Factors associated with country-variation in COVID-19 morbidity and mortality worldwide: an observational geographic study COVID-19 morbidity and mortality country-variation

  1. H Bellali
  2. N Chtioui
  3. M Chahed

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Abstract

Background

The world is threatened by the outbreak of coronavirus disease 19 (COVID-19) since December 2019. The number of cases and deaths increased dramatically in some countries from March 2020. The objective of our study was to examine potential associated factors with country-variation in COVID-19 morbidity and mortality in the world.

Methods

We performed a retrospective geographic study including all countries with the most recent available data on free access on the web. We analyzed univariate and multivariable correlation between both the number of reported cases and deaths by country and demographic, socioeconomic characteristics, lockdown as major control measure, average annual temperature and relative humidity. We performed simple linear regression, independent t test and ANOVA test for univariate analyses and negative binomial regression model for multivariable analyses.

Results

We analyzed data of 186 countries from all world regions. As of 13 th April 2020, a total of 1 804 302 COVID-19 cases and 113 444 deaths were reported. The reported number of COVID-19 cases and deaths by countries was associated with the number of days between the first case and lockdown, the number of cases at lockdown, life expectancy at birth, average annual temperature and the socio-economic level. Countries which never implemented BCG vaccination reported higher mortality than others.

Conclusions

The pandemic is still ongoing and poses a global health threat as there is no effective antiviral treatment or vaccines. Thus, timing of control measure implementation is a crucial factor in determining the spread of the epidemic. It should be a lesson for this pandemic and for the future.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.27.20114280: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableWe collected population density, infant mortality rate 2015-2020 per 1000 inhabitant, the proportion of male/female, the proportion of people aged 65 years and over and the life expectancy at birth from the United Nations Website (7).

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We analyzed all data with SPSS Software.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our work had several limitations. First, we carried out an observational geographic study. Our results cannot prove causality, they just suggest an association. However, in such pandemic situation due to emerging pathogen, these results are useful because data are immediately available and could help predicting the spread of the epidemic and guide control measures in the absence of effective treatment and vaccine to tackle the epidemic. Second, we used general indicators by countries such as average annual temperature and relative humidity; the data could be not very precise. But it might not affect the analysis since the objective was to compare outcomes between countries once we used the same variable and the same source for all countries. Third, we didn’t take into account the difference in population rate change in behaviors (50), societal and social psychological factors (51) and the real application and respect for total containment, that’s why our work explained only a part of country-variation on COVID-19 morbidity and mortality.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.05.27.20114280v1 on medRxiv