Factors Associated with Binge Drinking Among People Who Inject Drugs in Montréal, Canada
Bibliographic record
Abstract
ABSTRACT Objectives: Binge drinking is frequent among people who inject drugs (PWID), and is a major contributor to drug morbidity, particularly when used in combination with other substances. The aim of this study was to identify demographic and behavioural characteristics associated with binge drinking among PWID, to better tailor preventive interventions. Methods: Data were drawn from the Hepatitis Cohort (HEPCO), a longitudinal cohort study of PWID from Montreal, Canada. Binge drinking was defined as reporting, in the past month, at least 4 (females) or 5 (males) drinks on one occasion. Frequent binge drinking was defined as reporting 5 binge drinking episodes in the last month. Multivariate logistic regressions with hierarchical entry of variables by block were used. Results: Among the 805 PWID included in this study (82% men, average 47 y old), 303 reported past-month binge drinking. Binge drinking was positively associated with use of noninjected coacine/crack [odds ratio (OR): 1.45 (1.07–1.98)] amphetamines and other psychostimulants [OR: 2.09 (1.40–3.15)], tranquilizers [OR: 1.72 (1.16–2.56)], and psychedelic drugs [OR: 1.85 (1.04–3.32)] while injected nonheroin opioids [OR: 0.66 (0.46–0.93)] and opioid agonist treatment [OR: 0.59 (0.42–0.84)] were negatively associated with this outcome. Frequent binge drinkers were less likely to be men [OR: 0.46 (0.21–0.96)] or to inject nonheroin opioids [OR: 0.45 (0.26–0.79)]. Conclusions: Types of drugs used differ between PWID reporting and those not reporting binge drinking. Opioid agonist treatment was negatively associated with binge drinking. PWID women seem to engage in more frequent binge drinking than men and might be a subpopulation that requires further investigation and targeted strategies. Introduction: La consommation excessive d’alcool est fréquente chez les personnes qui utilisent des drogues par injection (PUDI) et est un contributeur majeur de la morbidité associée aux drogues, particulièrement lorsque l'alcool est utilisé substances. Le but de cette étude était d’identifier les caractéristiques démographiques et comportementales associées à la consommation excessive d’alcool chez les PUDI, afin de mieux adapter les stratégies de prévention. Méthode: Les données sont extraites de la cohorte HEPCO, une cohorte longitudinale de PUDI de Montréal, Canada. La consommation excessive d’alcool a été définie comme la prise d’au moins quatre (femmes) ou cinq (hommes) consommations en une occasion dans le dernier mois. La consommation excessive fréquente était définie comme cinq épisodes dans le dernier mois. Des régressions logistiques multivariées avec entrée hiérarchique des variables par bloc ont été effectuées. Résultats: Parmi les 805 PUDI inclus dans cette étude (hommes : 82%, âge moyen 47 ans), 303 rapportaient une consommation excessive d’alcool dans le dernier mois. La consommation excessive d’alcool était positivement associée avec l’usage de cocaïne/crack non injecté (OR: 1.45 [1.07–1.98]), d’amphétamines et d’autres psychostimulants (OR: 2.09 [1.40–3.15]), de tranquillisants (OR: 1.72 [1.16–2.56]) et de drogues psychédéliques (OR: 1.85 [1.04–3.32]) alors que les opioïdes injectés autres que l’héroïne (OR: 0.66 [0.46–0.93]) et les traitements agonistes aux opioïdes (TAO) (OR: 0.59 [0.42–0.84]) étaient négativement associés avec cette issue. Le fait d’être un homme (OR: 0.46 [0.21–0.96]) ou de s’injecter des opioïdes autres que l’héroïne (OR: 0.45 [0.26–0.79]) étaient moins associés à la consommation excessive fréquente d’alcool. Conclusion: Notre étude suggère que le type de drogues utilisées diffère entre les PUDI rapportant ou non une consommation excessive d’alcool. Les TAO y étaient négativement associés à la consommation excessive d’alcool. Les femmes semblaient consommer plus fréquemment de l’alcool de manière excessive et pourraient possiblement être visée par des interventions ciblées.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".