The Potential Impact of Oral Nicotine Pouches on Public Health: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: Oral nicotine pouches (ONPs) are a new class of nicotine products. This scoping review summarizes evidence on ONPs and explores their potential public health impact. AIMS AND METHODS: We conducted a structured literature search for empirical studies across three electronic databases through January 10, 2024. Outcomes included ONP product characteristics, use patterns, beliefs and perceptions, toxicity, and marketing and sales. RESULTS: Sixty-two studies were included, 17 were industry-funded. Most studies were from the United States. While large variations across studies were observed in ONP youth prevalence estimates, nationally representative U.S. studies find current use at 1.5% and lifetime use below 2.5% through 2023. Between 35% and 42% of U.S. adolescents and young adults have heard of ONPs, and 9-21% of tobacco-naïve youth were susceptible to trying them. U.S. adult-use estimates varied widely (0.8%-3% current; 3%-16% lifetime use) and were limited to populations with a history of tobacco use. The chemical composition of ONPs suggests fewer harmful/potentially harmful compounds at lower levels than cigarettes and smokeless tobacco (SLT), except formaldehyde. Industry-funded studies find substantially less cytotoxicity compared to cigarettes and suggest that higher nicotine-strength ONPs can deliver nicotine at levels comparable to or higher than SLT or cigarettes, although with slower nicotine release than cigarettes. Evidence on the cytotoxicity of ONPs relative to SLT is mixed. CONCLUSIONS: ONPs appear to be less toxic than cigarettes and deliver comparable nicotine, presenting an alternative for combustible product users, although key data are mainly available from industry-funded studies. Data from independent research is critically needed. Industry marketing of ONPs may encourage initiation in youth and situational and dual use in adults. IMPLICATIONS: The review provides an initial assessment of the potential role of ONPs in harm reduction and aims to determine unintended consequences of their use (youth uptake and dual-use) and identify populations that disproportionately use the product. This information is essential for tobacco regulatory bodies in determining the net public health impact of nicotine pouches.
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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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".