Cigarette prices, cigarette expenditure and smoking-induced deprivation: findings from the International Tobacco Control Mexico survey
Bibliographic record
Abstract
AIM: Mexico implemented annual tax increases between 2009 and 2011. We examined among current smokers the association of price paid per cigarette and daily cigarette expenditure with smoking-induced deprivation (SID) and whether the association of price or expenditure with SID varies by income. METHODS: We used data (n=2410) from three waves of the International Tobacco Control Mexico survey (ie, 2008, 2010, 2011) and employed logistic regression to estimate the association of price paid per cigarette and daily cigarette expenditure with the probability of SID ('In the last 6 months, have you spent money on cigarettes that you knew would be better spent on household essentials like food?'). RESULTS: Price paid per cigarette increased from Mex$1.24 in 2008, to Mex$1.36 in 2010, to Mex$1.64 in 2011. Daily cigarette expenditure increased from Mex$6.9, to Mex$7.6 and to Mex$8.4 in the 3 years. There was no evidence of an association between price and SID. However, higher expenditure was associated with a higher probability of SID. There was no evidence that the association of price or expenditure with SID varied by income. CONCLUSION: Tax increases in Mexico have resulted in smokers paying more and spending more for their cigarettes. Those with higher cigarette expenditure experience more SID, with no evidence that poorer smokers are more affected.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".