Prevalence and perceptions of flavour capsule cigarettes among adults who smoke in Brazil, Japan, Republic of Korea, Malaysia and Mexico: findings from the ITC surveys
Bibliographic record
Abstract
INTRODUCTION: The global market of flavour capsule cigarettes (FCCs) has grown significantly over the past decade; however, prevalence data exist for only a few countries. This study examined prevalence and perceptions of FCCs among adults who smoke across five countries. METHODS: Cross-sectional data among adults who smoked cigarettes came from the International Tobacco Control Policy Evaluation Project Surveys-Brazil (2016/2017), Japan (2021), Republic of Korea (2021), Malaysia (2020) and Mexico (2021). FCCs use was measured based on reporting one's usual/current brand or favourite variety has flavour capsule(s). Perceptions of the harmfulness of one's usual brand versus other brands were compared between those who used capsules versus no capsules. Adjusted logistic regression models examined correlates of FCC use. RESULTS: There were substantial differences in the prevalence of FCC use among adults who smoke across the five countries: Mexico (50.3% in 2021), Republic of Korea (31.8% in 2021), Malaysia (26.5% in 2020), Japan (21.6% in 2021) and Brazil (6.7% in 2016/2017). Correlates of FCC use varied across countries. Capsule use was positively associated with being female in Japan and Mexico, younger age in Japan, Republic of Korea and Malaysia, high education in Brazil, Japan and Mexico, non-daily smoking in Republic of Korea, and having plans to quit in Japan and Republic of Korea. There was no consistent pattern of consumer perceptions of brand harmfulness. CONCLUSION: Our study documented the high prevalence of FCCs in some countries, pointing to the need to develop and implement regulatory strategies to control these attractive products.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".