Impact of vaping restrictions in public places on smoking and vaping in the United States—evidence using a difference‐in‐differences approach
Bibliographic record
Abstract
AIMS: To estimate whether and to what extent extending indoor smoking restrictions to include electronic cigarettes (ECs) impact the use of ECs and cigarette smoking among adults in the United States. DESIGN: Observational study using a linear probability model and applying a difference-in-differences analysis. SETTING: United States. PARTICIPANTS: People aged 18-54 who lived in US counties where comprehensive indoor smoking laws in bars, restaurants and private work-places have been in place prior to 2010 (n = 45 111 for EC use analysis, n = 75 959 for cigarette use analysis). MEASUREMENTS: Data on cigarette smoking, use of ECs and place of residence from the Tobacco Use Supplement of the Current Population Survey (TUS-CPS 2010-11, 2014-15 and 2018-19) were combined with the American Nonsmokers' Rights Foundation (ANRF) database of state and local indoor smoking and vaping restriction laws. FINDINGS: Indoor vaping restriction (IVR) coverage was not significantly associated with the likelihood of adult EC use [coefficient estimate = 0.001; 95% confidence interval (CI) = -0.009, 0.013, P-value = 0.783]. In addition, IVR coverage was not significantly associated with adult cigarette smoking (coefficient estimate = -0.00; 95% CI = -0.016, 0.015, P-value = 0.954). The non-significant results appeared in different socio-demographic subgroups. CONCLUSIONS: IVRs do not appear to decrease electronic cigarette use among US adults. There is no evidence that IVRs increase or decrease cigarette smoking among US adults.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".