Heavy Alcohol Use and Suicidal Behavior Among People Who Use Illicit Drugs : A Cohort Study
Bibliographic record
Abstract
Background People who use illicit drugs (PWUD) are known to experience high rates of suicidal behavior. While heavy alcohol use has been associated with suicide risk, its impact on the suicidal behavior of PWUD has not been well characterized. Therefore, we examined the relationship between heavy alcohol use and suicidal behavior among PWUD in Vancouver, Canada. Methods Data are derived from two prospective cohort studies of PWUD in Vancouver, Canada, from 2005 to 2013. Participants completed questionnaires that elicited information regarding sociodemographics, drug use patterns, and mental health problems, including suicidal behavior. We used recurrent event survival analyses to estimate the independent association between at-risk/heavy drinking (based on National Institute of Alcohol Abuse and Alcoholism [NIAAA] criteria) and risk of incident, self-reported suicide attempts. Results Of 1,757 participants, 162 participants (9.2%) reported 227 suicide attempts over the 8-year study period, resulting in an incidence rate of 2.5 cases per 100 person-years. After adjusting for potential confounders, including intensive illicit drug use patterns, heavy alcohol use (adjusted hazard ratio [AHR] = 1.97; 95% confidence interval [CI] = 1.39, 2.78) was positively associated with an increased risk of suicidal behavior. Conclusions We observed a high burden of suicidal behavior among a community-recruited sample of PWUD. Heavy alcohol use predicted a higher risk of suicide attempt, independent of other drug use patterns. These findings demonstrate the need for evidence-based interventions to address suicide risk among PWUD, particularly those who are heavy consumers of alcohol.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".