What cigarette price would drive Vietnamese people who smoke to quit smoking? Findings from the 2019-2020 ITC Vietnam surveys
Bibliographic record
Abstract
BACKGROUND: Tobacco taxation is the most effective strategy for reducing tobacco consumption, yet it remains underused globally, especially in low- and middle-income countries. This study aimed to investigate the price that would lead Vietnamese people who smoke to quit smoking and examine the impact of non-tax tobacco control policies on this price. METHODS: Cross-sectional data from Waves 2 and 3 of the International Tobacco Control Project in Vietnam were analysed. The price to quit was assessed by a question 'What price for a pack of cigarettes would make you try to quit smoking?'. Tobit models were used to examine the association between non-tax policies (ie, noticing health warnings, anti-smoking advertising, use of cessation services and workplace smoke-free policies) and the price to quit. RESULTS: The weighted median of the price to quit for a cigarette pack was Vietnam dong (VND)20 000 (US$0.86), which doubled the weighted median of the purchased price of VND10 000 (US$0.43). If cigarette prices increased by VND2000 or VND5000, 27.4% and 42.8% of people who smoke would intend to quit smoking, respectively. Price increases that doubled or tripled current prices would lead 70.7% and 82.9% of people who smoke to consider quitting smoking, respectively. Smoke-free policies at workplace were associated with a lower price to quit. CONCLUSION: Given that the current cigarette prices are very low and affordable, substantial price increases are needed to motivate quitting. Adding specific taxes in addition to the existing ad valorem system could enhance the effectiveness of tobacco taxation.
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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 | 0.000 | 0.001 |
| 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".