Association between current cigarette prices and cessation behaviors among male adult smokers: findings from 2018 to 2020 ITC Vietnam surveys
Bibliographic record
Abstract
BACKGROUND: This study evaluated the impact of the tax increase in January 2019 on changes in intention to quit and the effect of cigarette prices on quit attempts and successful quitting among male cigarette smokers in Vietnam. METHODS: Data were derived from the ITC project in Vietnam, which included 1585 adult smokers at baseline (Wave 1, Aug-Oct 2018) followed up to waves 2 (Sep-Nov 2019) and 3 (Sep-Dec 2020). Generalized estimating equations regression was performed to estimate changes in the intention to quit. Multiple logistic regression analysis was used to evaluate the cigarette price of a cigarette pack in relation to quit attempts and successful quitting. RESULTS: The increase in cigarette tax in 2019 did not significantly increase the likelihood of the intention to quit. After the tax increase, 63.6% of participants who smoked made a quit attempt, and 27.6% successfully quit smoking in the follow-up waves. However, the price of a cigarette pack was not significantly associated with quit attempts and successful quitting. The study did not observe a significant impact of cigarette prices on quit attempts and successful quitting in all subgroups of household income. Factors associated with quit attempts included the number of cigarettes smoked and the intention to quit, while those associated with successful quitting included age, dual use of cigarettes and other tobacco products, and the intention to quit. CONCLUSION: Current cigarette prices were not associated with cessation behaviors even within the lowest household income group. Therefore, a sharp rise in cigarette tax is required to incentivize smokers to quit smoking.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".