Prices, use restrictions and electronic cigarette use—evidence from wave 1 (2016) US data of the ITC Four Country Smoking and Vaping Survey
Bibliographic record
Abstract
AIMS: To determine if there are associations between changes in the explicit (i.e. price) and implicit (i.e. use restrictions in public places) costs of cigarettes and nicotine vaping products (NVPs) and their use patterns in the United States. METHODS: Data came from wave 1 (2016) US data of the ITC Four Country Smoking and Vaping Survey (ITC US 4CV1) and Nielsen Scanner Track database. A multiple logistic regression model was applied to estimate the likelihoods of NVP use (vaping at least monthly), cigarette/NVP concurrent use (vaping and smoking at least monthly) and switch from cigarettes to NVPs (had quit smoking < 24 months and currently vape) among ever smokers, conditioning upon cigarette/NVP prices, use restrictions and socio-demographics. RESULTS: Living in places where vaping is allowed in smoke-free areas was significantly associated with an increase in the likelihood of vaping [marginal effect (ME) = 0.17; P < 0.05] and the concurrent use of cigarettes and NVPs (ME = 0.11; P < 0.05). Higher NVP prices were associated with decreased likelihood of NVP use, concurrent use, and complete switch (P > 0.05). Higher cigarette prices were associated with greater likelihood of cigarette and NVP concurrent use (P > 0.05). Working in places where vaping is banned is associated with lower likelihood of vaping and NVP and cigarette concurrent use (P > 0.05). CONCLUSIONS: Higher prices for nicotine vaping products (NVPs) and vaping restrictions in public places are associated with less NVP use and less concurrent use of vaping and smoking. Public policies that increase prices for vaping devices and supplies (i.e. regulations, taxes) and restrict where vaping is allowed are likely to suppress vaping.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".