Drug Use Patterns Predict Risk of Non-Fatal Overdose Among Street-Involved Youth in a Canadian Setting
Bibliographic record
Abstract
Background Non-fatal drug overdose is a major cause of morbidity among people who use drugs, although few studies have examined this risk among street-involved youth. We sought to determine the risk factors associated with non-fatal overdose among Canadian street-involved youth who reported illicit drug use. Methods Using data from a prospective cohort of street-involved youth in Vancouver, Canada, we identified youth without a history of overdose and employed Cox regression analyses to determine factors associated with time to non-fatal overdose between September 2005 and May 2012. Results Among 615 participants, 98 (15.9%) reported a non-fatal overdose event during follow-up, resulting in an incidence density of 7.67 cases per 100 person-years. In multivariate Cox regression analyses, binge drug use (adjusted hazard ratio [AHR] = 1.85; 95% confidence interval [CI] = 1.20 – 2.84), non-injection crystal methamphetamine use (AHR = 1.70; 95% CI = 1.12 – 2.58), non-injection prescription opiate use (AHR = 2.56; 95% CI = 1.36 – 4.82), injection prescription opiate use (AHR = 2.49; 95% CI = 1.40 – 4.45) and injection heroin use (AHR = 1.85; 95% CI = 1.14 – 3.00) were positively associated with time to non-fatal overdose. Social, behavioural and demographic factors were not significantly associated with time to non-fatal overdose event. Conclusions Rates of non-fatal overdose were high among street-involved youth. Drug use patterns, in particular prescription opiate use, were associated with overdose. These findings underscore the importance of addiction treatment and prevention efforts aimed at reducing the risk of overdose among youth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".