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. 2022 Jul 4;10(7):1244.
doi: 10.3390/healthcare10071244.

Assessing Smoking Status and Risk of SARS-CoV-2 Infection: A Machine Learning Approach among Veterans

Alice B S Nono Djotsa  1   2   3 , Drew A Helmer  1   2 , Catherine Park  1   2   3 , Kristine E Lynch  4 , Amir Sharafkhaneh  1   2 , Aanand D Naik  1   5 , Javad Razjouyan  1   2 , Christopher I Amos  2
Affiliations

Assessing Smoking Status and Risk of SARS-CoV-2 Infection: A Machine Learning Approach among Veterans

Alice B S Nono Djotsa et al. Healthcare (Basel). .

Abstract

The role of smoking in the risk of SARS-CoV-2 infection is unclear. We used a retrospective cohort design to study data from veterans' Electronic Medical Record to assess the impact of smoking on the risk of SARS-CoV-2 infection. Veterans tested for the SARS-CoV-2 virus from 02/01/2020 to 02/28/2021 were classified as: Never Smokers (NS), Former Smokers (FS), and Current Smokers (CS). We report the adjusted odds ratios (aOR) for potential confounders obtained from a cascade machine learning algorithm. We found a 19.6% positivity rate among 1,176,306 veterans tested for SARS-CoV-2 infection. The positivity proportion among NS (22.0%) was higher compared with FS (19.2%) and CS (11.5%). The adjusted odds of testing positive for CS (aOR:0.51; 95%CI: 0.50, 0.52) and FS (aOR:0.89; 95%CI:0.88, 0.90) were significantly lower compared with NS. Four pre-existing conditions, including dementia, lower respiratory infections, pneumonia, and septic shock, were associated with a higher risk of testing positive, whereas the use of the decongestant drug phenylephrine or having a history of cancer were associated with a lower risk. CS and FS compared with NS had lower risks of testing positive for SARS-CoV-2. These findings highlight our evolving understanding of the role of smoking status on the risk of SARS-CoV-2 infection.

Keywords: SARS Coronavirus 2; machine learning; smoking; veteran.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Strobe diagram.
Figure 2
Figure 2
Most important variable selection with a cascade of machine learning methods.
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