The prevalence of food allergy among Aboriginal people in Canada
Bibliographic record
Abstract
Data suggest that Aboriginal people may experience lower rates of food allergy compared with the general population. However, there have not been any population-based studies to estimate the prevalence of food allergy among Aboriginal People in Canada. Given this gap in the literature, the goal of this study is to estimate the prevalence of food allergy among Canadian Aboriginal people (First Nations, Métis or Inuit), and to compare these estimates with the general population. We performed a nationwide, cross-sectional telephone survey of all Canadian provinces and territories. Census Canada 2006 data were used to identify postal codes containing a high proportion of Aboriginal people and telephone numbers were randomly selected. The household respondent was queried on whether any household member had a food allergy. Prevalence estimates and 95% Confidence Intervals (CI) were calculated for the nine most common food allergens (peanut, tree nut, fish, shellfish, sesame, milk, egg, wheat, and soy) and for all foods, among individuals reporting Aboriginal status and for the general population. Out of 12,747 households contacted, 6,403 responded (50.2% response rate, representing 15,043 individuals), of which 2,264 reported Aboriginal status (15.1% of individuals). All allergies except peanut and fish are more common among the general population than Aboriginal people, although the difference is only significant for tree nut [1.22% (95% CI, 1.00%, 1.44%) vs. 0.57% (95% CI, 0.31%, 0.98%)], shellfish [1.60% (95% CI, 1.35%, 1.86%) vs. 0.93% (95% CI, 0.58%, 1.41%)], milk [1.97% (95% CI, 1.64%, 2.29%) vs. 0.49% (95% CI, 0.24%, 0.87%)], wheat [0.77% (95% CI, 0.57%, 0.96%) vs. 0.13% (95% CI, 0.03%, 0.39%)] and all foods [8.07% (95% CI, 7.47%, 8.67%) vs. 4.90% (95% CI, 4.05%, 5.87%)]. Our study suggests that the self-reported prevalence of several food allergies is lower in the Aboriginal population as compared with a representative sample of the general Canadian population. The lower prevalence of self-reported food allergy in the Aboriginal population may be attributable to several factors: genetics, differences in dietary habits and the environment, and inequities in access to health care, education and information about food allergy. These findings support a need for better education and access to health care services for Aboriginal communities, and future studies to explore the role of genetics, diet and environment in the development of food allergy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".