E-Cigarette Market Trends in Traditional U.S. Retail Channels, 2012–2013
Bibliographic record
Abstract
INTRODUCTION: E-cigarette sales continue to increase in the United States. To date, little surveillance research has documented the specific product attributes driving growth. This study uses national market scanner data to describe sales trends in traditional U.S. tobacco retail channels between 2012 and 2013 and identifies product features associated with sales increases. METHODS: Data on e-cigarette sales in convenience stores, drug stores, grocery stores, and mass merchandisers in the United States were obtained from the Nielsen Company. Each product was coded for attributes such as brand, flavor, and unit size. Total sales volume, market share, and percent growth were calculated for various product attributes. RESULTS: E-cigarette sales more than doubled between 2012 and 2013, from $273.6 million to $636.2 million, respectively. Growth was particularly strong in the convenience store channel. Blu eCigs quickly emerged as the best-selling brand and in 2013 constituted nearly half (44.1%) of overall sales. Although fruit-flavored and other flavored products experienced marked growth, unflavored and menthol e-cigarettes overwhelmingly dominated the market. Sales of single unit products (likely disposable e-cigarettes) increased by 216.4%, a much faster rate than multi-unit packs and cartridge refills. CONCLUSIONS: In traditional U.S. retail channels, particularly the convenience store channel, sales of e-cigarettes continue to grow, with brands like blu and disposable products as the likely drivers. Given the rapidly-changing market, expanded surveillance is needed to monitor sales not only in traditional retail locations, but sales online and in specialty "vape shops," as well.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".