Tobacco taxes as a tobacco control strategy
Bibliographic record
Abstract
BACKGROUND: Increases in tobacco taxes are widely regarded as a highly effective strategy for reducing tobacco use and its consequences. METHODS: The voluminous literature on tobacco taxes is assessed, drawing heavily from seminal and recent publications reviewing the evidence on the impact of tobacco taxes on tobacco use and related outcomes, as well as that on tobacco tax administration. RESULTS: Well over 100 studies, including a growing number from low-income and middle-income countries, clearly demonstrate that tobacco excise taxes are a powerful tool for reducing tobacco use while at the same time providing a reliable source of government revenues. Significant increases in tobacco taxes that increase tobacco product prices encourage current tobacco users to stop using, prevent potential users from taking up tobacco use, and reduce consumption among those that continue to use, with the greatest impact on the young and the poor. Global experiences with tobacco taxation and tax administration have been used by WHO to develop a set of 'best practices' for maximising the effectiveness of tobacco taxation. CONCLUSIONS: Significant increases in tobacco taxes are a highly effective tobacco control strategy and lead to significant improvements in public health. The positive health impact is even greater when some of the revenues generated by tobacco tax increases are used to support tobacco control, health promotion and/or other health-related activities and programmes. In general, oppositional arguments that higher taxes will have harmful economic effects are false or overstated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".