Impact of binge alcohol on\n\t\t\t\t mortality among people who inject drugs
Bibliographic record
Abstract
While the impacts of illicit drug use on mortality have been well described, the impact of poly-substance that includes alcohol has received less attention. We examined the impact of binge alcohol use on mortality among a cohort of people who inject drugs (PWID) in a Canadian setting. Using data derived from a prospective cohort study of PWID in Vancouver, Canada recruited between May 1996 and November 2013. We ascertained dates and causes of death through a confidential linkage with the provincial registry and examined the impact of binge alcohol use. The primary outcome of interest was all-cause mortality. We used Cox proportional hazard regression to determine factors associated with mortality, including socio-demographic characteristics, drug use patterns and other risk behaviours. During the study period, 2550 individuals were followed (844 of whom were HIV positive at baseline) for a median of 75.4 months (interquartile range 37.9–113.2). Of these, 795 (31%) participants reported binge alcohol use at some time during the study period. In multivariable analyses, binge alcohol use remained independently associated with all-cause-mortality (adjusted hazard ratio = 1.41, 95% confidence interval: 1.06–1.88) after adjustment for other drug use patterns. Binge alcohol use was associated with time to all-cause mortality among PWID in this setting. Since alcohol use is often overlooked as a risk factor for mortality among this population, these findings highlight the continued need to incorporate addiction treatment and public health interventions and policies that address binge alcohol use to reduce alcohol related-harms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".