A population-based study on peanut, tree nut, fish, shellfish, and sesame allergy prevalence in Canada
Bibliographic record
Abstract
BACKGROUND: Recent studies suggest an increased prevalence of food-induced allergy and an increased incidence of food-related anaphylaxis. However, prevalence estimates of food allergies vary considerably between studies. OBJECTIVES: To determine the prevalence of peanut, tree nut, fish, shellfish, and sesame allergy in Canada. METHODS: Using comparable methodology to Sicherer et al in the United States in 2002, we performed a cross-Canada, random telephone survey. Food allergy was defined as perceived (based on self-report), probable (based on convincing history or self-report of physician diagnosis), or confirmed (based on history and evidence of confirmatory tests). RESULTS: Of 10,596 households surveyed in 2008 and 2009, 3666 responded (34.6% participation rate), of which 3613 completed the entire interview, representing 9667 individuals. The prevalence of perceived peanut allergy was 1.00% (95% CI, 0.80%-1.20%); tree nut, 1.22% (95% CI, 1.00%-1.44%); fish, 0.51% (95% CI, 0.37%-0.65%); shellfish, 1.60% (95% CI, 1.35%-1.86%); and sesame, 0.10% (95% CI, 0.04%-0.17%). The prevalence of probable allergy was 0.93% (95% CI, 0.74%-1.12%); 1.14% (95% CI, 0.92%-1.35%); 0.48% (95% CI, 0.34%-0.61%); 1.42% (95% CI, 1.18%-1.66%); and 0.09% (95% CI, 0.03%-0.15%), respectively. Because of the infrequency of confirmatory tests and the difficulty in obtaining results if performed, the prevalence of confirmed allergy was much lower. CONCLUSION: This is the first nationwide Canadian study to determine the prevalence of severe food allergies. Our results indicate disparities between perceived and confirmed food allergy that might contribute to the wide range of published prevalence estimates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".