Ethnicity and clinical outcomes in COVID-19: A systematic review and meta-analysis

  1. Shirley Sze
  2. Daniel Pan
  3. Clareece R. Nevill
  4. Laura J. Gray
  5. Christopher A. Martin
  6. Joshua Nazareth
  7. Jatinder S. Minhas
  8. Pip Divall
  9. Kamlesh Khunti
  10. Keith R. Abrams
  11. Laura B. Nellums
  12. Manish Pareek

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Abstract

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  1. ScreenIT

    SciScore for 10.1101/2020.09.05.20188821: (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
    7 Data sources and searches: An academic librarian (PD) developed the search strategies, and carried out a search of the databases MEDLINE, EMBASE, PROSPERO and the Cochrane Library, as shown in the Supplementary materials 1.
    MEDLINE
    suggested: (MEDLINE, RRID:SCR_002185)
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    Cochrane Library
    suggested: (Cochrane Library, RRID:SCR_013000)
    Where multiple preprints of the same database have been published (for example, the Biobank database), the most recent version up to 30th June 2020 was used, with published peer-reviewed studies favoured over those in the preprint database.
    Biobank
    suggested: (HIV Biobank, RRID:SCR_004691)
    Adjusted OR were converted to adjusted RR using the conversion method as recommended by the Cochrane Handbook.
    Cochrane Handbook
    suggested: None
    All meta-analyses were conducted using STATA version 16.1 (StataCorp, United States).
    STATA
    suggested: (Stata, RRID:SCR_012763)
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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:
    Strengths and limitations: This is the first and only comprehensive synthesis of the existing evidence base, in which we provide a robust examination of both published peer-reviewed research and preprints reporting on COVID-19 outcomes in ethnic minority groups. Whilst the findings integrate the available data in this field, providing insight into disparities in COVID-19 outcomes in ethnic minorities in order to inform both policy and practice, our study had several limitations. Variations across papers in relation to populations, setting, treatment context, and reporting of ethnicity and outcomes, resulted in high heterogeneity. However, this does not preclude pooling of data and is consistent with other meta-analyses on infection in diverse populations.60,61 Instead, we explored heterogeneity through sensitivity analyses. The analyses provide an important visualisation of the data available, and highlight the heterogeneity across the research and need to improve data collection and analysis, including greater standardisation in adjusted analyses. Several studies may have overlapping populations.62 For example, we found six studies from Mount Sinai investigating mortality from COVID-19; quite possibly from the same population.39,63–66 We have minimised this error by excluding studies which were clearly done on the same database, though we urge greater transparency in reporting for future research. Finally, all studies included were from the UK and USA. Whilst both these coun...

    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 10.1016/j.eclinm.2020.100630
  3. Version published to 10.1101/2020.09.05.20188821v1 on medRxiv