E-cigarettes and youth: Patterns of use, potential harms, and recommendations
Bibliographic record
Abstract
Electronic cigarette (e-cigarette) use has risen to unprecedented levels among youth in the United States. In this review, we discuss the patterns of use underlying the current youth vaping epidemic, potential harms from e-cigarette use, and the regulatory, public health , and clinical responses to e-cigarette use among youth. Between 2017 and 2018, past 30-day use of nicotine e-cigarettes among high school seniors nearly doubled, from 11% to 21%, representing the largest recorded increase for any adolescent substance use in over four decades. There are concerns that e-cigarette use could renormalize smoking behaviors, lead to the uptake of conventional cigarette use by youth, and have adverse effects in the developing brain and lungs of adolescents. Prevention and harm reduction efforts thus far have focused on policies to prevent youth access to vaping products and on public health strategies to expose the risks of youth vaping. However, it remains unclear if ongoing initiatives are sufficient to curb e-cigarette use by youth. Most health professionals agree that youth exposure to e-cigarettes needs to be addressed but feel uninformed, rely on unconventional information sources such as the media and their patients, and report that routine screening procedures concerning e-cigarettes are lacking. A coordinated effort from policy makers, public health agencies, parents, educators, health practitioners, and researchers is essential to mitigate harms from e-cigarette use in this vulnerable population. • E-cigarettes attract youth who would otherwise not smoke conventional cigarettes. • Newer “pod mod” devices (e.g. JUUL) are sleek, user-friendly, and easy to conceal. • A recent outbreak of lung injury, mainly in younger individuals, is vaping-related. • Regulatory, public health, and clinical approaches are needed to minimize harms. • Future research should evaluate adolescent-targeted policies and interventions.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".