Estimation of Risk Factor’s Contribution to mortality from COVID-19 in Highly Populated European Countries

Uri Eliyahu; Avi Magid

doi:doi:10.11648/j.bsi.20210601.11

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Estimation of Risk Factor’s Contribution to mortality from COVID-19 in Highly Populated European Countries

Uri Eliyahu, Avi Magid

Published in Biomedical Statistics and Informatics (Volume 6, Issue 1)

Received: 30 December 2020 Accepted: 9 January 2021 Published: 22 January 2021

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Abstract

Background: The outbreak of the COVID-19 epidemic and the excess of mortality attributed to COVID-19 worldwide raised the need to develop a simple and applicable mathematical model for predicting mortality in different countries, as well as to point out the risk factors for COVID-19 mortality, and, in particular, demographic risk factors. Methods: A linear model was developed based on demographic data (population density, percentage of population over age 65 and degree of urbanity) as well as a clinical data (number of days since the first case was diagnosed in each country) from 10 highly populated (over 8.5 million people) randomly selected European countries (Austria, Hungary, Portugal, Sweden, Czech Republic, Belgium, the Netherlands, Romania, Italy, France). A linear regression model was applied, using IBM SPSS version 20 software. Results: The proposed model predicts mortality among the selected countries. This model is found to be highly correlated (R²=0.821, p=0.042) with the actual (reported) number of deaths in each country. Percentage of population above age 65, population density and number of days since the first case appear at each state were found to be positively correlated with COVID-19 mortality, whereas urbanity were negatively correlated with mortality. Conclusions: Percentage of population above age 65 and population’s density and the number of days of exposure to COVID 19 are potential risk factors for dying from the pandemic, whereas, urbanity is considered a protective factor. However, it should be remembered that this model is based on data from medium to large populations and only in continental Europe. Moreover, it is based on mortality data of the "first wave" of the pandemic. Further study should evaluate the model accuracy based on data from the "second wave" and not only in continental Europe.

Published in	Biomedical Statistics and Informatics (Volume 6, Issue 1)
DOI	10.11648/j.bsi.20210601.11
Page(s)	1-5
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Pandemic, Linear Model, Demographics, Mortality, European

References

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[16]	Eliyahu U, Boaz M. Use of Databases Available on the Web to Describe Covid-19 Morbidity and Mortality Trends. Biomedical Statistics and Informatics. Vol. 5, No. 2, 2020, pp. 47-51. doi: 10.11648/j.bsi.20200502.12K.
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APA Style

Uri Eliyahu, Avi Magid. (2021). Estimation of Risk Factor’s Contribution to mortality from COVID-19 in Highly Populated European Countries. Biomedical Statistics and Informatics, 6(1), 1-5. https://doi.org/10.11648/j.bsi.20210601.11

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ACS Style

Uri Eliyahu; Avi Magid. Estimation of Risk Factor’s Contribution to mortality from COVID-19 in Highly Populated European Countries. Biomed. Stat. Inform. 2021, 6(1), 1-5. doi: 10.11648/j.bsi.20210601.11

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AMA Style

Uri Eliyahu, Avi Magid. Estimation of Risk Factor’s Contribution to mortality from COVID-19 in Highly Populated European Countries. Biomed Stat Inform. 2021;6(1):1-5. doi: 10.11648/j.bsi.20210601.11

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@article{10.11648/j.bsi.20210601.11,
  author = {Uri Eliyahu and Avi Magid},
  title = {Estimation of Risk Factor’s Contribution to mortality from COVID-19 in Highly Populated European Countries},
  journal = {Biomedical Statistics and Informatics},
  volume = {6},
  number = {1},
  pages = {1-5},
  doi = {10.11648/j.bsi.20210601.11},
  url = {https://doi.org/10.11648/j.bsi.20210601.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20210601.11},
  abstract = {Background: The outbreak of the COVID-19 epidemic and the excess of mortality attributed to COVID-19 worldwide raised the need to develop a simple and applicable mathematical model for predicting mortality in different countries, as well as to point out the risk factors for COVID-19 mortality, and, in particular, demographic risk factors. Methods: A linear model was developed based on demographic data (population density, percentage of population over age 65 and degree of urbanity) as well as a clinical data (number of days since the first case was diagnosed in each country) from 10 highly populated (over 8.5 million people) randomly selected European countries (Austria, Hungary, Portugal, Sweden, Czech Republic, Belgium, the Netherlands, Romania, Italy, France). A linear regression model was applied, using IBM SPSS version 20 software. Results: The proposed model predicts mortality among the selected countries. This model is found to be highly correlated (R2=0.821, p=0.042) with the actual (reported) number of deaths in each country. Percentage of population above age 65, population density and number of days since the first case appear at each state were found to be positively correlated with COVID-19 mortality, whereas urbanity were negatively correlated with mortality. Conclusions: Percentage of population above age 65 and population’s density and the number of days of exposure to COVID 19 are potential risk factors for dying from the pandemic, whereas, urbanity is considered a protective factor. However, it should be remembered that this model is based on data from medium to large populations and only in continental Europe. Moreover, it is based on mortality data of the "first wave" of the pandemic. Further study should evaluate the model accuracy based on data from the "second wave" and not only in continental Europe.},
 year = {2021}
}

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TY  - JOUR
T1  - Estimation of Risk Factor’s Contribution to mortality from COVID-19 in Highly Populated European Countries
AU  - Uri Eliyahu
AU  - Avi Magid
Y1  - 2021/01/22
PY  - 2021
N1  - https://doi.org/10.11648/j.bsi.20210601.11
DO  - 10.11648/j.bsi.20210601.11
T2  - Biomedical Statistics and Informatics
JF  - Biomedical Statistics and Informatics
JO  - Biomedical Statistics and Informatics
SP  - 1
EP  - 5
PB  - Science Publishing Group
SN  - 2578-8728
UR  - https://doi.org/10.11648/j.bsi.20210601.11
AB  - Background: The outbreak of the COVID-19 epidemic and the excess of mortality attributed to COVID-19 worldwide raised the need to develop a simple and applicable mathematical model for predicting mortality in different countries, as well as to point out the risk factors for COVID-19 mortality, and, in particular, demographic risk factors. Methods: A linear model was developed based on demographic data (population density, percentage of population over age 65 and degree of urbanity) as well as a clinical data (number of days since the first case was diagnosed in each country) from 10 highly populated (over 8.5 million people) randomly selected European countries (Austria, Hungary, Portugal, Sweden, Czech Republic, Belgium, the Netherlands, Romania, Italy, France). A linear regression model was applied, using IBM SPSS version 20 software. Results: The proposed model predicts mortality among the selected countries. This model is found to be highly correlated (R2=0.821, p=0.042) with the actual (reported) number of deaths in each country. Percentage of population above age 65, population density and number of days since the first case appear at each state were found to be positively correlated with COVID-19 mortality, whereas urbanity were negatively correlated with mortality. Conclusions: Percentage of population above age 65 and population’s density and the number of days of exposure to COVID 19 are potential risk factors for dying from the pandemic, whereas, urbanity is considered a protective factor. However, it should be remembered that this model is based on data from medium to large populations and only in continental Europe. Moreover, it is based on mortality data of the "first wave" of the pandemic. Further study should evaluate the model accuracy based on data from the "second wave" and not only in continental Europe.
VL  - 6
IS  - 1
ER  -

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Author Information

Uri Eliyahu

Department of Nutrition, Faculty of Health Sciences, Ariel University, Ariel, Israel
Avi Magid

Department of Health System Management, Peres Academic Center, Rehovot, Israel

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Uri Eliyahu, Avi Magid. (2021). Estimation of Risk Factor’s Contribution to mortality from COVID-19 in Highly Populated European Countries. Biomedical Statistics and Informatics, 6(1), 1-5. https://doi.org/10.11648/j.bsi.20210601.11

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ACS Style

Uri Eliyahu; Avi Magid. Estimation of Risk Factor’s Contribution to mortality from COVID-19 in Highly Populated European Countries. Biomed. Stat. Inform. 2021, 6(1), 1-5. doi: 10.11648/j.bsi.20210601.11

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AMA Style

Uri Eliyahu, Avi Magid. Estimation of Risk Factor’s Contribution to mortality from COVID-19 in Highly Populated European Countries. Biomed Stat Inform. 2021;6(1):1-5. doi: 10.11648/j.bsi.20210601.11

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@article{10.11648/j.bsi.20210601.11,
  author = {Uri Eliyahu and Avi Magid},
  title = {Estimation of Risk Factor’s Contribution to mortality from COVID-19 in Highly Populated European Countries},
  journal = {Biomedical Statistics and Informatics},
  volume = {6},
  number = {1},
  pages = {1-5},
  doi = {10.11648/j.bsi.20210601.11},
  url = {https://doi.org/10.11648/j.bsi.20210601.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20210601.11},
  abstract = {Background: The outbreak of the COVID-19 epidemic and the excess of mortality attributed to COVID-19 worldwide raised the need to develop a simple and applicable mathematical model for predicting mortality in different countries, as well as to point out the risk factors for COVID-19 mortality, and, in particular, demographic risk factors. Methods: A linear model was developed based on demographic data (population density, percentage of population over age 65 and degree of urbanity) as well as a clinical data (number of days since the first case was diagnosed in each country) from 10 highly populated (over 8.5 million people) randomly selected European countries (Austria, Hungary, Portugal, Sweden, Czech Republic, Belgium, the Netherlands, Romania, Italy, France). A linear regression model was applied, using IBM SPSS version 20 software. Results: The proposed model predicts mortality among the selected countries. This model is found to be highly correlated (R2=0.821, p=0.042) with the actual (reported) number of deaths in each country. Percentage of population above age 65, population density and number of days since the first case appear at each state were found to be positively correlated with COVID-19 mortality, whereas urbanity were negatively correlated with mortality. Conclusions: Percentage of population above age 65 and population’s density and the number of days of exposure to COVID 19 are potential risk factors for dying from the pandemic, whereas, urbanity is considered a protective factor. However, it should be remembered that this model is based on data from medium to large populations and only in continental Europe. Moreover, it is based on mortality data of the "first wave" of the pandemic. Further study should evaluate the model accuracy based on data from the "second wave" and not only in continental Europe.},
 year = {2021}
}

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TY  - JOUR
T1  - Estimation of Risk Factor’s Contribution to mortality from COVID-19 in Highly Populated European Countries
AU  - Uri Eliyahu
AU  - Avi Magid
Y1  - 2021/01/22
PY  - 2021
N1  - https://doi.org/10.11648/j.bsi.20210601.11
DO  - 10.11648/j.bsi.20210601.11
T2  - Biomedical Statistics and Informatics
JF  - Biomedical Statistics and Informatics
JO  - Biomedical Statistics and Informatics
SP  - 1
EP  - 5
PB  - Science Publishing Group
SN  - 2578-8728
UR  - https://doi.org/10.11648/j.bsi.20210601.11
AB  - Background: The outbreak of the COVID-19 epidemic and the excess of mortality attributed to COVID-19 worldwide raised the need to develop a simple and applicable mathematical model for predicting mortality in different countries, as well as to point out the risk factors for COVID-19 mortality, and, in particular, demographic risk factors. Methods: A linear model was developed based on demographic data (population density, percentage of population over age 65 and degree of urbanity) as well as a clinical data (number of days since the first case was diagnosed in each country) from 10 highly populated (over 8.5 million people) randomly selected European countries (Austria, Hungary, Portugal, Sweden, Czech Republic, Belgium, the Netherlands, Romania, Italy, France). A linear regression model was applied, using IBM SPSS version 20 software. Results: The proposed model predicts mortality among the selected countries. This model is found to be highly correlated (R2=0.821, p=0.042) with the actual (reported) number of deaths in each country. Percentage of population above age 65, population density and number of days since the first case appear at each state were found to be positively correlated with COVID-19 mortality, whereas urbanity were negatively correlated with mortality. Conclusions: Percentage of population above age 65 and population’s density and the number of days of exposure to COVID 19 are potential risk factors for dying from the pandemic, whereas, urbanity is considered a protective factor. However, it should be remembered that this model is based on data from medium to large populations and only in continental Europe. Moreover, it is based on mortality data of the "first wave" of the pandemic. Further study should evaluate the model accuracy based on data from the "second wave" and not only in continental Europe.
VL  - 6
IS  - 1
ER  -

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