SARS-CoV-2 vaccination prevalence by mental health diagnosis: a population-based cross-sectional study in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Since the onset of the COVID-19 pandemic, there has been concern about the impact of SARS-CoV-2 infection among individuals with mental illnesses. We analyzed the SARS-CoV-2 vaccination status of Ontarians with and without a history of mental illness. METHODS: We conducted a population-based cross-sectional study of all community-dwelling Ontario residents aged 19 years and older as of Sept. 17, 2021. We used health administrative data to categorize Ontario residents with a mental disorder (anxiety, mood, substance use, psychotic or other disorder) within the previous 5 years. Vaccine receipt as of Sept. 17, 2021, was compared between individuals with and without a history of mental illness. RESULTS: Our sample included 11 900 868 adult Ontario residents. The proportion of individuals not fully vaccinated (2 doses) was higher among those with substance use disorders (37.7%) or psychotic disorders (32.6%) than among those with no mental disorders (22.9%), whereas there were similar proportions among those with anxiety disorders (23.5%), mood disorders (21.5%) and other disorders (22.1%). After adjustment for age, sex, neighbourhood income and homelessness, individuals with psychotic disorders (adjusted prevalence ratio 1.19, 95% confidence interval [CI] 1.18-1.20) and substance use disorders (adjusted prevalence ratio 1.35, 95% CI 1.34-1.35) were more likely to be partially vaccinated or unvaccinated relative to individuals with no mental disorders. INTERPRETATION: Our study found that psychotic disorders and substance use disorders were associated with an increased prevalence of being less than fully vaccinated. Efforts to ensure such individuals have access to vaccinations, while challenging, are critical to ensuring the ongoing risks of death and other adverse consequences of SARS-CoV-2 infection are mitigated in this high-risk population.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".