Prevalence of lower harm perceptions of cigarette product characteristics: findings from 10 low-middle-income countries from the ITC project
Bibliographic record
Abstract
Background A major objective of FCTC Article 9, which calls for tobacco product regulation, is to eliminate or reduce tobacco industry product design strategies that have been shown to lead smokers to believe that some cigarettes are less harmful than others. However, nearly all of the studies documenting beliefs about harm perceptions have come from high-income countries; little is known whether the same misperceptions about harmfulness are present in low-middle income countries (LMICs)― where 80% of the world's smokers reside. This study measured the level of perceptions of harmfulness of light and menthol cigarettes among smokers from 10 LMICs of the International Tobacco Control (ITC) Project. Methods Cross-sectional analyses of ITC surveys in Bangladesh, Brazil, China, India, Kenya, Malaysia, Mauritius, Mexico, Thailand, and Zambia were conducted using the country's most recent survey wave (ranging from 2011 to 2016). Adult smokers were asked whether each of three design features— (1) light/low tar, (2) filters, and (3) menthol—were less harmful. Results The percentage of smokers with erroneous beliefs was variable but substantial: 'light cigarettes are less harmful': 21% (Mexico) to 66% (China); 'low tar cigarettes are less harmful': 41% (Zambia) to 71% (China); 'menthol cigarettes are less harmful': 11% (Brazil) to 52% (China); 'filters reduce harm': 36% (Mexico) to 82% (China); 'if a cigarette tastes lighter, it is less harmful': 39% (Zambia) to 71% (Kenya). [POH 10 LMICs ITC] Conclusions A substantial proportion of smokers in the 10 LMICs erroneously believe that light, low tar, and menthol cigarettes are less harmful, and that filters reduce harm. This is particularly disturbing in China, where prevalence of industry-induced misconceptions was the highest for 4 of the 5 measures. These findings point to the necessity of Article 9 regulations to restrict/ban product design features that mislead consumers about the harmfulness of tobacco products, particularly in LMICs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".