Incidence and trends of non-fatal overdoses among people with and without HIV: a population-based cohort study in British Columbia, Canada (2012–2020)
Bibliographic record
Abstract
OBJECTIVES: Our study investigated the age-adjusted incidence rates of non-fatal overdoses by HIV status and sex, and examined trends over time. DESIGN: We used data from the Comparative Outcomes and Service Utilization Trends study, a population-based cohort study that includes clinical and administrative health data on virtually all people with HIV (PWH) and a 10% random sample of people without HIV in the province. SETTING: British Columbia, Canada. PARTICIPANTS: Between April 2012 and March 2020, 11 050 PWH (81.8% male) and 473 952 people without HIV (50.3% male) who were 19 years and older contributed 68 035 and 3 285 824 person years (PY) of follow-up, respectively. OUTCOME MEASURES: The primary outcome was age-adjusted incidence rates of non-fatal overdose events stratified by sex and HIV status. Trends over time were also assessed. RESULTS: Age-adjusted non-fatal overdose incidence rates among males with and without HIV were 36.4 and 3.12 per 1000 PY, respectively (incidence rate ratio (IRR) = 11.7, 95% CI 10.9 to 12.5). For females with and without HIV, the age-adjusted incidence rates were 61.4 and 2.33 per 1000 PY, respectively (IRR=26.3, 95% CI 24.0 to 28.7). Between 2013 and 2019 (calendar years with full-year data), the age-adjusted non-fatal overdose rate increased significantly among males and females without HIV but not among PWH. CONCLUSIONS: We observed a significantly higher non-fatal overdose rate among PWH compared to people without HIV. The rate was highest among females with HIV. These findings underline the need for policies and programmes oriented towards PWH to mitigate overdoses, especially for females.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".