A case-control and cohort study to determine the relationship between ethnic background and severe COVID-19

  1. Rosita Zakeri
  2. Rebecca Bendayan
  3. Mark Ashworth
  4. Daniel M. Bean
  5. Hiten Dodhia
  6. Stevo Durbaba
  7. Kevin O'Gallagher
  8. Claire Palmer
  9. Vasa Curcin
  10. Elizabeth Aitken
  11. William Bernal
  12. Richard D. Barker
  13. Sam Norton
  14. Martin Gulliford
  15. James T.H. Teo
  16. James Galloway
  17. Richard J.B. Dobson
  18. Ajay M. Shah

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Abstract

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  1. Version published to 10.1016/j.eclinm.2020.100574
  2. ScreenIT

    SciScore for 10.1101/2020.07.08.20148965: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: De-identified data from patients admitted to KCHFT were analysed under London SE Research Ethics Committee approval (reference 18/LO/2048) granted to the King’s Electronic Records Research Interface (KERRI).
    Consent: Access to Lambeth DataNet was under a project-specific approval granted by Lambeth Public Health Caldicott Guardian, 30 April 2020; additional informed consent was not required.
    Randomization11,12 This dataset includes de-identified data on 344,083 (96.8%) community-resident adults registered with 41 practices in inner south-east London.13 For each case, we randomly sampled four controls who were individually matched to cases by age (within 5-year age bands) and sex.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Experimental Models: Organisms/Strains
    SentencesResources
    17 These were reduced into four groups: White (British, Irish, Gypsy, any other White), Black (African, Caribbean, any other Black), Asian (Indian, Pakistani, Bangladeshi, Chinese, any other Asian), and Mixed/Other.
    groups: White
    suggested: None
    Software and Algorithms
    SentencesResources
    Analyses were performed using STATA/IC (
    STATA/IC
    suggested: None
    v16.1; StataCorp LLC, TX)
    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:
    The limitations of these approaches in terms of selection or collider bias have been discussed.32 In our study, the use of a case-population design to assess the risk of admission for severe COVID-19 allowed us to compare the characteristics of admitted patients with a representative sample of the source population and thereby minimise selection bias. Our primary care database covered a large inner city region that closely matched the normal catchment area for the hospital. We cannot exclude the possibility that White patients are differentially admitted to other hospitals in the area but we consider this unlikely since emergency admissions are typically to the nearest hospital in the UK National Health Service. Moreover, we present data for all five hospitals in south-east London, which confirm excess COVID-19 admissions in the Black ethnic group. It is unlikely that our findings are explained by a lower threshold for admission in the Black group, because symptom duration was generally greater for these patients. This approach strengthens our conclusions regarding the interrelationship between ethnicity, comorbidities, deprivation score and risk of admission. Although our study was performed in a single region of inner London, the results are likely to be applicable to similar large cities with multi-ethnic populations around the world, especially in relation to the risks that appear independent of socioeconomic deprivation. Our study has some limitations. In common with the...

    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 10.1101/2020.07.08.20148965v1 on medRxiv