Harmonization of alcohol use data and mortality across a multi‐national <scp>HIV</scp> cohort collaboration
Bibliographic record
Abstract
BACKGROUND: Alcohol use is measured in diverse ways across settings. Harmonization of measures is necessary to assess effects of alcohol use in multi-cohort collaborations, such as studies of people with HIV (PWH). METHODS: Data were combined from 14 HIV cohort studies (nine European, five North American) participating in the Antiretroviral Therapy Cohort Collaboration. We analyzed data on adult PWH with measured alcohol use at any time from 6 months before starting antiretroviral therapy. Five cohorts measured alcohol use with AUDIT-C and others used cohort-specific measures. We harmonized alcohol use as grams/day, calculated using country-level definitions of a standard drink. For Alcohol Use Disorders Identification Test (AUDIT-C), we used Items 1 (frequency) and 2 (number of drinks on a typical day). Where alcohol was measured in categories, we used the mid-point to calculate grams/day. We used multivariable Cox models to estimate associations of alcohol use with mortality. RESULTS: Alcohol use data were available for 83,424 PWH, 22,447 (27%) had AUDIT-C measures and 60,977 (73%) recorded the number of drinks/units per week/day. Of the sample, 19,150 (23%) were female, 54,006 (65%) had White ethnicity, and median age was 42 years. Median alcohol use was 0.3 g/day (interquartile range [IQR] 0-4.8) and 0 g/day (IQR 0-20) for those with and without AUDIT-C. There was a J-shaped relationship between grams/day and mortality, with higher mortality for PWH reporting no alcohol use (adjusted hazard ratio [aHR] 1.46; 95% CI: 1.23-1.72) and heavier (>61.0 g/day) alcohol use (aHR 1.92; 1.41-2.59) compared with 0.1-5.5 g/day among those with AUDIT-C measures. Associations were similar among those with non-AUDIT-C measures. CONCLUSIONS: Grams/day is a useful metric to harmonize diverse measures of alcohol use. Magnitudes of associations of alcohol use with mortality may differ by setting and measurement method. Higher mortality among those with heavier alcohol use strengthens the case for interventions to reduce drinking.
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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 | 0.130 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".