Smoking is associated with worse outcomes of COVID-19 particularly among younger adults: a systematic review and meta-analysis

  1. Roengrudee Patanavanich
  2. Stanton A. Glantz

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Abstract

Background

Smoking impairs lung immune function and damages upper airways, increasing risks of contracting and severity of infectious diseases. This paper quantifies the association between smoking and COVID-19 disease progression.

Methods

We searched PubMed and Embase for studies published from January 1–May 25, 2020. We included studies reporting smoking behavior of COVID-19 patients and progression of disease, including death. We used random effects meta-analysis, meta-regression and locally weighted regression and smoothing to examine relationships in the data.

Results

We identified 46 peer-reviewed papers with a total of 22,939 COVID-19 patients, 5421 (23.6%) experienced disease progression and 2914 (12.7%) with a history of smoking (current and former smokers). Among those with a history of smoking, 33.5% experienced disease progression, compared with 21.9% of non-smokers. The meta-analysis confirmed an association between ever smoking and COVID-19 progression (OR 1.59, 95% CI 1.33–1.89, p  = 0.001). Ever smoking was associated with increased risk of death from COVID-19 (OR 1.19, 95% CI 1.02–1.39, p  = 0.003). We found no significant difference ( p  = 0.864) between the effects of ever smoking on COVID-19 disease progression between adjusted and unadjusted analyses, suggesting that smoking is an independent risk factor for COVID-19 disease progression. We also found the risk of having COVID-19 progression higher among younger adults ( p  = 0.001), with the effect most pronounced among younger adults under about 45 years old.

Conclusions

Smoking is an independent risk for having progression of COVID-19, including mortality. The effects seem to be higher among young people. Smoking prevention and cessation should remain a priority for the public, physicians, and public health professionals during the COVID-19 pandemic.

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  1. Version published to 10.1186/s12889-021-11579-x
  2. ScreenIT

    SciScore for 10.1101/2020.09.22.20199802: (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 variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data source and search strategy: We conducted a systematic search using PubMed and Embase on May 25, 2020, with the search term: “((smoking) OR (characteristics) OR (risk factors) OR (retrospective*) OR (outcomes) OR (smoker*)) AND ((COVID-19) OR (COVID) OR (coronavirus) OR (sars cov-2) OR (sars cov 2))” for studies published between January 1, 2020 and May 25, 2020.
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    A total of 2,600 studies were retrieved through PubMed and 1,962 studies through Embase.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)

    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 10.1101/2020.09.22.20199802 on medRxiv