Illicit trade in tobacco products: recent trends and coming challenges
Bibliographic record
Abstract
BACKGROUND: Illicit trade in tobacco products is a menace to the goal of eliminating tobacco consumption. Although tax policy is very effective in reducing consumption, illicit trade can reduce (though not eliminate) its effectiveness. METHODS: This article discusses the recent evolution of illicit trade and the context in which it occurred; the new methods that have been developed to measure it and, finally, the challenges in the next phase in the control of illicit trade. RESULTS: There has been a remarkable stability in the penetration of cigarette illicit trade in the past decade. Such a stability, however, occurred in a world of shrinking tobacco consumption, implying a decreasing absolute illicit trade. Most countries have progressed in increasing tobacco taxes and changing tax structures. Prices of illicit cigarettes follow legal cigarette prices. Concomitantly, many new studies, independent from the tobacco industry, have been conducted allowing for better understanding of the illicit trade and providing inputs to its solution. The entry into force of the WHO FCTC Protocol to Eliminate Illicit Trade in Tobacco Products provides both a global and a national policy framework to further curb illicit trade. Instruments such as track-and-trace systems must be promoted and adopted to maximise reductions in illicit trade. CONCLUSIONS: Global efforts to curb the illicit trade in tobacco products are gaining momentum and progress has been made in many parts of the world. The next decade can witness a decisive decrease in tobacco consumption, both licit and illicit, if countries further engage in international collaboration.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".