Snapshot PCR surveillance for SARS-CoV-2 in hospital staff in England

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Abstract

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  1. SciScore for 10.1101/2020.06.14.20128876: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Staff who volunteered to participate provided informed consent and were allocated a unique identifier.
    Randomizationnot detected.
    Blindingnot detected.
    Power AnalysisStudy size: A sample size calculation estimated 98 staff would require testing to detect a 10% prevalence (based upon unpublished data in early to mid-April 2020 shared through professional networks) with a margin of error 5% (with 90% power).
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Viral culture of PHE laboratory positives was attempted in Vero E6 cells with virus detection confirmed by cytopathic effect up to 14 days post-inoculation.
    Vero E6
    suggested: None
    Software and Algorithms
    SentencesResources
    Stata (version 15, StataCorp, Texas) was used to describe the data and a random effects regression analysis was performed to examine the relationship between positive results and demographics and a priori exposures that were thought predictive of infection.
    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 study is the first which estimates point prevalence of SARS-CoV-2 carriage in HCWs at work in multiple job roles across a number of hospitals in different UK regions. Our study also includes a larger number of participants than previous reported studies. Prevalence among HCWs will be dynamic, and likely to change as the infection rate across the whole population falls.13 This snapshot study is unable to capture such trends. Selection bias could be present in either direction, particularly for the latter two sites: staff may have been more inclined to volunteer if they were concerned about COVID-19 infection, or less likely if they were anxious of work exclusion for themselves or their household. Staff with symptoms or exposures may have been less inclined to report these honestly (information bias), though reassurance about confidentiality will have at least in part mitigated this. The potential for symptom and exposure recall bias about was present throughout, and questionnaires were single data entered. Due to full anonymisation we were unable to follow-up one participant who was asymptomatic at the time of testing to see if they developed symptoms, and the one culture positive participant was unable to be matched to a questionnaire, so it is not possible to estimate the true asymptomatic infection prevalence. Of the 17 post-symptomatic HCWs, four had a previous positive test result, and a further six were followed-up at the two latter hospit...

    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.