Increasing availability of illicit and prescription opioids among people who inject drugs in a Canadian setting, 2010–2014
Bibliographic record
Abstract
BACKGROUND: Nonmedical use of prescription opioid and illicit opioid has been increasing at an alarming rate in North America over the past decade. OBJECTIVE: We sought to examine the temporal trends and correlates of the availability of illicit and prescription opioids among people who inject drugs (PWID) in Vancouver, Canada. METHODS: Data were derived from three prospective cohort studies of PWID in Vancouver between 2010 and 2014. In semiannual interviews, participants reported the availability of five sets of illicit and prescription opioids: (1) heroin; (2) Percocet (oxycodone/acetaminophen), Vicodin (hydrocodone/acetaminophen), or Demerol (meperidine); (3) Dilaudid (hydromorphone); (4) Morphine; (5) oxycontin/OxyNEO (controlled-release oxycodone). We defined perceived availability as immediate (e.g., available within 10 minutes) versus no availability/available after 10 minutes. The trend and correlation of immediate availability were identified by multivariable generalized estimating equations logistic regression. RESULTS: Among 1584 participants, of which 564 (35.6%) were female, the immediate availability of all illicit and prescribed opioids (except for oxycontin/OxyNEO) increased over time, independent of potential confounders. The Adjusted Odds Ratios of immediate availability associated with every calendar year increase were between 1.09 (95% confidence interval 1.05-1.12) (morphine and Dilaudid) and 1.13 (95% confidence interval 1.09-1.17) (Percocet/Vicodin/Demerol) (all p-values <0.05). CONCLUSION: The availability of most prescription opioids had continued to increase in recent years among our sample of PWID in Vancouver. Concurrent increases in the availability of heroin were also observed, raising concerns regarding combination of both illicit and prescription opioid use among PWID that could potentially increase the risk of overdose.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".