E-Cigarette Brand Trends in the United States: An Investigation of Data From a Youth and Young Adult Sample and the E-Cigarette Retail Market (2022)
Bibliographic record
Abstract
Background: Electronic cigarettes (e-cigarettes) remain the most used tobacco product among young people in the United States (US). Given the need for current data on popular e-cigarette products, the current study leverages data from a rapid surveillance survey of young people and examines whether the top e-cigarette brands identified from this source align with US market data. Methodology: Data were obtained from current e-cigarette users (N = 4145) participating in the Truth Continuous Tracker Online (CTO; a cross-sectional tracking survey of 15-24 year-olds sourced from the national Dynata panel) and NielsenIQ retail scanner data, collected in 2022 and aggregated by quarter (Q1, Q2, and Q3). The top 15 e-cigarette brands were determined from respondents' endorsement in the Truth CTO and ranked total sales in NielsenIQ in nominal dollars. Results: Overall, 58% of e-cigarette brands overlapped across the Truth CTO and NielsenIQ data (60% for Q1, 47% for Q2 and 67% for Q3). Pod-based (JUUL; VUSE) and disposable (Hyde; Breeze Smoke) brands appeared as top brands in both datasets. Top brands were fairly consistent within and across quarters; though, more variability was found in the Truth CTO, relative to NielsenIQ. Many top brands were disposable. Conclusions: Results suggest that data from rapid surveillance and retail data can be used complementarily to characterize the popular e-cigarette brands currently on the US market. Many of these popular e-cigarette brands have yet to receive marketing granted orders under the US Food and Drug Administration, suggesting the need to continue monitoring e-cigarette brands among young people.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".