Tobacco use and nicotine dependence among people living with HIV who drink heavily in South Africa: a cross-sectional baseline study
Bibliographic record
Abstract
BACKGROUND: People living with HIV (PLWH) who drink alcohol and use tobacco are particularly vulnerable to tobacco-induced diseases due to an already compromised immune system. This study investigated the prevalence and factors associated with tobacco use (cigarette and snuff) among PLWH who drink heavily. METHODS: Participants (n = 623) on antiretroviral therapy for HIV who reported heavy drinking using the Alcohol Use Disorders Identification Test (AUDIT) and AUDIT-C were recruited from six hospitals in Gauteng Province, South Africa. The Fagerström test was used to assess nicotine dependence. Chi Square tests and modified Poisson regression analyses were conducted to identify factors associated with tobacco use. RESULTS: Almost half of the participants reported ever smoking (44.0%; CI: 40.1-47.9) and about a quarter reported ever using snuff (25.5%; CI: 22.2-29.1). Current smokers and current snuff users comprised 27.3% (CI: 23.9-30.9) and 19.1% (CI: 16.2-22.3) of all participants respectively. Among current smokers, 37.9% (CI: 30.8-45.3) were moderately/highly dependent on nicotine. Current 'any tobacco product users' (ATPU: use cigarettes or snuff) were 45.4% (CI: 41.5-49.3) while 1.0% (CI: 0.4-2.0) currently used cigarettes and snuff. Adjusted regression analyses showed that, compared to males, females were less at risk of being: ever smokers (Relative Risk Ratio [RRR] = 0.33; CI: 0.27-0.41), current smokers (RRR = 0.18; CI: 0.12-0.25), and ATPU (RRR = 0.75; CI: 0.63-0.89) but were more at risk of ever snuff use (RRR = 5.23; CI: 3.31-8.25), or current snuff use (RRR = 26.19; CI: 8.32-82.40) than males. Ever snuff users (RRR = 1.32; CI: 1.03-1.70), current snuff users (RRR = 1.40; CI: 1.03-1.89) and ATPU (RRR = 1.27; CI: 1.07-1.51) were more at risk of reporting significant depressive symptoms. We found no significant associations between smoking status and years on ART and viral load. CONCLUSION: There is a high prevalence of cigarette and snuff use among PLWH who drink heavily. Tobacco use cessation interventions tailored specifically for this population and according to their tobacco product of choice are urgently needed given their vulnerability to ill-health.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".