Examining prevalence and correlates of smoking opioids in British Columbia: opioids are more often smoked than injected
Bibliographic record
Abstract
BACKGROUND: British Columbia (BC) is in the midst of an opioid overdose crisis. Since 2017, smoking illicit drugs has been the leading mode of drug administration causing overdose death. Yet, little is known about people who smoke opioids, and factors underlying choice of mode of administration. The study objectives are to identify the prevalence and correlates associated with smoking opioids. METHODS: The Harm Reduction Client Survey is a monitoring tool used by the BC Centre for Disease Control since 2012. This survey is disseminated to harm reduction sites across BC to understand drug use trends and drug-related harms. We examined data from the survey administered October-December 2019 and performed descriptive, univariate, and multivariate analyses to better understand factors associated with smoking opioids. RESULTS: A total of 369 people who used opioids in the past 3 days were included, of whom 251 (68.0%) reported smoking opioids. A total of 109 (29.5%) respondents experienced an overdose in the past 6 months; of these 79 (72.5%) smoked opioids. Factors significantly associated with smoking opioids were: living in a small community (AOR =2.41, CI =1.27-4.58), being a woman (AOR = 1.84, CI = 1.03-3.30), age under 30 (AOR = 5.41, CI = 2.19-13.40) or 30-39 (AOR = 2.77, CI = 1.33-5.78) compared to age ≥ 50, using drugs alone (AOR = 2.98, CI = 1.30-6.83), and owning a take-home naloxone kit (AOR = 2.01, CI = 1.08-3.72). Reported use of methamphetamines within the past 3 days was strongly associated with smoking opioids (AOR = 6.48, CI = 3.51-11.96). CONCLUSIONS: Our findings highlight important correlates associated with smoking opioids, particularly the recent use of methamphetamines. These findings identify actions to better respond to the overdose crisis, such as targeted harm reduction approaches, educating on safer smoking, advocating for consumption sites where people can smoke drugs, and providing a regulated supply of opioids that can be smoked.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".