Sagheb S, Gholamrezanezhad A, Pavlovic E, Karami M, Fakhrzadegan M. Country-based modelling of COVID-19 case fatality rate: A multiple regression analysis. World J Virol 2024; 13(1): 87881 [PMID: 38616858 DOI: 10.5501/wjv.v13.i1.87881]
Corresponding Author of This Article
Ali Gholamrezanezhad, MD, Associate Professor, Department of Radiology, Keck School of Medicine, University of Southern California, 3551 Trousdale Pkwy, University Park, Los Angeles, CA 90033, United States. ali.gholamrezanezhad@med.usc.edu
Research Domain of This Article
Virology
Article-Type of This Article
Basic Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Virol. Mar 25, 2024; 13(1): 87881 Published online Mar 25, 2024. doi: 10.5501/wjv.v13.i1.87881
Country-based modelling of COVID-19 case fatality rate: A multiple regression analysis
Soodeh Sagheb, Ali Gholamrezanezhad, Elizabeth Pavlovic, Mohsen Karami, Mina Fakhrzadegan
Soodeh Sagheb, Department of Radiology, Seattle Children's Hospital, University of Washington, Seattle, WA 98145, United States
Ali Gholamrezanezhad, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States
Elizabeth Pavlovic, Department of Nursing, University of New Brunswick, New Brunswick E3B 5A3, Canada
Mohsen Karami, Mina Fakhrzadegan, Department of Orthopedics, Taleghani Hospital, Shahid Beheshti University of Medical Sciences, Tehran 1516745811, Iran
Author contributions: Gholamrezanezhad A and Sagheb S conceived of the presented idea, and set the first draft of manuscript; Karami M and Pavlovic E developed the theory and worked out almost all of the technical details, and data extraction; Sagheb S and Fakhrzadegan M designed the model and the computational framework and analysed the data; language editing and revising the manuscript has done by Pavlovic E; All authors discussed the results and contributed to the final manuscript.
Institutional review board statement: In this modeling publicly available register-based ecological study as a population study, all data are available on open data sources like Our World in Data, World Bank, Statistics, OECD Database, and World Population Review. We included no private patients and no private data, and everything is clear in the references. As the research involves information freely available in the public domain, this study doesn't need to ethics code or institute approval.
Conflict-of-interest statement: All the authors have no financial relationships relevant to this article to disclose.
Data sharing statement: All study datasets are referenced and available to the public.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ali Gholamrezanezhad, MD, Associate Professor, Department of Radiology, Keck School of Medicine, University of Southern California, 3551 Trousdale Pkwy, University Park, Los Angeles, CA 90033, United States. ali.gholamrezanezhad@med.usc.edu
Received: August 31, 2023 Peer-review started: August 31, 2023 First decision: October 24, 2023 Revised: November 7, 2023 Accepted: December 25, 2023 Article in press: December 25, 2023 Published online: March 25, 2024 Processing time: 193 Days and 2.3 Hours
Abstract
BACKGROUND
The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection. The determinants of mortality on a global scale cannot be fully understood due to lack of information.
AIM
To identify key factors that may explain the variability in case lethality across countries.
METHODS
We identified 21 Potential risk factors for coronavirus disease 2019 (COVID-19) case fatality rate for all the countries with available data. We examined univariate relationships of each variable with case fatality rate (CFR), and all independent variables to identify candidate variables for our final multiple model. Multiple regression analysis technique was used to assess the strength of relationship.
RESULTS
The mean of COVID-19 mortality was 1.52 ± 1.72%. There was a statistically significant inverse correlation between health expenditure, and number of computed tomography scanners per 1 million with CFR, and significant direct correlation was found between literacy, and air pollution with CFR. This final model can predict approximately 97% of the changes in CFR.
CONCLUSION
The current study recommends some new predictors explaining affect mortality rate. Thus, it could help decision-makers develop health policies to fight COVID-19.
Core Tip: The current study recommends some new predictors explaining affect mortality rate. Thus, it could help decision-makers develop health policies to fight coronavirus disease 2019.
