Factors associated with premature mortality among young injection drug users in Vancouver.
Bibliographic record
Abstract
BACKGROUND: Young injection drug users (IDUs) may be at increased risk of premature mortality due to the health risks associated with injection drug use including overdoses and infections. However, there has been little research conducted on mortality causes, rates and associations among this population. We undertook this study to investigate patterns of premature mortality, prior to age 30 years, among young IDUs. METHODS: Since 1996, 572 young (< or = 29 years) IDUs have been enrolled in the Vancouver Injection Drug Users Study (VIDUS). Semi-annually, participants have completed an interviewer-administered questionnaire and have undergone serologic testing for HIV and hepatitis C (HCV). Mortality data have been continually updated through linkages with the Provincial Coroner's Office. Crude and age-specific mortality rates, standardized mortality ratios, and life expectancy measures were calculated using person-time methods. Predictors of mortality were identified using Cox regression analyses. FINDINGS: Twenty-two participants died prior to age 30 years during the follow-up period for an overall crude mortality rate of 1,368 per 100,000 person-years. Overall, young IDUs were 16.4 times (95% confidence interval [CI]; 9.1-27.1) more likely to die; young women IDUs were 54.1 times (95%CI; 29.6-90.8) and young men IDUs were 12.9 times (95%CI; 5.5, 25.3) more likely to die when compared to the Canadian non-IDU population of the same age. The leading observed cause of death among females was: homicide (N = 9); and among males: suicide (N = 3) and overdose (N = 3). In Cox regression analyses, factors associated with mortality were, HIV infection (Hazard Ratio [HR]: 4.55; CI: 1.92-10.80) and sex work (HR: 2.76; CI: 1.16-6.56). INTERPRETATION: Premature mortality was 13 and 54 times higher among young men and women who use injection drugs in Vancouver than among the general population in Canada. The majority of deaths among the women were attributable to homicide, suggesting that interventions should occur not only through harm reduction services but also through structural interventions at the legal and policy level.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".