Systematic review and meta-analysis to estimate the burden of fatal and non-fatal overdose among people who inject drugs
Bibliographic record
Abstract
Abstract Background People who inject drugs (PWID) have high overdose risk. To assess the burden of drug overdose among PWID in light of opioid epidemic-associated increases in injection drug use (IDU), we estimated rates of non-fatal and fatal overdose among PWID living in Organization for Economic Cooperation and Development (OECD) countries using data from 2010 or later. Methods PubMed, Psych Info, and Embase databases were systematically searched to identify peer-reviewed studies reporting prevalence or rates of recent (past 12 months) fatal or non-fatal overdose events among PWID in OECD countries. Data were extracted and meta-analyzed using random effects models to produce pooled non-fatal and fatal overdose rates. Results 57 of 13,307 identified reports were included in the review, with 33/57 studies contributing unique data and included in the meta-analysis. Other (24/57) studies presented overlapping data to those included in meta-analysis. The rates of non-fatal and fatal overdose among PWID in OECD countries were 24.74 per 100 person years (PY) (95% CI: 19.86 – 30.83; n=28; I 2 =98.5%) and 0.61 per 100 PY (95% CI: 0.32 – 1.16; n=8; I 2 =93.4%), respectively. The rate of non-fatal overdose was 27.79 in North American countries, 25.71 in Canada, 28.59 in the U.S., and 21.44 in Australia. Conclusion These findings suggest there is a fatal overdose for every 40 non-fatal overdose events among PWID in OECD countries. The magnitude of overdose burden estimated here underscores the need for expansion of overdose prevention and treatment programs and serves as a baseline estimate for monitoring success of such programs.
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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 | 0.022 | 0.059 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.048 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".