SARS-CoV-2 IgG seroprevalence in healthcare workers and other staff at North Bristol NHS Trust: A sociodemographic analysis

  1. Christopher R. Jones
  2. Fergus W. Hamilton
  3. Ameeka Thompson
  4. Tim T. Morris
  5. Ed Moran

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Abstract

No abstract available

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  1. Version published to 10.1016/j.jinf.2020.11.036
  2. ScreenIT

    SciScore for 10.1101/2020.11.12.20230458: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethics: Ethical approval for this study was granted by the North West - Greater Manchester West Research Ethics Committee (20/NW/0354).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Study design and setting: This was a cross-sectional SARS-CoV-2 IgG antibody seroprevalence study of HCWs and support staff at North Bristol NHS Trust.
    SARS-CoV-2 IgG
    suggested: None
    SARS-CoV-2 antibody assay: The serological status of HCWs and support staff was determined using one of two platforms: 1) the Abbott™ SARS-CoV-2 IgG chemiluminescent microparticle assay on an Architect™ system (Abbott Laboratories); or 2) the Roche™ Elecsys® Anti-SARS-CoV-2 (IgG/IgM) electrochemiluminescent immunoassay on a Cobas™ analyser (Roche Diagnostics).
    Anti-SARS-CoV-2 (IgG/IgM
    suggested: None
    Software and Algorithms
    SentencesResources
    SARS-CoV-2 antibody assay: The serological status of HCWs and support staff was determined using one of two platforms: 1) the Abbott™ SARS-CoV-2 IgG chemiluminescent microparticle assay on an Architect™ system (Abbott Laboratories); or 2) the Roche™ Elecsys® Anti-SARS-CoV-2 (IgG/IgM) electrochemiluminescent immunoassay on a Cobas™ analyser (Roche Diagnostics).
    Abbott Laboratories)
    suggested: None

    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:
    This study has limitations that must be considered when drawing conclusions. First, participants volunteered for serological testing and therefore data are subject to selection bias due to participation. To mitigate the effect of this non-random participation, inverse probability weighting was used to recover estimates that were unbiased by testing patterns on our observed characteristics. Our analysis may still be biased by other factors relating to participation that were not measured. Second, neighbourhood deprivation was analysed at the level of MSOA. The Lower Layer Super Output Area is a smaller geographical unit that offers more detailed estimates of geographical differences. However, due to sample size restrictions imposed by the testing dataset, it was not possible to perform analyses at a richer scale than MSOA. Third, it is possible that participants were more likely to attend for serological testing if they thought they had previously been infected with SARS-CoV-2. This ascertainment bias is difficult to control or adjust for in the absence of clinical data, which was not collected. Finally, HCWs are not representative of the UK population in general, nor are they representative of MSOAs, and therefore generalisability of findings is limited.

    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 10.1101/2020.11.12.20230458v1 on medRxiv