Effects of variations in access to care for children with atopic dermatitis
Bibliographic record
Abstract
BACKGROUND: An estimated 50% of children in the US are Medicaid-insured. Some of these patients have poor health literacy and limited access to medications and specialty care. These factors affect treatment utilization for pediatric patients with atopic dermatitis (AD), the most common inflammatory skin disease in children. This study assesses and compares treatment patterns and healthcare resource utilization (HCRU) between large cohorts of Medicaid and commercially insured children with AD. METHODS: Pediatric patients with AD were identified from 2 large US healthcare claims databases (2011-2016). Included patients had continuous health plan eligibility for ≥6 months before and ≥12 months after the first AD diagnosis (index date). Patients with an autoimmune disease diagnosis within 6 months of the index date were excluded. Treatment patterns and all-cause and AD-related HCRU during the observation period were compared between commercially and Medicaid-insured children. RESULTS: A minority of children were evaluated by a dermatology or allergy/immunology specialist. Several significant differences were observed between commercially and Medicaid-insured children with AD. Disparities detected for Medicaid-insured children included: comparatively fewer received specialist care, emergency department and urgent care center utilization was higher, a greater proportion had asthma and non-atopic morbidities, high- potency topical corticosteroids and calcineurin inhibitors were less often prescribed, and prescriptions for antihistamines were more than three times higher, despite similar rates of comorbid asthma and allergies among antihistamine users. Treatment patterns also varied substantially across physician specialties. CONCLUSIONS: Results suggest barriers in accessing specialty care for all children with AD and significant differences in management between commercially and Medicaid-insured children. These disparities in treatment and access to specialty care may contribute to poor AD control, especially in Medicaid-insured patients.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.004 | 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".