Illicit prescription opioid injection : prevalence, characteristics, and health outcomes among people who inject drugs in Vancouver, Canada
Bibliographic record
Abstract
Background: The use of prescription opioid (PO) painkillers has increased substantially in Canada and the United States over the previous two decades, bringing widespread public health concern related to non-medical PO use and overdose. Meanwhile, the injection of POs has become common among illicit drug-using populations. This thesis sought to outline the health outcomes associated with injecting POs; evaluate the impact of PO injection on non-fatal overdose; and identify the characteristics associated with PO injection among HIV-positive individuals who use injection drugs (IDU). Methods: A systematic search was undertaken to identify studies that assessed associations between PO injection and various health outcomes. Data-driven studies used longitudinal measures from the Vancouver Injection Drug User Study (VIDUS) or the AIDS Care Cohort to Evaluate Exposure to Survival Services (ACCESS). Generalized estimating equations (GEEs) were used to estimate the effect of PO injection (with and without concurrent heroin use) on non-fatal overdose (study 1) and to identify exposures and clinical characteristics independently associated with PO injection among HIV-positive IDU (study 2). Results: Among 31 articles included in a systematic review, several health outcomes were identified in relation to PO injection. Between December 2005 and May 2014, study 1 followed 1660 IDU, of whom a median of 24.5% reported recent PO injection. In a multivariable GEE analysis, injecting heroin or both POs and heroin, but not PO alone, significantly increased the odds of overdosing compared to injecting non-opioids. Between December 2005 and November 2013, study 2 followed 634 HIV-positive IDU. In a multivariable GEE analysis, periods of PO injection were positively associated with Caucasian ethnicity, heroin injection and drug dealing, and negatively associated with older age and methadone maintenance treatment. Conclusions: Findings of this research highlight a constellation of social and drug-related vulnerabilities associated with injecting POs, emphasizing the importance of considering PO injection in current harm reduction strategies. These results further strengthen the rationale for expanding several harm reduction-based interventions (e.g., supervised injection sites, take-home naloxone, methadone maintenance treatment and alternatives), and integrating them into current health care treatment for IDU.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".