Pediatric food allergy-related household costs are influenced by age, but not disease severity
Bibliographic record
Abstract
OBJECTIVE: The economic burden of food allergy on households is poorly understood. We evaluated the household costs associated with specialist-diagnosed pediatric food allergy, with focus on age and disease severity. STUDY DESIGN: A cross-sectional study of 70 Swedish case-control pairs (59% boys) was conducted using Food Allergy Economic questionnaire. Household costs were analyzed between age- and gender-matched cases (children aged 0-17 years, with specialist-diagnosed food allergy) and controls (non-food allergic households). RESULTS: Parents were predominantly university-educated and employed full-time. Most cases had parent-reported previous anaphylaxis. Mean total annual household costs were comparable between cases and controls. However, compared to controls, cases had significantly higher direct medical-, and non-medical related costs; higher indirect medical-related costs, and higher intangible costs (all p < 0.05). In a sensitivity analyses of only cases aged 0-12 years, direct household costs, including lost earnings due to child's hospitalization, were significantly higher than controls. Results from only children with severe disease paralleled those of all cases vs. controls. CONCLUSIONS: Although pediatric food allergy is not associated with higher total annual household costs, these households have significantly higher direct medical-related, indirect and intangible costs vs. non-food allergic households. Higher household costs were identified amongst younger children, but not disease severity.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".