ENDS advertising expenditures in English language media in the USA, 2015–2020
Bibliographic record
Abstract
BACKGROUND: Electronic nicotine delivery system (ENDS) advertising is associated with ENDS purchase and use. This study assessed trends in ENDS advertisement (ad) expenditures in the USA from 2015 to 2020 overall, by media channel and by advertiser. METHODS: Data came from Numerator, which conducts surveillance of ads and estimates expenditures. The estimates are dollars spent (adjusted to 2020) by the advertiser for each ad occurrence for print, radio, television and digital (online, mobile) media channels. ENDS ad expenditures were assessed by quarter, media channel and the top five advertisers based on ad occurrences. RESULTS: Overall ENDS ad expenditures increased from $38 million in 2015 to $217 million in 2019 before decreasing to a low of $22 million in 2020. By media channel, print expenditures led the channels with more than twice as much spent as television, four times more than radio and 10 times more than digital. By advertiser, JUUL led in ENDS ad expenditures from 2015 to 2020 with almost $189 million spent, followed by British American Tobacco (BAT, $105 million) and Imperial Tobacco ($62 million). CONCLUSIONS: Overall ad expenditures were relatively stable from 2015 to mid-2018 when large expenditures by JUUL and subsequent expenditures by BAT and Imperial Tobacco led to expenditure highs in 2019. E-cigarette and vaping-associated lung injury (EVALI), the JUUL self-imposed ad suspension and COVID-19 likely all played a role in advertising lows in 2020. The absence of popular Puff Bar brand ads from the traditional media channels studied highlights the importance of monitoring direct and indirect advertising on newer media channels like social media.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.005 | 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".