Impact of tobacco control interventions on socioeconomic inequalities in smoking: review of the evidence
Bibliographic record
Abstract
OBJECTIVE: We updated and expanded a previous systematic literature review examining the impact of tobacco control interventions on socioeconomic inequalities in smoking. METHODS: We searched the academic literature for reviews and primary research articles published between January 2006 and November 2010 that examined the socioeconomic impact of six tobacco control interventions in adults: that is, price increases, smoke-free policies, advertising bans, mass media campaigns, warning labels, smoking cessation support and community-based programmes combining several interventions. We included English-language articles from countries at an advanced stage of the tobacco epidemic that examined the differential impact of tobacco control interventions by socioeconomic status or the effectiveness of interventions among disadvantaged socioeconomic groups. All articles were appraised by two authors and details recorded using a standardised approach. Data from 77 primary studies and seven reviews were synthesised via narrative review. RESULTS: We found strong evidence that increases in tobacco price have a pro-equity effect on socioeconomic disparities in smoking. Evidence on the equity impact of other interventions is inconclusive, with the exception of non-targeted smoking cessation programmes which have a negative equity impact due to higher quit rates among more advantaged smokers. CONCLUSIONS: Increased tobacco price via tax is the intervention with the greatest potential to reduce socioeconomic inequalities in smoking. Other measures studied appear unlikely to reduce inequalities in smoking without specific efforts to reach disadvantaged smokers. There is a need for more research evaluating the equity impact of tobacco control measures, and development of more effective approaches for reducing tobacco use in disadvantaged groups and communities.
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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 | 0.017 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".