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

Jennifer Pan, Joseph Marie St. Pierre, Trevor A. Pickering, Natalie L. Demirjian, Brandon K.K. Fields, Bhushan Desai and Ali Gholamrezanezhad
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Jennifer Pan: Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
Joseph Marie St. Pierre: Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
Trevor A. Pickering: Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
Natalie L. Demirjian: Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
Brandon K.K. Fields: Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
Bhushan Desai: Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
Ali Gholamrezanezhad: Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA

IJERPH, 2020, vol. 17, issue 21, 1-11

Abstract: 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.

Keywords: COVID-19; SARS-CoV-2; pneumonia; computed tomography; case fatality rate; social distancing; smoking (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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