Public health benefits from pictorial health warnings on US cigarette packs: a SimSmoke simulation
Bibliographic record
Abstract
Introduction While many countries have adopted prominent pictorial warning labels (PWLs) for cigarette packs, the USA still requires only small, text-only labels located on one side of the cigarette pack that have little effect on smoking-related outcomes. Tobacco industry litigation blocked implementation of a 2011 Food and Drug Administration9s (FDA) rule requiring large PWLs. To inform FDA action on PWLs, this study provides research-based estimates of their public health impacts. Methods Literature was reviewed to identify the impact of cigarette PWLs on smoking prevalence, cessation and initiation. Based on this analysis, the SimSmoke model was used to estimate the effect of requiring PWLs in the USA on smoking prevalence and, using standard attribution methods, on smoking-attributable deaths (SADs) and key maternal and child health outcomes. Results Available research consistently shows a direct association between PWLs and increased cessation and reduced smoking initiation and prevalence. The SimSmoke model projects that PWLs would reduce smoking prevalence by 5% (2.5%–9%) relative to the status quo over the short term and by 10% (4%–19%) over the long term. Over the next 50 years, PWLs are projected to avert 652 800 (327 000–1 190 500) SADs, 46 600 (17 500–92 300) low-birth-weight cases, 73 600 (27 800–145 100) preterm births and 1000 (400–2000) cases of sudden infant death syndrome. Conclusions Requiring PWLs on all US cigarette packs would be appropriate for the protection of the public health, because it would substantially reduce smoking prevalence and thereby reduce SADs and the morbidity and medical costs associated with adverse smoking-attributable birth outcomes.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".