Alcohol Use Disorders Identification Test (AUDIT) and mortality risk: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: We summarise the evidence for an association between screening scores from the Alcohol Use Disorders Identification Test (AUDIT) and all-cause mortality. METHODS: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, prospective cohort studies reporting all-cause mortality risk by AUDIT scores (complete AUDIT-10 or AUDIT-C) were identified through MEDLINE, Embase, PubMed and Web of Science up to September 2016. Risk estimates were pooled using random effects meta-analyses. RESULTS: Seven observational studies with 18 920 observed deaths among 309 991 participants were identified. At-risk drinking (ie, hazardous/harmful consumption, AUDIT-10 ≥8 and AUDIT-C ≥4) was associated with elevated mortality risk after 2-10 years of follow-up (pooled relative risk (RR)=1.24, 95% CI 1.12 to 1.37) compared with moderate drinking (AUDIT-10=1-7, AUDIT-C=1-3). Compared to past year abstainers (AUDIT=0), moderate drinkers had a lower mortality risk (RR=0.75, 95% CI 0.71 to 0.79) in US Veterans and a similar mortality risk (RR=0.99, 95% CI 0.72 to 1.38) in population-based studies. Most data came from studies among Veterans using the short AUDIT-C in men and showed a dose-response relationship (RR=1.04, 95% CI 1.04 to 1.05 for each AUDIT-C score among drinkers). Data for women and young adults were scarce. CONCLUSION: AUDIT screening scores were associated with mortality risk. The association was differential depending on the population examined, which may be related to prevalence of former drinkers among current abstainers. Due to heterogeneity between studies and the small number of populations examined, generalisability may be limited.
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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 | 0.061 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.020 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".