Tobacco smoke exposure and sleep: estimating the association of urinary cotinine with sleep quality
Bibliographic record
Abstract
Introduction A majority of studies on tobacco smoke exposure and sleep quality have relied on self-reported smoking, resulting in potential exposure misclassification and biases related to self-report. The objective of this study was to investigate associations between urinary cotinine, a biological marker of tobacco smoke exposure, and sleep quality measures, including sleep duration, sleep continuity or efficiency, sleep satisfaction and alertness during normal waking hours. Methods Using data on a national sample of 10 806 adults (aged 18–79 years) from the Canadian Health Measures Survey (2007–2013), we performed binary logistic regression analyses to estimate associations between urinary cotinine concentrations and sleep quality measures, while controlling for potential confounders. Additionally, we performed ordinal logistic regression to assess the association between urinary cotinine concentrations and increased number of sleep problems. Results Overall, 28.7% of adult Canadian survey respondents had urinary cotinine concentrations above the limit of detection (LOD), and the prevalence of each sleep problem ranged from 5.5% to 35.6%. Elevated urinary cotinine concentrations (quartile 4 vs. $lt;LOD) were associated with significantly higher odds of short or long sleep duration (OR = 1.41; 95% CI: 1.02–1.95; p-trend = .021), trouble falling or staying asleep (OR = 1.71; 95% CI: 1.28–2.27; p-trend = .003), sleep dissatisfaction (OR = 1.87; 95% CI: 1.21–2.89; p-trend = .011), and increased number of sleep problems (OR = 1.64; 95% CI: 1.19–2.26; p-trend = .001). Stronger associations were observed among females compared to males. Conclusion Using a biological marker of tobacco smoke exposure, our study contributes to the body of literature of toxic environmental exposures on sleep quality by supporting an association between tobacco smoke exposure and poorer sleep quality. To address the limitations of a cross-sectional study design and to better assess the temporality of tobacco smoke exposure and sleep quality, longitudinal studies are recommended.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".