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Record W3021060057 · doi:10.1093/ntr/ntaa075

Smoking Cessation During the COVID-19 Epidemic

2020· article· en· W3021060057 on OpenAlexaff
S. Eisenberg, Mark J. Eisenberg

Bibliographic record

VenueNicotine & Tobacco Research · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Smoking cessation2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicineBetacoronavirusPandemicEnvironmental healthVareniclineVirologyOutbreakInternal medicineDisease

Abstract

fetched live from OpenAlex

The COVID-19 epidemic presents a unique public health opportunity for smoking cessation. Smokers are at a higher risk of developing COVID-19 and are also at a higher risk of developing severe COVID-19 complications.1 Although there are no data available regarding the benefits of smoking cessation during the COVID-19 epidemic, there is evidence to suggest that smoking cessation for 4 weeks or more will lessen the risk of developing COVID-19 as well as the risk of developing severe COVID-19 complications. Both smoking and COVID-19 affect the respiratory system. Smoking is known to increase the risk of lung cancer (relative risk [RR] 10.92; 95% confidence interval [CI] 8.28–14.20), chronic obstructive pulmonary disease (RR 4.01; 95% CI 3.18–5.05), and asthma (RR 1.61; 95% CI 1.07–2.42).2 In a study examining respiratory syncytial virus, a virus similar to SARS-CoV-2, it was shown that cigarette smoke causes necrosis of airway epithelial cells and prevents viral-induced apoptosis. Apoptosis usually limits viral replication and inflammation. However, when it is replaced with necrosis, both viral replication and inflammation are enhanced, leading to an increased susceptibility of acquiring viral infections.3 Furthermore, smokers often have more hand-to-face movements (compared with nonsmokers), making viral transmission more probable. Smokers also have an increased risk of developing severe complications once they become infected with SARS-CoV-2.1 A recent systematic review examined five studies that analyzed the smoking status of patients during the COVID-19 epidemic in China. The size of the patient population in all of these studies ranged from 41 to 1099 and the studies only included patients who were COVID-19 positive.1 The authors concluded that smokers (compared with nonsmokers) were 1.4 (RR 1.4; 95% CI 0.98–2.00) times more likely to suffer from severe symptoms of COVID-19. They were also 2.4 (RR 2.4; 95% CI 1.43–4.04) times more likely to be placed in the intensive care unit, require mechanical ventilation, or die.1 Another recent Chinese study published in the Lancet, compared the incidence of severe COVID-19 symptoms in 52 critically ill patients admitted to the intensive care unit. Comparing smokers to nonsmokers, 26 (81%) versus 9 (45%) had acute respiratory distress syndrome, 30 (94%) versus 7 (35%) required mechanical ventilation, 15 (29%) had heart failure, and 12 (23%) had kidney failure.4 Smokers are therefore more likely to acquire SARS-CoV-2 and are more likely to have adverse outcomes once the infection is acquired. Although there are limited data available, studies from the surgical literature suggest that even 4 weeks of smoking cessation may decrease the risk of adverse outcomes and intubation associated with COVID-19.5 In a study published in the Canadian Journal of Anesthesia in 2012, the authors conducted a systematic review and meta-analysis of 25 studies on short-term preoperative smoking cessation and postoperative complications. The authors of this study identified that at least 4 weeks of smoking cessation lowers the risk of respiratory complications compared with current smokers (RR 0.77; 95% CI 0.61–0.96 and RR 0.53; 95% CI 0.37–0.76).5 In another surgical study examining over 600 000 noncardiac surgical patients, current smokers had a higher likelihood of 30-day mortality (RR 1.38; 95% CI 1.11–1.72) and a higher incidence of postoperative complications such as surgical site infection (odds ratio [OR] 1.30; 95% CI 1.80–2.43), pneumonia (OR 2.09; 95% CI 1.80–2.43), unplanned intubation (OR 1.87; 95% CI 1.58–2.21), and septic shock (OR 1.55; 95% CI 1.29–1.87).6 Thus, based on data from the surgical literature, there is reason to conclude that 4 weeks of smoking cessation will be associated with a lower incidence of adverse events and intubation among COVID-19 patients. Physicians can play a crucial role in helping smokers quit smoking during the COVID-19 epidemic. Physicians can use telemedicine to advise their patients regarding smoking cessation and to recommend pharmacotherapy. In a study published in the Lancet in 2016, varenicline was shown to be the most effective pharmacotherapy for smoking cessation followed by bupropion and the nicotine patch.7 In this 12-week study of 8144 participants, patients treated with varenicline had better abstinence rates compared with those on placebo (OR 3.61; 95% CI 3.07–4.24), those using the nicotine patch (OR 1.68; 95% CI 1.46–1.93), and those using bupropion (OR 1.75; 95% CI 1.52–2.01). In addition, participants placed on bupropion and the nicotine patch achieved higher smoking cessation rates compared with those on placebo (OR 2.07; 1.75–2.45 and OR 2.15; 95% CI 1.82–2.54).7 The primary endpoint in the study was confirmed smoking cessation for weeks 9–12.7 These medications can be prescribed by the patient’s physician and can be delivered to their homes from the nearest pharmacy. Finally, physicians should also make their patients aware of behavioral therapy hotlines for smoking cessation. The National Cancer Institute offers these services on their website (smokefree.gov). Smoking cessation is likely to reduce the risk of developing COVID-19 as well as the likelihood of developing severe COVID-19 complications. For this reason, physicians should advise their patients to stop smoking immediately. A Contributorship Form detailing each author’s specific involvement with this content, as well as any supplementary data, are available online at https://academic.oup.com/ntr. None. None declared.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.215

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.364
GPT teacher head0.513
Teacher spread0.149 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations49
Published2020
Admission routes1
Has abstractno

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