Impact of cigarette price increase on health and financing outcomes in Vietnam
Bibliographic record
Abstract
Background: Vietnam had about 15 million male smokers in 2015. To reduce adult tobacco use in Vietnam through an increase in the excise tax of cigarettes, we conducted an extended cost-effectiveness analysis to examine the impact of two scenarios of cigarette price increases. Methods: We estimated, across income quintiles, the life-years gained, treatment cost averted, number of men avoiding catastrophic health expenditure and extreme poverty, and additional tax revenue under a 32% and a 62% increase in cigarette price through increased excise tax. We considered only male smokers as they constitute majority of the smokers. We used the average price elasticity of demand for cigarettes in Vietnam of -0.53. Results: Under both scenarios of price increase, men in the poorest quintile would gain about 2.8 times the life-years and avert 2.5 times the treatment cost averted by the richest quintile. With a 32% price increase, about 285,000 men would avoid catastrophic health expenditure; as a result, about 95,000 men, more than half of whom in the poorest quintile, would avoid falling into extreme poverty. In contrast to the distribution of health benefits, the extra revenue generated from men in the richest quintile would be 1.2 times that from the poorest quintile. With a 62% price increase, about 553,000 men would avoid catastrophic health expenditure, and about 183,000 men, more than half of whom in the poorest quintile, would avoid falling into extreme poverty. The extra revenue generated from men in the richest quintile would be 3.8 times that from the poorest quintile. Conclusions: Higher cigarette prices would particularly benefit the poorest income quintile of Vietnamese, in terms of health and financial outcomes. Thus, tobacco taxes are an effective way to improve health and reduce poverty in Vietnam.
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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 | 0.003 | 0.008 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".