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Association of flavoured cigar regulations with wholesale tobacco volumes in Canada: an interrupted time series analysis

2018· article· en· W2888391479 on OpenAlexaffabout
Michael Chaiton, Robert Schwartz, Gabrielle Tremblay

Bibliographic record

VenueTobacco Control · 2018
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsHealth CanadaOntario Tobacco Research UnitPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsInterrupted Time Series AnalysisTobacco controlInterrupted time seriesTobacco industryBusinessAdvertisingMedicinePublic healthMathematicsStatistics

Abstract

fetched live from OpenAlex

INTRODUCTION: This study examines the association of Federal Canadian regulations passed in 2009 addressing flavours (excluding menthol) in small cigars with changes in cigar sales. METHODS: Quarterly wholesale unit data as reported to Health Canada from 2001 through 2016 were analysed using interrupted time series analysis. Changes in sales of cigars with and without flavour descriptors were estimated. Analyses were seasonally adjusted. Changes in the flavour types were assessed over time. RESULTS: The Federal flavour regulations were associated with a reduction in the sales of flavoured cigars by 59 million units (95% CI -86.0 to -32.4). Increases in sales of cigars with descriptors other than flavours (eg, colour or other ambiguous terms) were observed (9.6 million increase (95% CI -1.3 to 20.5), but the overall level (decline of 49.6 million units (95% CI -73.5 to -25.8) and trend of sales of cigars (6.9 million units per quarter (95% CI -8.1 to -5.7)) declined following the ban. Sensitivity analysis showed that there was no substantial difference in effect over time comparing Ontario and British Columbia, suggesting that other provincial tobacco control legislation was not associated with the changes in levels. Analyses suggested that the level change was sensitive to the specification of the date. CONCLUSION: This study demonstrates that flavour regulations have the potential to substantially impact tobacco sales. However, exemptions for certain flavours and product types may have reduced the effectiveness of the ban, indicating the need for comprehensive, well-designed regulations.

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 score0.583
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.236
Teacher spread0.228 · 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

Citations15
Published2018
Admission routes2
Has abstractyes

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