Transformation of the tobacco product market in Japan, 2011–2023
Bibliographic record
Abstract
OBJECTIVE: This study updates a previous paper that examined trends in the sale of cigarettes and heated tobacco products (HTPs) in Japan between 2011 and part way through 2019. The current study includes complete unit sales data through 2023. METHODS: Data on cigarette and HTP sales were obtained from public sources available from the websites and stockholder reports for the Tobacco Institute of Japan, Philip Morris International and Japan Tobacco. We used joinpoint regression using the parametric method to test for trends in both per capita and total sales for the three outcome variables assessed between 2011 and 2023: (1) cigarette sales, (2) HTP sales and (3) combined cigarette and HTP sales. Joinpoint regression identifies changes in trends and estimates the annual per cent change (APC) for each trend segment. RESULTS: Between 2011 and 2023, per capita and total cigarette sales declined by 52.6% and 52.7%, respectively. From 2011 to 2015, per capita cigarette sales in Japan decreased -1.5% APC; from 2015 to 2018, the decline accelerated to -10.5% APC and continued to fall -7.3% APC between 2018 and 2023. Between 2016 and 2018, per capita HTP sales increased by 149.0% APC, and since 2018, they have increased by 8.1% APC. CONCLUSION: While many factors may account for the decreased sale of cigarettes in Japan over the past 12 years, the increased sale of HTPs appears to be a factor.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".