Alcohol and Mortality: Combining Self-Reported (AUDIT-C) and Biomarker Detected (PEth) Alcohol Measures Among HIV Infected and Uninfected
Bibliographic record
Abstract
BACKGROUND: Unhealthy alcohol use may be particularly detrimental among individuals living with HIV and/or hepatitis C virus (HCV), and is often under-reported. Direct biomarkers of alcohol exposure may facilitate improved detection of alcohol use. METHODS: We evaluated the association of alcohol exposure determined by both self-report [Alcohol Use Disorders Identification Test-Consumption (AUDIT-C)] and a direct biomarker [phosphatidylethanol (PEth)], with mortality among HIV-infected and HIV-uninfected in the Veterans Aging Cohort Study-Biomarker Cohort. We considered PEth <8 ng/mL to represent no alcohol use. Alcohol exposure by AUDIT-C scores [0, 1-3/1-2 (men/women), 4-7/3-7 (men/women), 8-12] and PEth (<8, ≥8) was combined into categories to model the relationship of alcohol with mortality. Participants were followed from blood collection date for 5 years or until death within 5 years. RESULTS: The sample included 2344 (1513 HIV+; 831 uninfected) individuals, 95% men. During a median follow-up of 5 years, 13% died. Overall, 36% were infected with HCV (40% HIV+/HCV+, 27% HIV-/HCV+). Overall, 43% (1015/2344) had AUDIT-C = 0 (abstinence). Of these, 15% (149/1015) had PEth ≥8 suggesting recent alcohol exposure. Among those with AUDIT-C = 0, HCV+ individuals were more likely to have PEth ≥8. After controlling for age, sex, race, HIV, HCV, and HIV viral suppression, those with AUDIT-C = 0 but PEth ≥8 had the highest risk of mortality (adjusted hazard ratio 2.15, 95% confidence interval: 1.40 to 3.29). CONCLUSIONS: PEth in addition to self-report may improve detection of alcohol use in clinical settings, particularly among those at increased risk of harm from alcohol use. Individuals infected with HCV were more likely to under-report alcohol use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".