Understanding <scp>COVID</scp>‐19 Risk in Patients With <scp>Immune‐Mediated</scp> Inflammatory Diseases: A <scp>Population‐Based</scp> Analysis of <scp>SARS</scp>–<scp>CoV</scp>‐2 Testing
Bibliographic record
Abstract
OBJECTIVE: To investigate the incidence of and factors associated with SARS-CoV-2 testing and infection in immune-mediated inflammatory disease (IMID) patients versus matched non-IMID comparators from the general population. METHODS: We conducted a population-based, matched cohort study among adult residents from Ontario, Canada, from January 2020 to December 2020. We created cohorts for the following IMIDs: rheumatoid arthritis (RA), psoriasis, psoriatic arthritis, ankylosing spondylitis, systemic autoimmune rheumatic diseases, multiple sclerosis (MS), iritis, inflammatory bowel disease (IBD), polymyalgia rheumatica, and vasculitis. Each patient was matched with 5 patients without IMIDs based on sociodemographic factors. We estimated the incidence of SARS-CoV-2 testing and infection in IMID patients and non-IMID patients. Multivariable logistic regressions assessed odds of SARS-CoV-2 infection. RESULTS: We studied 493,499 patients with IMIDs and 2,466,946 patients without IMIDs. Patients with IMIDs were more likely to have at least 1 SARS-CoV-2 test versus patients without IMIDs (27.4% versus 22.7%), but the proportion testing positive for SARS-CoV-2 was identical (0.9% in both groups). Overall, IMID patients had 20% higher odds of being tested for SARS-CoV-2 (odds ratio 1.20 [95% confidence interval 1.19-1.21]). The odds of SARS-CoV-2 infection varied across IMID groups but was not significantly elevated for most IMID groups compared with non-IMID comparators. The odds of SARS-CoV-2 infection was lower in IBD and MS and marginally higher in RA and iritis. CONCLUSION: Patients across all IMIDs were more likely to be tested for SARS-CoV-2 versus those without IMIDs. The risk of SARS-CoV-2 infection varied across disease subgroups.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".