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Record W4387357346 · doi:10.3310/rpdn7327

Effects of reduced-risk nicotine-delivery products on smoking prevalence and cigarette sales: an observational study

2023· article· en· W4387357346 on OpenAlexafffund
Francesca Pesola, Anna Phillips‐Waller, Emma Beard, Lion Shahab, David Sweanor, M. J. Jarvis, Peter Hájek

Bibliographic record

VenuePublic Health Research · 2023
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of Ottawa
FundersPublic Health Research ProgrammeUniversity of OttawaMedical Research CouncilNational Institute for Health and Care ResearchCancer Research UKPfizer
KeywordsSnusNicotineEnvironmental healthMedicineElectronic cigarettePopulationTobacco industryBusinessSmokeless tobaccoTobacco use

Abstract

fetched live from OpenAlex

Background: It is not currently clear what impact alternative nicotine-delivery products (electronic cigarettes, heated tobacco products and snus) have on smoking rates and cigarette sales. Objective: To assess whether access to these products promotes smoking in the population. Design and data sources: We examined associations of alternative nicotine product use and sales with smoking rates and cigarette sales overall, and in different age and socioeconomic groups, and compared smoking prevalence over time in countries with contrasting regulations of these products. For electronic cigarettes, we examined data from countries with historically similar smoking trajectories but differing current electronic cigarette regulations (United Kingdom and United States of America vs. Australia, where sales of nicotine-containing electronic cigarettes are banned); for heated tobacco, we used data from countries with state tobacco monopolies, where cigarette and heated tobacco sales data are available (Japan, South Korea), and for snus we used data from Sweden. Analysis methods: We pre-specified dynamic time series analyses to explore associations between use and sales of alternative nicotine-delivery products and smoking prevalence and cigarette sales, and time series analyses to compare trends of smoking prevalence in countries with different nicotine product policies. Results: Because of data and analysis limitations (see below), results are only tentative and need to be interpreted with caution. Only a few findings reached statistical significance and for most results the Bayes factor indicated inconclusive evidence. We did not find an association between rates of smoking and rates of the use of alternative nicotine products. The increase in heated tobacco product sales in Japan was accompanied by a decrease in cigarette sales. The decline in smoking prevalence seems to have been slower in Australia than in the United Kingdom overall, and slower than in both the United Kingdom and the United States of America among young people and also in lower socioeconomic groups. The decline in cigarette sales has also accelerated faster in the United Kingdom than in Australia. Limitations: Most of the available data had insufficient data points for robust time series analyses. The assumption of our statistical approach that causal interactions are more likely to be detected when longer-term changes are screened out may not apply for short time series and in product interaction scenarios, where short-term fluctuations can be caused by, for example, fluctuations in prosperity or product supplies. In addition, due to dual use, prevalence figures for smoking and alternative product use overlap. The ecological study design limits the causal inferences that can be made. Longer time periods are needed for any effects of exclusive use of the new products on smoking prevalence to emerge. Conclusions: We detected some indications that alternative nicotine products are competing with cigarettes rather than promoting smoking and that regulations that allow their sales are associated with a reduction rather than an increase of smoking, but the findings are inconclusive because of insufficient data points and issues with the assumptions of the pre-specified statistical analyses. Future work: As further prevalence and sales data emerge the analyses will become more informative. Accessing sales figures in particular is the current research priority. Study registration: The project is registered on Open Science Framework https://osf.io/bd3ah. Funding: ; Vol. 11, No. 7. See the NIHR Journals Library website for further project information.

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)0.010
metaresearch head score (Gemma)0.004
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 score0.031
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.471
GPT teacher head0.491
Teacher spread0.021 · 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

Citations9
Published2023
Admission routes2
Has abstractyes

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