Risk of non‐fatal overdose and polysubstance use in a longitudinal study with people who inject drugs in Tijuana, Mexico
Bibliographic record
Abstract
INTRODUCTION: Among people who inject drugs (PWID), polysubstance use has been associated with fatal and non-fatal overdose (NFOD). However, the risk of overdose due to the cumulative number of various recently used drug types remains unexplored. We estimated the risk of NFOD for different polysubstance use categories among PWID in Tijuana, Mexico. METHODS: Data came from 661 participants followed for 2 years in Proyecto El Cuete-IV, an ongoing prospective cohort of PWID. A multivariable Cox model was used to assess the cumulative impact of polysubstance use on the time to NFOD. We used the Cochran-Armitage test to evaluate a dose-response relationship between number of polysubstance use categories and NFOD. RESULTS: We observed 115 NFOD among 1029.2 person-years of follow-up (incidence rate: 11.2 per 100 person-years; 95% confidence interval [CI] 9.3-13.3). Relative to those who used one drug class, the adjusted hazard ratio of NFOD for individuals reporting using two drug classes was 1.11 (95% CI 0.69-1.79), three drug classes was 2.00 (95% CI 1.16-3.44) and for those reporting three compared to two was 1.79 (95% CI 1.09-2.97). A significant Cochran-Armitage trend test (P < 0.001) suggested a dose-response relationship. DISCUSSION AND CONCLUSIONS: Polysubstance use was associated with increased risk of NFOD with a dose-response relationship over 2 years. We identified a subgroup of PWID at high risk of NFOD who reported concurrent use of opioids, stimulants and benzodiazepines. Prioritising tailored harm reduction and overdose prevention interventions for PWID who use multiple substances in Tijuana is 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".