Trends in opioid toxicities among people with and without opioid use disorder and the impact of the COVID-19 pandemic in Ontario, Canada: A population-based analysis
Bibliographic record
Abstract
BACKGROUND: Across Canada, the COVID-19 pandemic occurred amidst an ongoing drug toxicity crisis. Although elevated rates of substance-related harms have been observed nationally, it remains unknown if the pandemic state of emergency led to disproportionate increases in opioid toxicities among people with opioid use disorder (OUD) compared to those without. METHODS: We conducted a population-based repeated cross-sectional time series analysis of fatal and non-fatal opioid toxicities between January 1, 2014, and December 31, 2021, in Ontario, Canada. We used interventional autoregressive integrated moving average models to examine the impact of the pandemic on monthly rates of opioid toxicities per 100,000 Ontario residents stratified by people with and without OUD. RESULTS: We identified 80,296 opioid toxicities of which 53.5 % occurred among people with OUD. Among 52,052 unique individuals, 60.5 % were male and 46.2 % were 25-44 years old. Between January 2014 and December 2021, the rate of opioid toxicities increased from 2.6 to 10.5 per 100,000 (rate ratio [RR]=4.07). The magnitude of this increase differed among people with OUD (0.8 to 7.4 per 100,000; RR=9.35) and without OUD (1.8 to 3.1 per 100,000; RR=1.74). We observed a significant ramp increase in the overall rate of opioid toxicities following the declaration of the pandemic emergency in March 2020 (+0.19 per 100,000 monthly, 95 % CI: 0.029, 0.36, p = 0.021). In a stratified analysis, we found a similar ramp increase among people with OUD (+0.19 per 100,000 monthly, 95 % CI: 0.10, 0.28, p < 0.001); however, this was not observed among people without OUD (p = 0.95). CONCLUSIONS: The rate of opioid toxicities accelerated across Ontario following the pandemic-related state of emergency, with the majority of this increase among people with OUD. The important differences observed among people with OUD compared with those without, highlights the critical need for improved access to harm reduction and treatment interventions among this population.
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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 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".