Association of flavoured cigar regulations with wholesale tobacco volumes in Canada: an interrupted time series analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".