The health, economic and social burden of smoking in Argentina, and the impact of increasing tobacco taxes in a context of illicit trade
Bibliographic record
Abstract
Tobacco tax increases, the most cost-effective measure in reducing consumption, remain underutilized in low and middle-income countries. This study estimates the health and economic burden of smoking in Argentina and forecasts the benefits of tobacco tax hikes, accounting for the potential effects of illicit trade. Using a probabilistic Markov microsimulation model, this study quantifies smoking-related deaths, health events, and societal costs. The model also estimates the health and economic benefits of different increases in the price of cigarettes through taxes. Annually, smoking causes 45,000 deaths and 221,000 health events in Argentina, costing USD 2782 million in direct medical expenses, USD 1470 million in labor productivity loss costs, and USD 1069 million in informal care costs-totaling 1.2% of the national gross domestic product. Even in a scenario that considers illicit trade of tobacco products, a 50% cigarette price increase through taxes could yield USD 8292 million in total economic benefits accumulated over a decade. Consequently, raising tobacco taxes could significantly reduce the health and economic burdens of smoking in Argentina while increasing fiscal revenue.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".