“To screen or not to screen”: Comparing the health and economic benefits of early peanut introduction strategies in five countries
Bibliographic record
Abstract
BACKGROUND: Early peanut introduction (EPI) in the first year of life is associated with reduced risk of developing peanut allergy in children with either severe eczema and/or egg allergy. However, EPI recommendations differ among countries with formal guidelines. METHODS: Using simulation and Markov modeling over a 20-year horizon to attempt to explore optimal EPI strategies applied to the US population, we compared high-risk infant-specific IgE peanut screening (US/Canadian) with the Australiasian Society for Clinical Immunology and Allergy (Australia/New Zealand) (ASCIA) and the United Kingdom Department of Health (UKDOH)-published EPI approaches. RESULTS: Screening peanut skin testing of all children with early-onset eczema and/or egg allergy before in-office peanut introduction was dominated by a no screening approach, in terms of number of cases of peanut allergy prevented, quality-adjusted life years (QALY), and healthcare costs, although screening resulted in a slightly lower rate of allergic reactions to peanut per patient in high-risk children. Considering costs of peanut allergy in high-risk children, the per-patient cost of early introduction without screening over the model horizon was $6556.69 (95%CI, $6512.76-$6600.62), compared with a cost of $7576.32 (95%CI, $7531.38-$7621.26) for skin test screening prior to introduction. From a US societal perspective, screening prior to introduction cost $654 115 322 and resulted in 3208 additional peanut allergy diagnoses. Both screening and nonscreening approaches dominated deliberately delayed peanut introduction. CONCLUSIONS: A no-screening approach for EPI has superior health and economic benefits in terms of number of peanut allergy cases prevented, QALY, and total healthcare costs compared to screening and in-office peanut introduction.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".