COVID-19 Hospitalizations, Intensive Care Unit Stays, Ventilation, and Death Among Patients With Immune-mediated Inflammatory Diseases Compared to Controls
Bibliographic record
Abstract
OBJECTIVE: To investigate coronavirus disease 2019 (COVID-19) hospitalization risk in patients with immune-mediated inflammatory diseases (IMIDs) compared with matched non-IMID comparators from the general population. METHODS: We conducted a population-based, matched cohort study using health administrative data from January to July 2020 in Ontario, Canada. Cohorts for each of the following IMIDs were assembled: rheumatoid arthritis (RA), psoriasis, psoriatic arthritis (PsA), ankylosing spondylitis, systemic autoimmune rheumatic diseases (SARDs), multiple sclerosis (MS), iritis, inflammatory bowel disease, polymyalgia rheumatica, and vasculitis. Each patient was matched with 5 non-IMID comparators based on sociodemographic factors. We compared the cumulative incidence of hospitalizations for COVID-19 and their outcomes between IMID and non-IMID patients. RESULTS: A total of 493,499 patients with IMID (417 hospitalizations) and 2,466,946 non-IMID comparators (1519 hospitalizations) were assessed. The odds of being hospitalized for COVID-19 were significantly higher in patients with IMIDs compared with their matched non-IMID comparators (matched unadjusted odds ratio [OR] 1.37, adjusted OR 1.23). Significantly higher risk of hospitalizations was found in patients with iritis (OR 1.46), MS (OR 1.83), PsA (OR 2.20), RA (OR 1.42), SARDs (OR 1.47), and vasculitis (OR 2.07). COVID-19 hospitalizations were associated with older age, male sex, long-term care residence, multimorbidity, and lower income. The odds of complicated hospitalizations were 21% higher among all IMID vs matched non-IMID patients, but this association was attenuated after adjusting for demographic factors and comorbidities. CONCLUSION: Patients with IMIDs were at higher risk of being hospitalized with COVID-19. This risk was explained in part by their comorbidities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".