The Relationship Between Tobacco Use and Legal Document Gender-Marker Change, Hormone Use, and Gender-Affirming Surgery in a United States Sample of Trans-Feminine and Trans-Masculine Individuals: Implications for Cardiovascular Health
Bibliographic record
Abstract
PURPOSE: Transgender individuals smoke tobacco at disproportionately higher rates than the general U.S. population, and concurrent use of gender-affirming hormones (estrogen or testosterone) and tobacco confers greater cardiovascular (CV) risk. This study examines the relationship between tobacco use and legal document gender-marker change, and medical/surgical interventions for gender transition. METHODS: Data came from an Internet-based survey of U.S. trans-feminine (n = 631) and trans-masculine (n = 473) individuals. We used multivariable logistic regression to investigate the relationship between past 3-month tobacco use and legal document gender-marker change, hormone use, and gender-affirming surgery controlling for demographic covariates and enacted and felt stigma. RESULTS: Compared to trans-feminine participants, trans-masculine individuals reported significantly higher rates of lifetime (74.4% vs. 63.5%) and past 3-month tobacco use (47.8% vs. 36.1%), and began smoking at an earlier age (14.5 vs. 15.5 years). Trans-feminine smokers reported significantly more frequent and heavier use. Adjusting for demographic covariates and enacted and felt stigma, legal document gender-marker change was associated with lower tobacco-use odds among trans-feminine individuals, whereas gender-affirming surgery predicted lower smoking odds among trans-masculine individuals. There were no significant differences in tobacco use by hormone use status. CONCLUSION: In this study, trans-masculine individuals were more likely to smoke and trans-feminine individuals reported heavier use. It is concerning that individuals receiving hormones did not report lower smoking rates, given the elevated CV risk of this combination. This is a missed opportunity to intervene on a major public health issue and highlights the need for smoking cessation interventions in this population.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".