Data_Sheet_1_Uptake of COVID-19 vaccination among community-dwelling individuals receiving healthcare for substance use disorder and major mental illness: a matched retrospective cohort study.docx
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 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 teacher head, 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".