New Diagnoses of Juvenile Idiopathic Arthritis Early in the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Little is known about the rates of rheumatic disease diagnosis among children during the COVID-19 pandemic. We examined the impact of the pandemic on the diagnosis of juvenile idiopathic arthritis (JIA) in the United States. METHODS: We performed a historical cohort study using US commercial insurance data (2016-2021) to identify children aged <18 years without prior JIA diagnosis or treatment in the prior ≥12 months. New JIA diagnoses were identified using a combination of ICD-10-CM diagnosis codes, location, and timing of medical services. Crude rates with 95% confidence intervals (CIs) of JIA diagnosis per 100,000 enrolled children per quarter were estimated and stratified by age group, sex, region, JIA type, and uveitis. The incidence rate ratio (95% CI) for JIA diagnosis was estimated using Poisson regression, adjusted for various demographic variables. RESULTS: From 2018-2021, 643 children were diagnosed with JIA. Crude new JIA diagnoses per 100,000 children per quarter dropped from 2.62 (95% CI, 2.39-2.87) prepandemic to 1.94 (95% CI, 1.66-2.25) during the pandemic. Declines in JIA diagnosis were more apparent in the US Northeast and West regions and among children aged 6-11 years. After adjustment for covariates, JIA diagnoses fell by 30% during the pandemic compared with the prior 3 years (IRR, 0.70; 95% CI, 0.59-0.83). CONCLUSIONS: Compared with the prepandemic period, JIA was diagnosed 30% less often during the early pandemic among commercially insured children in the United States. More research is needed to understand the underlying reasons for these changes in JIA diagnosis and more recent trends.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".