Insurance Status and Tumor Necrosis Factor Inhibitor Initiation Among Children With Juvenile Idiopathic Arthritis in the CARRA Registry
Bibliographic record
Abstract
OBJECTIVE: Prompt escalation to tumor necrosis factor inhibitors (TNFis) is recommended for children with juvenile idiopathic arthritis (JIA) and ongoing disease activity despite treatment with conventional disease-modifying antirheumatic drugs (cDMARDs). It is unknown whether these recommendations are equitably followed for children with different insurance types. We assessed the association of insurance coverage on the odds and timing of TNFi use. METHODS: We conducted a retrospective study of children with newly diagnosed JIA in the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry. We compared the odds of starting a TNFi in the first year and time from cDMARD to TNFi initiation between those with public and private insurance. RESULTS: We identified 1086 children with new JIA diagnoses. Publicly insured children had significantly higher active joint counts and parent/patient global assessment scores at the enrollment visit. They were also more likely to have polyarticular arthritis compared to those with private insurance. Odds of any TNFi use in the first year did not differ between publicly and privately insured children. Publicly insured children were escalated from cDMARD to TNFi more quickly than privately insured children. CONCLUSION: Children who were publicly insured had more severe disease and polyarticular involvement at registry enrollment compared to those who were privately insured. Whereas overall TNFi use did not differ between children with different insurance types, publicly insured children were escalated more quickly, consistent with their increased disease severity. Further research is needed to determine why insurance coverage type is associated with disease severity, including how other socioeconomic factors affect presentation to care.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".