Beneficios del impuesto a los cigarros en México: análisis por sexo y quintil de ingreso
Bibliographic record
Abstract
Objective: Estimate economic and health benefits, by sex and income quintile, of tax-based cigarette price increases in Mexico. Methods: An extended cost-effectiveness analysis (ECEA) model was used to estimate distributional benefits for women and men in the scenario of a 44% increase in the price of cigarettes (from 56.4 Mexican pesos [MX$] to MX$81.2 per pack), as a result of tripling the current specific excise tax (from MX$0.49/cigarette to MX$1.49/cigarette). The model was calibrated with official national information sources. Results: With a tax increase of one peso per cigarette, about 1.5 million smokers would quit (351 300 women and 1.1 million men). This would prevent approximately 630 000 smoking-attributable premature deaths. Reducing the burden of disease would save the health sector close to MX$42.8 billion and prevent more than 250 000 people (including 50 200 women smokers) from falling into poverty. It would also result in an additional MX$16.2 billion in revenue per year, of which the lowest income quintile would contribute less than 3% (1% for low-income women). Conclusions: The tobacco epidemic has clearly differentiated patterns between women and men, reflecting a gender component. While the tobacco tax in Mexico would have great benefits with respect to the current state of the epidemic, this could also contribute to the broader goal of social justice by reducing gender inequities.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".