COVID-19 and Socioeconomic Factors: Cross-country Evidence

  1. Vishalkumar Jani
  2. Dileep Mavalankar

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Abstract

Background

COVID-19 pandemic has affected all countries across the globe in varying intensity resulting in varied numbers for total cases and deaths.

Objectives

The paper aims to understand if different socioeconomic factors have a role to play in determining the intensity of COVID-19 impact.

Methods

The study uses a country-wise number of corona cases and deaths and analyse them in a cross-country multivariate regression framework. It uses gross domestic product per capita, average temperature, population density, and median age as independent variables. The study uses testing data as a control variable.

Results

In absence of the testing variable, higher-income countries have experienced a higher number of COVID cases. The population density, median age, climate do not have significant impact. The countries with higher population density have lower deaths. Each region shows different patterns of correlation between socioeconomic factors and COVID intensity.

Conclusion

The majority of the cross-country variation can be attributed to the number of tests done by a country. The countries with high population density would have applied strict lockdowns and proactive testing to curb the deaths. The study essentially refutes claims around corona being a high-income group disease, cold-climate disease, or a disease impacting old-age patients more.

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  1. ScreenIT

    SciScore for 10.1101/2020.10.22.20217430: (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 variablenot detected.

    Table 2: Resources

    No key resources detected.

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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 10.1101/2020.10.22.20217430v1 on medRxiv