Sex-based differences in rates, causes, and predictors of death among injection drug users in Vancouver, Canada
Bibliographic record
Abstract
In the present study, we sought to identify rates, causes, and predictors of death among male and female injection drug users (IDUs) in Vancouver, British Columbia, Canada, during a period of expanded public health interventions. Data from prospective cohorts of IDUs in Vancouver were linked to the provincial database of vital statistics to ascertain rates and causes of death between 1996 and 2011. Mortality rates were analyzed using Poisson regression and indirect standardization. Predictors of mortality were identified using multivariable Cox regression models stratified by sex. Among the 2,317 participants, 794 (34.3%) of whom were women, there were 483 deaths during follow-up, with a rate of 32.1 (95% confidence interval (CI): 29.3, 35.0) deaths per 1,000 person-years. Standardized mortality ratios were 7.28 (95% CI: 6.50, 8.14) for men and 15.56 (95% CI: 13.31, 18.07) for women. During the study period, mortality rates related to infection with human immunodeficiency virus (HIV) declined among men but remained stable among women. In multivariable analyses, HIV seropositivity was independently associated with mortality in both sexes (all P < 0.05). The excess mortality burden among IDUs in our cohorts was primarily attributable to HIV infection; compared with men, women remained at higher risk of HIV-related mortality, indicating a need for sex-specific interventions to reduce mortality among female IDUs in this setting.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".