The impact of drug use patterns on mortality among polysubstance users in a Canadian setting : a prospective cohort study
Bibliographic record
Abstract
Background: Illicit drug use is a well-established risk factor for morbidity and mortality. However, few studies have examined the impact of different drug use patterns on mortality among polysubstance using populations. This study aimed to identify drug-specific patterns of mortality among a cohort of polysubstance using persons who inject drugs (PWIDs). Methods PWIDs in Vancouver, Canada were prospectively followed between May 1996 and December 2011. Participants were linked to the provincial vital statistics database to ascertain mortality rates and causes of death. We used multivariate Cox proportional hazards regression to investigate the relationships between drug use patterns (daily alcohol use, heroin injection and non-injection use, cocaine injection, amphetamine injection and non-injection use, crack smoking and speedball injecting) and time to all-cause mortality. Results 2330 individuals were followed for a median of 61 months (inter-quartile range: 33 – 112). In total, 466 (19.1%) individuals died for an incidence density of 3.1 (95% confidence interval [CI]: 2.8 – 3.4) deaths per 100 person-years. In multivariate analyses, after adjusting for HIV infection and other potential confounders, only daily cocaine injection remained independently associated with all-cause mortality (adjusted hazard ratio [AHR] = 1.36, 95% CI: 1.06 – 1.76). Conclusions Although heroin injecting is traditionally viewed as carrying the highest risk of mortality, in this setting, only daily cocaine injecting was associated with all-cause mortality. These findings highlight the urgent need to identify novel treatments and harm reduction strategies for cocaine injectors.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".