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Record W4366990153 · doi:10.1186/s12889-023-15351-1

The association between risk perceptions, anxiety, and self-reported changes in tobacco and nicotine product use due to COVID-19 in May-June 2020 in Israel

2023· article· en· W4366990153 on OpenAlexafffund
Noah G. Rubinson, Geoffrey T. Fong, Shannon Gravely, Anne C K Quah, Michal Bitan, Shahar Lev Ari, Laura Rosen

Bibliographic record

VenueBMC Public Health · 2023
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of WaterlooOntario Institute for Cancer Research
FundersCanadian Institutes of Health ResearchUniversity of WarwickTel Aviv UniversityOntario Institute for Cancer Research
KeywordsBiostatisticsMedicinePublic healthEnvironmental healthNicotinePandemicAnxietyMultinomial logistic regressionElectronic cigaretteCross-sectional studyCoronavirus disease 2019 (COVID-19)Logistic regressionEpidemiologyPopulationTobacco productDemographyPsychiatryDiseaseInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

BACKGROUND: Early in the COVID-19 pandemic, reports about a possible protective effect of nicotine on COVID-19 conflicted with messaging by public health organizations about increased risks of COVID-19 due to smoking. The ambiguous information the public received, combined with COVID-19-induced anxiety, may have led to changes in tobacco or other nicotine product use. This study examined changes in use of combustible cigarettes (CCs), nargila (hookah/waterpipe), e-cigarettes, and IQOS and home-smoking behaviors. We also assessed COVID-19 related anxiety and perceptions regarding changes in risk of COVID-19 severity due to smoking. METHODS: We used cross-sectional data from a population telephone survey that was conducted in Israel in the early phase of the COVID-19 pandemic (May-June 2020) and included 420 adult (age 18+) individuals who reported having ever used CCs (n = 391), nargila (n = 193), and/or electronic cigarettes (e-cigarettes)/heated tobacco products (e.g., IQOS) (n = 52). Respondents were asked about the effect that COVID-19 had on their nicotine product use (quit/reduced use, no change, increased use). We assessed changes in product use, risk perceptions, and anxiety using adjusted multinomial logistic regression analyses. RESULTS: Most respondents did not change their frequency of product use (CCs: 81.0%, nargila: 88.2%, e-cigarettes/IQOS: 96.8%). A small percentage either decreased use (CCs: 7.2%, nargila: 3.2%, e-cigarettes/IQOS:2.4%) or increased use (CCs:11.8%, nargila:8.6%, e-cigarettes/IQOS:+ 0.9%). 55.6% of respondents used a product in the home prior to COVID-19; but during the first lockdown COVID-19 period, a greater percentage increased (12.6%) than decreased (4.0%) their home use. Higher levels of anxiety due to COVID-19 were associated with increased home smoking (aOR = 1.59, 95% CI:1.04-2.42, p = 0.02). Many respondents believed that increased severity of COVID-19 illness was associated with CCs (62.0%) and e-cigarettes/vaping (45.3%), with uncertainty about the association being lower for CCs (20.5%) than for vaping (41.3%). CONCLUSIONS: While many respondents believed that nicotine product use (particularly CCs and e-cigarettes) was associated with increased risk of COVID-19 disease severity, the majority of users did not change their tobacco/nicotine use. The confusion about the relationship between tobacco use and COVID-19 calls for clear evidence-based messaging from governments. The association between home smoking and increased COVID-19-related stress suggests the need for campaigns and resources to prevent smoking in the home, particularly during times of stress.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.077
GPT teacher head0.367
Teacher spread0.290 · 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".

Quick stats

Citations6
Published2023
Admission routes2
Has abstractyes

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