Vaporized Nicotine (E-Cigarette) and Tobacco Smoking Among People With HIV: Use Patterns and Associations With Depression and Panic Symptoms
Bibliographic record
Abstract
BACKGROUND: Vaporized nicotine (VN) use is increasing among people with HIV (PWH). We examined demographics, patterns of use, depression, and panic symptoms associated with VN and combustible cigarette (CC) use among PWH. METHODS: We analyzed VN use among PWH in care at 7 US sites. PWH completed a set of patient-reported outcomes, including substance use and mental health. We categorized VN use as never vs. ever with the frequency of use (days/month) and CC use as never, former, or current. We used relative risk regression to associate VN and CC use, depression, and panic symptoms. Linear regression estimated each relationship with VN frequency. Models were adjusted for demographics. RESULTS: Among 7431 PWH, 812 (11%) reported ever-using VN, and 264 (4%) reported daily use. Half (51%) of VN users concurrently used CC. VN users were more likely than those without use to be younger, to be White, and to report ever-using CC. PWH reporting former CC use reported ≥8.5 more days per month of VN use compared with never CC use [95% confidence interval (95% CI): 5.5 to 11.5 days/month] or current CC use (95% CI: 6.6 to 10.5 days/month). Depression (relative risk: 1.20 [95% CI: 1.02 to 1.42]) and panic disorder (1.71 [95% CI: 1.43 to 2.05]) were more common among PWH ever-using VN. Depression was common among PWH using VN (27%) and CC (22%), as was panic disorder (21% for VN and 16% for CC). CONCLUSION: Our study elucidated demographic associations with VN use among PWH, revealed the overlap of VN and CC use, and associations with depression/panic symptoms, suggesting roles of VN in self-medication and CC substitution, warranting further longitudinal/qualitative research.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".