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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".