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Record W2912904794 · doi:10.1111/add.14558

Prevalence of awareness, ever‐use and current use of nicotine vaping products (NVPs) among adult current smokers and ex‐smokers in 14 countries with differing regulations on sales and marketing of NVPs: cross‐sectional findings from the ITC Project

2019· article· en· W2912904794 on OpenAlexafffundabout
Shannon Gravely, Pete Driezen, Janine Ouimet, Anne C K Quah, K. Michael Cummings, Mary E. Thompson, Christian Boudreau, David Hammond, Ann McNeill, Ron Borland, James F. Thrasher, Richard Edwards, Maizurah Omar, Sara C Hitchman, Hua‐Hie Yong, Tonatiuh Barrientos‐Gutiérrez, Marc C. Willemsen, Eduardo Bianco, Marcelo Boado, Fastone Goma, Hong Gwan Seo, Nigar Nargis, Yuan Jiang, Cristina Pérez, Geoffrey T. Fong

Bibliographic record

VenueAddiction · 2019
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsOntario Institute for Cancer ResearchUniversity of Waterloo
FundersFogarty International CenterPan American Health OrganizationHealth Research Council of New ZealandMedical Research CouncilCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchNational Cancer InstituteInstitut National Du CancerConsejo Nacional de Ciencia y TecnologíaNational Health and Medical Research CouncilFundação do CâncerCanadian Cancer Society
KeywordsNicotineMedicineEconomicsPsychiatry

Abstract

fetched live from OpenAlex

AIMS: This paper presents updated prevalence estimates of awareness, ever-use, and current use of nicotine vaping products (NVPs) from 14 International Tobacco Control Policy Evaluation Project (ITC Project) countries that have varying regulations governing NVP sales and marketing. DESIGN, SETTING, PARTICIPANTS AND MEASUREMENTS: A cross-sectional analysis of adult (≥ 18 years) current smokers and ex-smokers from 14 countries participating in the ITC Project. Data from the most recent survey questionnaire for each country were included, which spanned the period 2013-17. Countries were categorized into four groups based on regulations governing NVP sales and marketing (allowable or not), and level of enforcement (strict or weak where NVPs are not permitted to be sold): (1) most restrictive policies (MRPs), not legal to be sold or marketed with strict enforcement: Australia, Brazil, Uruguay; (2) restrictive policies (RPs), not approved for sale or marketing with weak enforcement: Canada, Malaysia, Mexico, New Zealand; (3) less restrictive policies (LRPs), legal to be sold and marketed with regulations: England, the Netherlands, Republic of Korea, United States; and (4) no regulatory policies (NRPs), Bangladesh, China, Zambia. Countries were also grouped by World Bank Income Classifications. Country-specific weighted logistic regression models estimated adjusted NVP prevalence estimates for: awareness, ever/current use, and frequency of use (daily versus non-daily). FINDINGS: NVP awareness and use were lowest in NRP countries. Generally, ever- and current use of NVPs were lower in MRP countries (ever-use = 7.1-48.9%; current use = 0.3-3.5%) relative to LRP countries (ever-use = 38.9-66.6%; current use = 5.5-17.2%) and RP countries (ever-use = 10.0-62.4%; current use = 1.4-15.5%). NVP use was highest among high-income countries, followed by upper-middle-income countries, and then by lower-middle-income countries. CONCLUSIONS: With a few exceptions, awareness and use of nicotine vaping products varied by the strength of national regulations governing nicotine vaping product sales/marketing, and by country income. In countries with no regulatory policies, use rates were very low, suggesting that there was little availability, marketing and/or interest in nicotine vaping products in these countries where smoking populations are predominantly poorer. The higher awareness and use of nicotine vaping products in high income countries with moderately (e.g. Canada, New Zealand) and less (e.g. England, United States) restrictive policies, is likely due to the greater availability and affordability of nicotine vaping products.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.004
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

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

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 head0.036
GPT teacher head0.292
Teacher spread0.256 · 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 teacher head, 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

Citations88
Published2019
Admission routes3
Has abstractyes

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