The Impact of Allergy Specialty Care on Health Care Utilization Among Peanut Allergy Children in the United States
Bibliographic record
Abstract
BACKGROUND: The influence of allergist management on peanut allergy (PA)-related health care utilization is unknown. OBJECTIVE: To determine whether allergist care lowers PA costs. METHODS: IBM MarketScan Commercial Claims and Encounters Database was analyzed for PA diagnosis/reaction-related codes (January 2010-June 2019) in patients 64 years or younger, with demographically matched non-PA food allergy controls (NPAFACs). Outcomes were measured and compared 12 months before/after first claim date. RESULTS: Among 72,854 persons with PA (39,068 with ≥1 allergist visit, 53.6%), and 166,825 NPAFACs, the number of National Drug Codes and International Classification of Diseases, 10th Revision codes was higher for persons with PA with versus without an allergist visit during both baseline and follow-up (all P < .001). Persons with PA with versus without an allergist visit were prescribed epinephrine at significantly higher rates (RR, 1.67; P < .001). Rates of epinephrine claims, mean epinephrine costs, and proportion with peanut anaphylaxis were higher among the PA group with versus without an allergist visit (69.9% vs 63.3%; $676 vs $493, 48.9% vs 20.7%; all P < .001). The proportion with anaphylaxis episodes was higher in the PA group versus the NPAFAC group (53.1% vs 31.6%; P < .001). Total health care costs were higher in the NPAFAC group versus the PA group ($7863 vs $7261; P < .001) and lower for persons with PA with versus without an allergist visit ($6347 vs $8270; P < .001), with no significant differences in PA reaction-related costs between PA groups. CONCLUSIONS: Higher rates of anaphylaxis were seen among the PA group with versus without an allergist visit during the follow-up period (53.6% of overall PA group). Allergist care was associated with a reduction in total health care costs and higher rates of epinephrine prescription.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".