The association of smoking status with hospitalisation for COVID-19 compared with other respiratory viruses a year previous: A case-control study at a single UK National Health Service trust

  1. David Simons
  2. Olga Perski
  3. Lion Shahab
  4. Jamie Brown
  5. Robin Bailey

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Abstract

Background

It is unclear whether smoking increases the risk of COVID-19 hospitalisation. We examined i) the association of smoking status with hospitalisation for COVID-19 compared with hospitalisation for other respiratory viral infections a year previous; and ii) concordance between smoking status recorded on the electronic health record (EHR) and the contemporaneous medical notes.

Methods

This case-control study enrolled adult patients (446 cases and 211 controls) at a single National Health Service trust in London, UK. The outcome variable was type of hospitalisation (COVID-19 vs. another respiratory virus a year previous). The exposure variable was smoking status (never/former/current smoker). Logistic regression analyses adjusted for age, sex, socioeconomic position and comorbidities were performed. The study protocol and analyses were pre-registered in April 2020 on the Open Science Framework .

Results

Current smokers had lower odds of being hospitalised with COVID-19 compared with other respiratory viruses a year previous (OR adj =0.55, 95% CI=0.31-0.96, p =.04). There was no significant association among former smokers (OR adj =1.08, 95% CI=0.72-1.65, p =.70). Smoking status recorded on the EHR (compared with the contemporaneous medical notes) was incorrectly recorded for 168 (79.6%) controls (χ 2 (3)=256.5, p =<0.001) and 60 cases (13.5%) (χ 2 (3)=34.2, p =<0.001).

Conclusions

In a single UK hospital trust, current smokers had reduced odds of being hospitalised with COVID-19 compared with other respiratory viruses a year previous, although it is unclear whether this association is causal. Targeted post-discharge recording of smoking status may account for the greater EHR- medical notes concordance observed in cases compared with controls.

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  1. Version published to 10.1101/2020.11.26.20238469v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.11.26.20238469: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: The requirement for informed consent was waived due to the nature of the study.
    Randomizationnot detected.
    Blindingnot detected.
    Power AnalysisThe pre-registered protocol stipulated a non-inferiority design (i.e. a one-tailed statistical test) to maximise statistical power to detect a significantly lower proportion of current smokers among patients hospitalised with COVID-19 compared with patients hospitalised with another respiratory viral infection a year previous.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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 10.1101/2020.11.26.20238469v1 on medRxiv