Food allergies in developing and emerging economies: need for comprehensive data on prevalence rates
Bibliographic record
Abstract
Although much is known today about the prevalence of food allergy in the developed world, there are serious knowledge gaps about the prevalence rates of food allergy in developing countries. Food allergy affects up to 6% of children and 4% of adults. Symptoms include urticaria, gastrointestinal distress, failure to thrive, anaphylaxis and even death. There are over 170 foods known to provoke allergic reactions. Of these, the most common foods responsible for inducing 90% of reported allergic reactions are peanuts, milk, eggs, wheat, nuts (e.g., hazelnuts, walnuts, almonds, cashews, pecans, etc.), soybeans, fish, crustaceans and shellfish. Current assumptions are that prevalence rates are lower in developing countries and emerging economies such as China, Brazil and India which raises questions about potential health impacts should the assumptions not be supported by evidence. As the health and social burden of food allergy can be significant, national and international efforts focusing on food security, food safety, food quality and dietary diversity need to pay special attention to the role of food allergy in order to avoid marginalization of sub-populations in the community. More importantly, as the major food sources used in international food aid programs are frequently priority allergens (e.g., peanut, milk, eggs, soybean, fish, wheat), and due to the similarities between food allergy and some malnutrition symptoms, it will be increasingly important to understand and assess the interplay between food allergy and nutrition in order to protect and identify appropriate sources of foods for sensitized sub-populations especially in economically disadvantaged countries and communities.
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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 | 0.024 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".