Burden of illness of patients with allergic asthma versus non-allergic asthma
Bibliographic record
Abstract
OBJECTIVE: Allergic and non-allergic asthma share similar symptoms, but differ in that allergic asthma is triggered by inhaled allergens. This study compared healthcare resource utilization (HCRU) and costs between these groups using US employer-based claims data. METHODS: Health insurance claims from Truven Marketscan database (2002Q1-2010Q2) were analyzed. Included patients had ≥2 asthma diagnoses and ≥1 year of eligibility prior to and following the date of first asthma diagnosis. Patients with ≥1 diagnosis for allergic asthma and ≥1 diagnosis for other allergic conditions formed the allergic asthma cohort whereas patients without any of these diagnoses formed the non-allergic asthma cohort. Allergic and non-allergic asthma patients were matched 1:1. HCRU and costs during the study period were compared between cohorts using incidence rate ratios (IRR) and bootstrap methods. RESULTS: Sixty four thousand four hundred and seventy three allergic and non-allergic asthma patients were matched (mean age = 30; 57.1% female; mean CCI = 0.2), with 7.1% and 0.36% having received an allergy test during the baseline period, respectively. During the study period, allergic asthma patients had significantly more asthma-related pharmacy dispensings (IRR[95% CI] = 2.25[2.22-2.28], p < 0.001) and asthma-related outpatient visits (IRR[95% CI] = 2.29[2.27-2.32], p < 0.001). Allergic asthma patients incurred 39% greater per-patient-per-year all-cause costs (allergic: $4008; non-allergic: $2889, p < 0.001) and 79% greater asthma-related costs (allergic: $1063; non-allergic: $592, p < 0.001) than non-allergic asthma patients. CONCLUSIONS: These results indicate, even in a relatively healthy population, allergic asthma is associated with greater HCRU and costs. Guideline-recommended IgE allergy tests should be employed in distinguishing the two forms of asthma, to optimize patient management and reduce costs.
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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 | 0.000 |
| 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".