Relationships Between Smoking Behaviors and Cotinine Levels Among Two American Indian Populations With Distinct Smoking Patterns
Bibliographic record
Abstract
Introduction: Smoking prevalence, cigarettes per day (CPD), and lung cancer incidence differ between Northern Plains (NP) and Southwest (SW) American Indian populations. We used cotinine as a biomarker of tobacco smoke exposure to biochemically characterize NP and SW smokers and nonsmokers and to investigate factors associated with variation in tobacco exposure. Methods: American Indians (N = 636) were recruited from two different tribal populations (NP and SW) as part of a study conducted as part of the Collaborative to Improve Native Cancer Outcomes P50 project. For each participant, a questionnaire assessed smoking status, CPD, second-hand smoke exposure, and traditional ceremonial tobacco use; plasma and/or salivary cotinine was measured. Results: Cotinine levels were (mean ± 95% confidence interval [CI]) 81.6 ± 14.1 and 21.3 ± 7.3 ng/ml among NP smokers and non-mokers, respectively, and 44.8 ± 14.4 and 9.8 ± 5.8 ng/ml among SW smokers and nonsmokers, respectively. Cotinine levels correlated with CPD in both populations (p < .0001). Cotinine ≥15 ng/ml was measured in 73.4% of NP smokers and 47.8% of SW smokers and in 19.0% of NP nonsmokers and 10.9% of SW nonsmokers. Ceremonial traditional tobacco use was associated with higher cotinine among NP smokers only (p = 0.004). Second-hand smoke exposure was associated with higher cotinine among NP non-smokers (P < 0.02). More secondhand smoke exposure was associated with smoking more CPD in both populations (p = 0.03-0.29). Linear regression modeling mirrored these findings. Conclusions: High prevalence of smoking in the Northern Plains and high cotinine levels among nonsmokers in both regions highlights the tribal populations' risk for tobacco-related disease. Implications: There is a high prevalence of smoking in Northern Plains American Indians. Among Northern Plains and Southwest nonsmokers, relatively high cotinine levels, representative of high tobacco exposure, suggest considerable exposure to second-hand smoke. It is critical to highlight the extent of second-hand smoke exposure among the Northern Plains and Southwest American Indians and to enhance efforts to initiate smoke-free policies in tribal communities, which are not subject to state-level polices.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".