Systematic review and meta-analysis to estimate the burden of non-fatal and fatal overdose among people who inject drugs living in the U.S. and comparator countries: 2010 – 2023
Bibliographic record
Abstract
Abstract Background People who inject drugs (PWID) have high risk for overdose, but there are no current estimates of overdose rates in this population. We estimated the rates of non-fatal and fatal overdose among PWID living in the U.S. and comparator countries (Canada, Mexico, United Kingdom, Australia), and ratios of non-fatal to fatal overdose, using literature published 01/01/2010 – 09/29/2023. Methods PubMed, PsychInfo, Embase, and ProQuest databases were systematically searched to identify publications reporting prevalence or rates of recent (past 12 months) non- fatal and fatal overdose among PWID. Non-fatal and fatal overdose rates were meta-analyzed using random effects models. Risk of bias was assessed using an adapted quality assessment tool, and heterogeneity was explored using sensitivity analyses. Results Our review included 143 records, with 58 contributing unique data to the meta- analysis. Non-fatal and fatal overdose rates among PWID in the U.S. were 32.9 per 100 person- years (PY) (95% CI: 26.4 – 40.9; n=28) and 1.7 per 100 PY (95% CI: 0.9 – 3.2; n=4), respectively. Limiting the analysis to data collected after 2016 yielded a non-fatal rate of 41.0 per 100 PY (95% CI: 32.1 – 52.5; n=16) and a fatal rate of 2.5 per 100 PY (95% CI: 1.4 – 4.3; n=2) in the U.S. An estimated 5% of overdoses among PWID in the U.S. result in death. Among the analyzed countries, Australia had the lowest non-fatal and fatal overdose rates and the largest ratio of non-fatal to fatal overdose. Conclusion Findings demonstrate substantial burden of non-fatal and fatal overdose among PWID in the U.S. and comparator countries. Scale-up of interventions that prevent overdose mortality and investments in PWID health research are urgently needed.
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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 | 0.020 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.043 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".