Obesity and COVID-19 Mortality A Cross-Country Analysis

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

We highlight a robust correlation between COVID-19 mortality and obesity prevalence using available country level data on COVID-19 mortality as of August 10, 2020. Such association is robust to controlling for other potential comorbidity factors: diabetes, cardio-vascular, and respiratory diseases, further to a set of demographics, urban, and economic, and containment policies controls. We estimate that .6 log point increase in obesity prevalence, or 1 standard deviation, is associated with about an extra .9 log point per 100,000 deaths (or 50% of a standard deviation, .5 σ) .

Article activity feed

  1. SciScore for 10.1101/2021.01.28.21249723: (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.