 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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 These authors contributed equally to the work and should be considered as co-first authors.
ISSN:	1660-4601 1661-7827 1660-4601
DOI:	10.3390/ijerph17218189