Recent Trends in Cigarette and HTP Use in Japan: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: The emergence and rapid increase in sales and use of heated tobacco products (HTPs) in Japan provides a unique case study of their viability as a potentially lower-risk substitute for combustible cigarettes. This review investigates the relationship between HTP and cigarette use in Japan. METHODS: We searched PubMed and Web of Science for studies on HTP and cigarette use, including sales trends, prevalence, and transitions between HTPs and cigarettes from 2010 to 2024. We distinguish results by source of funding and survey design. RESULTS: Our review included 25 relevant studies, of which 21 reported HTP and/or cigarette prevalence and transitions and 4 reported sales trends. Cigarette sales and use rapidly declined during the national expansion of HTPs. HTP use increased substantially from 2015 to about 2019, then slowed through 2023. Trends from industry-sponsored studies were mostly in line with the government-sponsored estimates. Estimates from government-sponsored (mostly in-person) surveys indicate that cigarette use continuously declined from 2015 to 2023 as HTP growth increased, although at a slower pace since 2018. After decreasing cigarette prevalence from 2015 to 2018, online surveys reported high rates of dual cigarette-HTP use and comparatively low rates of smoking discontinuation from 2018 to 2023. CONCLUSIONS: The rapid decline in cigarette use from 2015 to 2018 in Japan suggests that increasing HTP use may have contributed to this trend. After 2018, slowing HTP sales and mixed estimates of cigarette and HTP use raise uncertainty about the role of HTPs. As such, the evidence remains incomplete, limiting definitive conclusions. The current study highlights the challenges associated with distinguishing the impact of HTPs on displacing cigarettes. IMPLICATIONS: This review provides evidence that HTP use likely contributed to declines in cigarette use in Japan from 2015 to 2018, though recent trends are less conclusive. It highlights differences across data sources and survey types, which can affect how results are interpreted. The study adds to our understanding of how HTPs may or may not replace cigarettes over time and points to the need for better, more consistent data to track these trends.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".