Type of drug use and risky determinants associated with fatal overdose among people who use drugs: a meta-analysis
Bibliographic record
Abstract
Background We assessed sociodemographic variables, risky determinants, and type of drug use associated with fatal overdose among people who use drugs (PWUD).Methods Studies in English published from January 1, 1985 to May 1, 2021, were searched on PubMed, Scopus, Cochrane, and Web of Science to identify studies on variables associated with fatal overdoses among PWUDs. After reviewing for study duplicates, the full-text of selected articles were assessed for eligibility using Population, Intervention, Comparator, Outcomes (PICO) criteria: (i) population: PWUD; (ii) intervention: fatal overdose in the past year; (iii) comparator: PWUD who had not fatal overdose; (iv) outcome: fatal overdose in the last year and (v) study type: cross-sectional, cohort, and case–control studies.Results Out of 13,821 articles, 25 studies met eligibility criteria. Our findings showed socio-demographic determinants (younger age, marital status, being homeless, being male,) risky determinants (poor mental health, experience non-fatal overdose and needle sharing), and type of drug use (cocaine disorder, benzodiazepines disorder, alcohol disorder, psychostimulant disorder, polysubstance disorders, and heroin dependence), were significantly associated with fatal overdose among PWUD.Conclusions The present study data indicated that numerous characteristics were correlated with overdose-induced mortality. Such characteristics are certainly interrelated; however, each factor could potentially be targeted for intervention. The most particular reason for death was practicing illicit drug use, including opioids (e.g., heroin).Abbreviations PWUD: People who use drug; CI: Confidence intervals; NOS: Newcastle-Ottawa Scale; OR: Odds ratio; PICO: Population, Intervention, Comparator, Outcomes; PRISMA: Protocols of Systematic Reviews and Meta-Analyses; PWIDs: People who inject drugs; WHO: World Health Organization
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.055 |
| Bibliometrics | 0.007 | 0.007 |
| 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".