Association Between Ethnicity and Severe COVID-19 Disease: a Systematic Review and Meta-analysis

  1. Antony Raharja
  2. Alice Tamara
  3. Li Teng Kok

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Abstract

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  1. Version published to 10.1007/s40615-020-00921-5
  2. ScreenIT

    SciScore for 10.1101/2020.08.12.20157271: (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

    Software and Algorithms
    SentencesResources
    Databases used were Ovid MEDLINE, Ovid EMBASE, Cochrane COVID-19 Study Register, and World Health Organization (WHO) COVID-19 Global Research Database.
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    Preprint servers (MedRxiv and BioRxiv) were searched for non-peer-reviewed preprint articles.
    BioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)
    Citation tracking was carried out using Google Scholar on 24 June 2020 to identify relevant articles which cited any of the included studies.
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    All statistical analyses were performed in RStudio (version 1.3.959).
    RStudio
    suggested: (RStudio, RRID:SCR_000432)

    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:
    Despite these limitations, our rigorous study updates current evidence on the association between ethnicity and poor covid-19 outcomes, and identifies gaps in evidence that future studies can work towards.

    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.

  3. Version published to 10.1101/2020.08.12.20157271v1 on medRxiv