Uptake of COVID-19 vaccination among community-dwelling individuals receiving healthcare for substance use disorder and major mental illness: a matched retrospective cohort study
Bibliographic record
Abstract
Introduction Patients with major mental illness (MMI) and substance use disorders (SUD) face barriers in accessing healthcare. In this population-based retrospective cohort study, we investigated the uptake of COVID-19 vaccination in Ontario, Canada among community-dwelling individuals receiving healthcare for major mental illness (MMI) and/or substance use disorders (SUD), comparing them to matched general population controls. Methods Using linked health administrative data, we identified 337,290 individuals receiving healthcare for MMI and/or SUD as of 14 December 2020, matched by age, sex, and residential geography to controls without such healthcare. Follow-up extended until 31 December 2022 to document vaccination events. Results Overall, individuals receiving healthcare for MMI and/or SUD ( N = 337,290) had a slightly lower uptake of first (cumulative incidence 82.45% vs. 86.44%; hazard ratio [HR] 0.83 [95% CI 0.82–0.83]) and second dose (78.82% vs. 84.93%; HR 0.77 [95% CI 0.77–0.78]) compared to matched controls. Individuals receiving healthcare for MMI only ( n = 146,399) had a similar uptake of first (87.96% vs. 87.59%; HR 0.97 [95% CI 0.96–0.98]) and second dose (86.09% vs. 86.05%, HR 0.94 [95% CI 0.93–0.95]). By contrast, individuals receiving healthcare for SUD only ( n = 156,785) or MMI and SUD ( n = 34,106) had significantly lower uptake of the first (SUD 78.14% vs. 85.74%; HR 0.73 [95% CI 0.72–0.73]; MMI & SUD 78.43% vs. 84.74%; HR 0.76 [95% CI 0.75–0.77]) and second doses (SUD 73.12% vs. 84.17%; HR 0.66 [95% CI 0.65–0.66]; MMI & SUD 73.48% vs. 82.93%; HR 0.68 [95% CI 0.67–0.69]). Discussion These findings suggest that effective strategies to increase vaccination uptake for future COVID-19 and other emerging infectious diseases among community-dwelling people with SUD are 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".