Exposure to e-cigarette TV advertisements among U.S. youth and adults, 2013–2019
Bibliographic record
Abstract
INTRODUCTION: E-cigarette advertising has been shown to increase e-cigarette awareness and use. Although e-cigarette marketing in the early 2010s has been well-documented, little is known about how it has changed in recent years in response to the regulatory scrutiny from the FDA and the Congress to combat youth vaping epidemic. This study aims to examine the exposure to e-cigarette TV advertising among youth and adults in the U.S. from 2013 to 2019, overall and by media market and brand. METHODS: Quarterly data on e-cigarette TV advertising exposure, measured by target rating points (TRPs), and expenditures from 2013 to 2019 were compiled from the StradegyTM of Kantar Media. Trends of quarterly e-cigarette advertising TRPs were reported by age group, market, and brand. RESULTS: Over the study period, overall exposure to e-cigarette TV advertising was higher among adults than among youth. E-cigarette advertising TRPs and expenditures were relatively stable, despite intermittent fluctuations, between 2013 Q1 and 2017 Q1 except for a one-time dip in 2015 Q3, followed by a sharp decline in 2017 Q2 and stayed low till the end of 2018. A resurgence of e-cigarette advertising TRPs occurred in 2019 Q1, led by the advertising from JUUL, Blu, and Vuse, which peaked in the third quarter of 2019, with quarterly TRPs reaching 316.8 for youth and 1,701.9 for adults, and quarterly advertising expenditure totaling $31 million. CONCLUSIONS: Significant variations, both over time and across media markets and brands, were observed for e-cigarette televised advertising between 2013 and 2019. Following a lull in TV advertising in 2017/18, major e-cigarette companies have substantially increased advertising of their products on American television since early 2019, resulting in a surge in exposure to e-cigarette advertising among both youth and adults. Our findings highlighted the importance of continued monitoring of e-cigarette advertising in the U.S.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".