Prevalence and high-risk behaviors associated with non-fatal overdose among people who use illicit opioids: A systematic review and meta-analysis
Bibliographic record
Abstract
Background The aim of the present study was to determine the prevalence of non-fatal overdose among people who use illicit opioids (PWUIOs) and determine the demographic and behavioral characteristics associated with non-fatal overdose among this population.Methods Studies in English published before February 1, 2021, were searched for on PubMed, Scopus, Cochrane, and Web of Science to identify primary studies on the factors associated with non-fatal overdose among PWUIOs. After reviewing for study duplicates, the full-texts of selected papers were assessed for eligibility using PICOs criteria.Results After a detailed assessment of over 13,845 papers, a total of 67 studies met the eligibility criteria. The findings showed a past-year pooled prevalence rate of non-fatal overdose among PWUIOs of 26% (95% CI, 23% −29%). Greater odds of non-fatal overdose among PWUIOs was associated with (i) being younger, (ii) being female, (iii) being homeless, (iv) individuals who received money, goods, or drugs in exchange for sex, (v) individuals who had witnessed somebody else have an overdose, (vi) individuals who had used public spaces as primary injection locations, (vii) individuals who reported polydrug use, (vii) use of non-injection methamphetamine, and (viii) high injecting frequency (more than two daily drug injections).Conclusion Harm-reduction programs should consider the provision of education concerning overdose-related risk factors to the vulnerable and hard-to-reach PWUIOs.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".