COVID-19 immunisation among individuals with opioid use disorder in Ontario: a population-based cohort study
Bibliographic record
Abstract
Background: The COVID-19 pandemic exacerbated health inequities, particularly among individuals with opioid use disorder (OUD). Disparities in vaccine uptake among people with OUD remain poorly understood. This study assessed COVID-19 immunisation rates among individuals with OUD compared with the general population in Ontario, Canada. Methods: This population-based retrospective cohort study used linked administrative health data to compare COVID-19 vaccination rates between individuals diagnosed with OUD and a 10% random sample of individuals without OUD. Ontario residents aged >15 years with continuous healthcare coverage as of the censor date, 16 March 2020, were included. Inverse Probability of Treatment Weighting (IPTW) was applied to balance confounders, and Cox proportional hazards models estimated adjusted HRs (aHRs) for receiving two and three or more vaccine doses. Results: The cohort included 105 733 individuals with OUD and 1 185 993 without OUD. Individuals with OUD had a lower hazard of receiving two vaccine doses (aHR: 0.75, 95% CI 0.73 to 0.76) and three or more doses (aHR: 0.69, 95% CI 0.67 to 0.70). The rate of two-dose and three-dose vaccination was also lower among those with OUD (115.3 vs 149.0 per 100 000 person-years and 44.7 vs 77.5 per 100 000 person-years). Conclusion: Individuals with OUD had lower COVID-19 vaccination rates, suggesting barriers to access and uptake. Addressing these disparities through targeted interventions is crucial for equitable public health responses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".