Evidence map›Paper›PMID 38616858›Full record

ArticleWorld journal of virology2024

Country-based modelling of COVID-19 case fatality rate: A multiple regression analysis.

Soodeh Sagheb, Ali Gholamrezanezhad, Elizabeth Pavlovic, Mohsen Karami, Mina Fakhrzadegan

Abstract read
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Article in World journal of virology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Soodeh SaghebDepartment of Radiology, Seattle Children's Hospital, University of Washington, Seattle, WA 98145, United States.
Ali GholamrezanezhadDepartment of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States. ali.gholamrezanezhad@med.usc.edu.
Elizabeth PavlovicDepartment of Nursing, University of New Brunswick, New Brunswick E3B 5A3, Canada.
Mohsen KaramiDepartment of Orthopedics, Taleghani Hospital, Shahid Beheshti University of Medical Sciences, Tehran 1516745811, Iran.
Mina FakhrzadeganDepartment of Orthopedics, Taleghani Hospital, Shahid Beheshti University of Medical Sciences, Tehran 1516745811, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

aimTo identify key factors that may explain the variability in case lethality across countries.

methodsWe 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.

resultsThe 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.

conclusionThe current study recommends some new predictors explaining affect mortality rate. Thus, it could help decision-makers develop health policies to fight COVID-19.

Indexed as

Case fatality rateCOVID-19Multiple regressionPredictive modelSARS-CoV-2

Identifiers

PMID38616858
PMCPMC11008404

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.