Mortality Among People With Opioid Use Disorder: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Opioids are among the most commonly used class of illicit drugs. We aimed to produce pooled estimates of mortality risks among people with opioid use disorder (OUD), with a focus upon all-cause mortality, and also overdose-specific causes of death. DESIGN: Systematic review and meta-analysis of cohorts of people with OUD involving illicit opioids with data on all-cause or overdose-specific mortality. SETTING AND PARTICIPANTS: Of 4247 papers, 92 were eligible, reporting on 101 cohorts that measured all-cause mortality and opioid-overdose mortality. Cohorts (n = 101-229,274) were in North America, Australia, several Eastern and Western European countries, and Asia. MEASUREMENT: Titles/abstracts and full texts were independently screened by 2 reviewers, with discrepancies resolved via a third reviewer. We extracted data on crude mortality rates (CMRs) per 1000 person-years (PY); we imputed CMRs where possible if not reported by study authors. We also calculated mortality relative risks. Data were pooled using random-effects models; potential reasons for heterogeneity were explored using subgroup analyses and meta-regressions. FINDINGS: The overall all-cause CMR was 18.7 per 1000 PY (95% confidence interval [CI] 17.1-20.3). The overall overdose-specific CMR was 7.0 per 1000 PY (95% CI 6.1-8.0). All-cause and overdose-specific mortality were substantially higher in low/middle-income countries, among those with HIV, and among people who use injection drugs. CONCLUSIONS: Individuals with OUD carry a high risk of all-cause and overdose-specific mortality. Potentially modifiable risk factors, such as HIV and injection drug use, were predictive of mortality risk and are amenable to global efforts aiming to improve access to OUD treatment and targeted harm reduction efforts.
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| 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 | 0.002 | 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".