tetano
Editor, Senior Moderator
World J Virol
. 2024 Mar 25;13(1):87881.
doi: 10.5501/wjv.v13.i1.87881. Country-based modelling of COVID-19 case fatality rate: A multiple regression analysis
Soodeh Sagheb[SUP] 1 [/SUP], Ali Gholamrezanezhad[SUP] 2 [/SUP], Elizabeth Pavlovic[SUP] 3 [/SUP], Mohsen Karami[SUP] 4 [/SUP], Mina Fakhrzadegan[SUP] 4 [/SUP]
Affiliations
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.
Keywords: COVID-19; Case fatality rate; Multiple regression; Predictive model; SARS-CoV-2.
. 2024 Mar 25;13(1):87881.
doi: 10.5501/wjv.v13.i1.87881. Country-based modelling of COVID-19 case fatality rate: A multiple regression analysis
Soodeh Sagheb[SUP] 1 [/SUP], Ali Gholamrezanezhad[SUP] 2 [/SUP], Elizabeth Pavlovic[SUP] 3 [/SUP], Mohsen Karami[SUP] 4 [/SUP], Mina Fakhrzadegan[SUP] 4 [/SUP]
Affiliations
- PMID: 38616858
- PMCID: PMC11008404
- DOI: 10.5501/wjv.v13.i1.87881
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.
Keywords: COVID-19; Case fatality rate; Multiple regression; Predictive model; SARS-CoV-2.