Associations between vaping and Covid-19: Cross-sectional findings from the HEBECO study

  1. Dimitra Kale
  2. Aleksandra Herbec
  3. Olga Perski
  4. Sarah E. Jackson
  5. Jamie Brown
  6. Lion Shahab

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Abstract

No abstract available

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  1. Version published to 10.1016/j.drugalcdep.2021.108590
  2. ScreenIT

    SciScore for 10.1101/2020.12.01.20241760: (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 variableAge categorical (≤24, 25-34, 35-44, 45-54, 55-64, ≥65) Gender (female vs male/other) Education 2 levels: [=0/1/2 vs all other] (0, No formal qualification | 1, GCSE/School certificate/O-level/CSE | 2, Vocational qualifications (e.g. NVQ1+2) VS. 3, A-level/Higher school certificate or equivalent (e.g. NVQ3) | 4, Bachelor degree or equivalent (e.g.NVQ4) | 5, Masters/ PhD/PGCE or equivalent | 6, Other) Household income pre-Covid-19 3 levels: (<50 000/ ≥50 000/ unknown=prefer not to say) Ethnicity (2 levels; any white ethnicity vs all other including prefer not to say) Occupation 2 levels: (employed (1, 2) versus not (all other) (1, Employed (full or part-time) | 2, Self-employed (full or part-time) | 3, Student | 4, Furloughed during Covid-19 | 5, Laid off during Covid-19 | 6, Unemployed since before Covid-19 | 7,

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    HEBECO study data are collected and managed using REDCap electronic data capture tools hosted at University College London (Harris et al., 2009; Harris et al., 2019).
    REDCap
    suggested: (REDCap, RRID:SCR_003445)
    2.4 Statistical analysis: Analyses were conducted on complete cases using SPSS v.26.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    However, the study also had several limitations. First, diagnosed/suspected Covid-19 was self-reported and not confirmed with a viral or antibody test. As many other respiratory infections share symptoms with Covid-19, some participants may have misinterpreted their symptoms. In addition, it is likely that many participants experienced asymptomatic infection (Oran & Topol, 2020) and therefore did not report being infected. Second, our measure of vaping less likely underestimated the proportion reducing their vaping as those who quit vaping altogether since Covid-19 were not included in the analyses. Third, for some analyses the sample size was small, resulting in wide confidence intervals meaning that we likely lacked sufficient power to detect differences (confirmed by BFs, indicating data insensitivity). Fourth, while we weighted the sample to be representative of the UK adult population, it was self-selected and not random which affects the generalisability of the results. Fifth, most of the questions used were adapted from previous research and not validated, while changes in vaping were self-reported. Finally, the data were cross-sectional, and we did not measure the prospective relationship between changes in vaping and potential predictor variables. Longitudinal data following changes in vaping over time as the pandemic continues would be useful in evaluating the extent to which any initial changes in vaping behaviour are maintained over time.

    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 10.1101/2020.12.01.20241760v1 on medRxiv