Association of tobacco use with depressive symptoms in adults: Considerations of symptom severity, symptom clusters, and sex
Bibliographic record
Abstract
OBJECTIVE: We aim to assess the association between depressive symptoms, depressive symptom severity and symptom clusters with tobacco use. We will also evaluate sex differences in these associations. METHOD: This cross-sectional study used data from the National Health and Nutrition Examination Survey (2005-2018). Depressive symptoms were assessed using the Patient Health Questionnaire-9. Tobacco use was categorized into four groups: cigarette use, smoked tobacco products (pipes and cigars), smokeless tobacco products (chewing tobacco and snuff), and non-tobacco use (reference group). RESULTS: This study included 33,509 participants. Cigarette use was associated with a 0.83-unit increase in total PHQ-9 scores (95% CI = [0.63, 1.04]), and 1.73 times higher odds of reporting depressive symptoms (95% CI = [1.48, 2.02]) compared to non-tobacco use. However, the use of smoked and smokeless tobacco was not associated with depressive symptoms. In females, cigarette use showed a stronger association with total PHQ-9 scores (aCoef = 1.23, 95% CI = [0.92, 1.55]) than in males (aCoef = 0.45, 95% CI = [0.21, 0.69]). Additionally, female smoked tobacco users showed positive associations with both PHQ-9 scores and the presence of depressive symptoms, but this relationship was not observed in males. Furthermore, subgroup analysis revealed associations between cigarette use and cognitive-affective and somatic symptom clusters, as well as a relationship between the logarithm of total cigarette consumption and depressive symptoms. CONCLUSION: Cigarette use was associated with higher odds of depressive symptoms with females having a stronger association. Further studies are needed to replicate these findings and examine the underlying mechanisms.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".