Racial and Ethnic Distribution of Rheumatic Diseases in Health Systems of the National Patient-Centered Clinical Research Network
Bibliographic record
Abstract
OBJECTIVE: To evaluate the relative prevalence of 8 rheumatic and musculoskeletal diseases (RMDs) across racial and ethnic groups within the National Patient-Centered Clinical Research Network (PCORnet). METHODS: Electronic health records from participating PCORnet institutions and systems from January 1, 2013, to December 31, 2018, were used to identify adult patients with ≥ 2 diagnosis codes for rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), osteoporosis (OP), granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA), eosinophilic granulomatosis with polyangiitis (EGPA), giant cell arteritis (GCA), and Takayasu arteritis (TAK). Among those with race and ethnicity data available, we compared prevalence of RMDs by race and ethnicity. RESULTS: Data from 28,059,546 patients were available for analysis. RA was more common in patients who were American Indian or Alaska Native vs White, with a prevalence of 11.57 vs 10.11/1000 (odds ratio [OR] 1.15, 95% CI 1.09-1.22). SLE was more common in patients who were Black or African American (6.73/1000), American Indian or Alaska Native (3.82/1000), and Asian (3.39/1000) vs White (2.80/1000; OR 2.43, 95% CI 2.39-2.46; OR 1.39, 95% CI 1.25-1.53; OR 1.26, 95% CI 1.21-1.31, respectively). SLE was more common in patients who were Hispanic vs non-Hispanic (prevalence 3.93 vs 3.45/1000, OR 1.14, 95% CI 1.12-1.16). TAK was more common in patients who were Asian vs White (prevalence 0.05 vs 0.04/1000, OR 1.43, 95% CI 1.00-2.03). OP, RA, and the vasculitides were all more common in patients who were White vs Black or African American. CONCLUSION: These data provide important information on the prevalence of RMDs by race and ethnicity in the United States. PCORnet can be used as a reliable data source to study RMDs within a large representative population.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".