Racial/Ethnic Differences in Light of 100% Smoke-free State Laws: Evidence from Adults in the United States
Bibliographic record
Abstract
This study estimates racial/ethnic differences in the association between 100% smoke-free state laws and smoking, as well as self-reported health, to facilitate policy aimed at reducing disparities. Data pertain to adults aged 18 years and older, obtained from the public-use Behavioral Risk Factor Surveillance System (2002-2014). The authors exploit variation in the timing of 100% smoke-free state laws using a difference-in-differences model. Examining heterogeneity across racial/ethnic minority groups, the authors consider the association between smoke-free laws and the probability of being: a daily smoker (versus occasional); an occasional smoker (versus former); and at the top of the self-reported health scale (versus the bottom). The authors find that 100% smoke-free state laws were not correlated with smoking among women. Moreover, racial/ethnic minority men who smoked occasionally were less likely to quit than white men, and results suggest that smoke-free laws did not reduce these disparities. However, there is evidence that smoke-free laws reduced the probability of being a daily smoker for Asian and Hispanic/Latinx men, but not the probability of quitting or being at the top of the self-reported health scale. More generally, smoke-free laws were not associated with self-reported health, except that there may have been an improvement among nonsmoking American Indian/Alaska Native women. These findings underscore the importance of looking beyond average effects to consider how 100% smoke-free state laws impact racial/ethnic minorities. There is evidence that they reduced smoking and improved self-reported health for some groups, but a suite of tobacco control policies is necessary to reduce racial/ethnic disparities more broadly.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".