Dynamics of overdose and non-overdose mortality among people living with HIV amidst the illicit drug toxicity crisis in British Columbia
Bibliographic record
Abstract
We sought to characterize overdose and non-overdose mortality among PLWH amidst the illicit drug toxicity crisis in British Columbia, Canada. A population-based analysis of PLWH (age ≥19) in British Columbia accessing healthcare from April 1996 to March 2017 was conducted using data from the Seek and Treat for Optimal Prevention of HIV/AIDS (STOP HIV/AIDS) cohort linkage. Underlying causes of deaths were stratified into overdose and non-overdose causes. We compared (bivariate analysis) health-related characteristics and prescription history between PLWH died of overdose and non-overdose causes between April 2009 and March 2017. Among 9,180 PLWH, we observed 962 deaths (142 [14.7%] overdoses; 820 [85.2%] other causes). Compared to those who died from other causes, those who died of overdose were significantly younger (median age [Q, Q3]: 46 years [42, 52] vs. 54 years [48, 63]); had an indication of chronic pain (35.9% vs. 27.1%) and hepatitis C virus (64.8% vs. 50.4%), but fewer experienced hospitalization in the year before death. PLWH who died were most likely to be prescribed with opioids (>50%) and least likely with opioid agonist therapy (<10%) in a year before death. These findings highlight the syndemic of substance use, HCV, and chronic pain, and how the crisis is unqiuely impacting females and younger people.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".