Effect of cigarette tax increase in combination with mass media campaign on smoking behaviour in Mauritius: findings from the ITC Mauritius Survey
Bibliographic record
Abstract
BACKGROUND: Mauritius has made great strides in adopting evidence-based tobacco control measures, including an increase in its cigarette excise tax and antitobacco mass media (Sponge) campaign. The primary objective of this study is to examine the combined effect of these measures on smoking behaviour. METHODS: This study used longitudinal data from the International Tobacco Control Mauritius Survey, 2009-2011. Waves 1 and 2 were conducted before the tax increase and wave 3 was conducted shortly after the Sponge campaign and 6 months after the cigarette excise tax increase. Generalised estimating equations were used to examine the effects of these two key tobacco control measures on smoking prevalence and the quantity of cigarettes smoked. RESULTS: The results showed that the combination of cigarette tax increase and the Sponge campaign had a significantly negative effect on the prevalence of smoking in Mauritius and the number of cigarettes smoked among continuing smokers. Specifically, the measures significantly reduced the odds of being a smoker (adjusted OR 0.88, 95% CI 0.81 to 0.97). For average daily cigarettes smoked, the measures had a significant reduction in cigarettes per day by about 6% (incidence rate ratios 0.94, 95% CI 0.89 to 0.99). CONCLUSIONS: The combination of policy measures significantly reduced the consumption of cigarettes in Mauritius. While these results are encouraging, these efforts must be part of a sustained effort to further reduce the smoking prevalence in Mauritius.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".