The relationship of smoking and unhealthy alcohol use to HIV care retention and viral suppression: findings from a multisite cohort study
Bibliographic record
Abstract
OBJECTIVES: Tobacco smoking and alcohol use may negatively influence HIV care, but associations have not been examined across cohorts. DESIGN: Multisite international collaboration of cohort studies. METHODS: People with HIV (PWH) were included from 11 cohorts; 5 North American and 6 Western European. Exposures were harmonized smoking and alcohol measures (2010-2018). Loss to care was defined as not having 2+ HIV care visits (HIV RNA and/or CD4 measurement dates) at least 60 days apart, within 12 months following alcohol measure date; HIV viral nonsuppression was defined as >200 copies/ml. Adjusted prevalence ratios (PRs) were estimated using modified Poisson regression; pooled effect estimates and the heterogeneity measure ( I2 ).were derived from a random-effect meta-analysis. RESULTS: Among 83 102 PWH (87.4% male, 46.1% white); 43.7% currently smoked, 44.5% reported low/moderate drinking, 6.9% heavy drinking, 48.6% did not drink. PWH who currently smoked had higher risk of loss to care than nonsmoking PWH (pooled PR [95% CI] = 1.12 [1.08-1.16], I2 = 18.1%); those with heavy drinking had higher risk than those with low/moderate drinking (1.13 [1.03-1.25], I2 = 57.8%). PWH who currently smoked had higher risk of viral nonsuppression than nonsmoking PWH (1.44 [1.25-1.67], I2 = 90.6%); those reporting heavy drinking had higher risk than those with low/moderate drinking (pooled PR [95% CI] = 1.18 [1.02-1.37], I2 = 68.9%). PWH who reported heavy drinking and current smoking, in comparison to low/moderate alcohol use but no current smoking, had highest risk of viral nonsuppression (pooled PR [95% CI] =1.74 [1.37-2.22]), I2 = 81.8%. CONCLUSIONS: Smoking and unhealthy alcohol use were associated with HIV loss to care and viral nonsuppression, with variability between cohorts.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.010 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".