Trajectories of injection drug use among people who use drugs in Vancouver, Canada, 1996–2017: growth mixture modeling using data from prospective cohort studies
Bibliographic record
Abstract
BACKGROUND AND AIMS: Injection drug use patterns are known to change over time, although such long-term changes have not been well described. We sought to characterize longitudinal trajectories of injection drug use and identify associated factors. DESIGN: Data were derived from the Vancouver Injection Drug Users Study and AIDS Care Cohort to evaluate the Exposure to Survival Services study, two prospective cohorts involving people who inject drugs in Vancouver, Canada between 1996 and 2017. Growth mixture modeling was applied to identify distinct injection drug use trajectories. Multinomial logistic regression was used to identify baseline factors associated with each trajectory. SETTING: Canada. PARTICIPANTS: A total of 2057 participants who reported having used illicit drugs via injection in the past 6 months at the baseline visit were included in the study. The median time since first injection drug use at baseline was 14.8 years (quartile 1-quartile 3: 6.5-24.3). MEASUREMENTS: Information regarding self-reported injection drug use during the past 6 months was collected at baseline and semi-annually thereafter via interviewer-administered questionnaires. FINDINGS: Participants were followed for a median of 113.4 months (quartile 1-quartile 3: 63.4-161.7). Five trajectories were identified: persistent high frequency injection (507, 24.6%); high frequency injection with late decrease (374, 18.2%); gradual cessation (662, 32.2%); early cessation with late relapse (227, 11.0%); and early cessation (287, 14.0%). Factors found to be associated with distinct trajectories included: daily heroin injection, binge injection drug use, age, not being in a stable relationship and year of study enrollment. CONCLUSIONS: People who used drugs in Vancouver, Canada from 1996 to 2017 appeared to follow five drug use trajectories, ranging from persistent high frequency use to early cessation. Almost 25% of participants remained high-frequency injectors over the study period.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".