Individual and Combined Impact of Cigarette Smoking, Anxiety, and Mood Disorders on Asthma Control
Bibliographic record
Abstract
INTRODUCTION: Despite the availability of effective therapies, research indicates that more than 50% of asthmatics are poorly controlled. Poor asthma control has been linked to behavioral (i.e., cigarette smoking) and psychological factors (i.e., anxiety and depression). However, little is known about the individual versus combined impact of cigarette smoking and anxiety or mood disorders in adult asthmatics on asthma control. METHODS: A total of 796 confirmed adult asthma patients completed a sociodemographic and medical history interview and underwent a psychiatric interview using the Primary Care Evaluation of Mental Disorders. Asthma control was evaluated using the Asthma Control Questionnaire. RESULTS: After adjusting for age, sex, and dose of inhaled corticosteroids, general linear model analyses indicated a significant main effect of current smoking on asthma control (B [SE] = 0.156 [0.059], p = .008) and main effects of anxiety disorders (B [SE] = 0.408 [0.095], p = < .001) and mood disorders (B [SE] = 0.448 [0.098], p = < .001) on asthma control. Pack-years were not associated with asthma control, and there were no interaction effects of current smoking or pack-years with either anxiety or mood disorders on asthma control. CONCLUSIONS: Findings suggest that current smoking, having an anxiety disorder, and having a mood disorder are independently associated with poorer asthma control but that cumulative smoking history (i.e., pack-years) was not associated with worse asthma control. These results indicate that smoking cessation may have a positive impact on asthma control levels in spite of past smoking intensity and highlight the importance of interventions that target anxiety and mood disorders in adult asthmatics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".