Association of Race and Ethnicity With COVID‐19 Outcomes in Rheumatic Disease: Data From the COVID‐19 Global Rheumatology Alliance Physician Registry
Bibliographic record
Abstract
OBJECTIVE: Racial/ethnic minorities experience more severe outcomes of coronavirus disease 2019 (COVID-19) in the general US population. This study was undertaken to examine the association between race/ethnicity and COVID-19 hospitalization, ventilation status, and mortality in people with rheumatic disease. METHODS: US patients with rheumatic disease and COVID-19 were entered into the COVID-19 Global Rheumatology Alliance physician registry between March 24, 2020 and August 26, 2020 were included. Race/ethnicity was defined as White, African American, Latinx, Asian, or other/mixed race. Outcome measures included hospitalization, requirement for ventilatory support, and death. Multivariable regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (95% CIs) adjusted for age, sex, smoking status, rheumatic disease diagnosis, comorbidities, medication use prior to infection, and rheumatic disease activity. RESULTS: A total of 1,324 patients were included, of whom 36% were hospitalized and 6% died; 26% of hospitalized patients required mechanical ventilation. In multivariable models, African American patients (OR 2.74 [95% CI 1.90-3.95]), Latinx patients (OR 1.71 [95% CI 1.18-2.49]), and Asian patients (OR 2.69 [95% CI 1.16-6.24]) had higher odds of hospitalization compared to White patients. Latinx patients also had 3-fold increased odds of requiring ventilatory support (OR 3.25 [95% CI 1.75-6.05]). No differences in mortality based on race/ethnicity were found, though power to detect associations may have been limited. CONCLUSION: Similar to findings in the general US population, racial/ethnic minorities with rheumatic disease and COVID-19 had increased odds of hospitalization and ventilatory support. These results illustrate significant health disparities related to COVID-19 in people with rheumatic diseases. The rheumatology community should proactively address the needs of patients currently experiencing inequitable health outcomes during the pandemic.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".