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<Articles JournalTitle="Journal of Biostatistics and Epidemiology">
  <Article>
    <Journal>
      <PublisherName>Tehran University of Medical Sciences</PublisherName>
      <JournalTitle>Journal of Biostatistics and Epidemiology</JournalTitle>
      <Issn>2383-4196</Issn>
      <Volume>6</Volume>
      <Issue>2</Issue>
      <PubDate PubStatus="epublish">
        <Year>2020</Year>
        <Month>12</Month>
        <Day>13</Day>
      </PubDate>
    </Journal>
    <title locale="en_US">Country Level Socioeconomic and Health System Indicators Explain COVID-19 Mortality Worldwide</title>
    <FirstPage>93</FirstPage>
    <LastPage>100</LastPage>
    <AuthorList>
      <Author>
        <FirstName>Mohammad</FirstName>
        <LastName>Noorchenarboo</LastName>
        <affiliation locale="en_US">Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.</affiliation>
      </Author>
      <Author>
        <FirstName>Seyed Amirreza</FirstName>
        <LastName>Mousavi</LastName>
        <affiliation locale="en_US">Students Scientific Research Center, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.</affiliation>
      </Author>
      <Author>
        <FirstName>Hamed</FirstName>
        <LastName>Moheimani</LastName>
        <affiliation locale="en_US">Students Scientific Research Center, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.</affiliation>
      </Author>
    </AuthorList>
    <History>
      <PubDate PubStatus="received">
        <Year>2020</Year>
        <Month>07</Month>
        <Day>02</Day>
      </PubDate>
      <PubDate PubStatus="accepted">
        <Year>2020</Year>
        <Month>07</Month>
        <Day>27</Day>
      </PubDate>
    </History>
    <abstract locale="en_US">Background: COVID-19 mortality rates differ across countries. We aimed to construct a model that predicts mortality worldwide, by including only country-level socioeconomic and health system indicators and&#xA0;excluding variables related to short-term measures for pandemic management.
Methods: COVID-19 mortality data was collected from Johns Hopkins University resource center.&#xA0;Additional sources were public reports from the United Nations, the World Bank and the Heritage Foundation.&#xA0;We implemented multiple linear regression with backward elimination on the selected predictors.
Results: The final model constructed on seven Independent variables, significantly predicted COVID-19 mortality rate by country (F-statistic: 29.2, p&lt;0.001). Regression coefficients (95% CI) in descending order of standardized effects: Annual tourist arrivals: 5.43 (4.03, 6.83); health expenditure per capita: 4.43 (2.92, 5.96); GDP (PPP): -4.60 (-6.81, -2.38); specialist surgical workforce per 100000: 2.63 (0.67, 4.59); number of physicians per 1000: -2.32 (-4.3, -0.28); economic freedom score: -1.35 (-2.60, -0.10); and total population: 1.66 (-0.19, 3.52). All VIF values were below 5, showing acceptable collinearity. R-squared (52.65%),&#xA0;adjusted R-squared (50.25%) and predicted R-squared (42.33%) showed strong model fit.
Conclusion: limited country-level socioeconomic and health system indicators can explain COVID-19&#xA0;mortality worldwide; emphasizing the priority of attending to these fundamental structures when planning for pandemic preparedness.</abstract>
    <web_url>https://jbe.tums.ac.ir/index.php/jbe/article/view/349</web_url>
  </Article>
</Articles>
