142: Iron Status of Young Children of Immigrant Families in Toronto
Bibliographic record
Abstract
Iron deficiency (ID) is the most common micronutrient deficiency in the world. Pre-school age children are a well-known high risk group for ID. Children from immigrant families may be at risk for ID due to a greater burden of acute and chronic infections, difficulty accessing health services, and cultural dietary practices. Conversely, such children may have family and community supports or exposure to selection and settlement policies when immigrating to Canada that improve their health status. To assess serum ferritin levels (a measure of iron stores) in young children from immigrant versus non-immigrant families; and to evaluate the relationship between immigrant status and ID and iron deficiency anemia (IDA) and whether known dietary, environmental, or biologic determinants of low iron status influence this relationship. This was a cross-sectional study of healthy urban children (12 to 72 months of age), recruited as part of TARGet Kids!, a primary care practice-based research network in Toronto, Canada. Laboratory assessment of serum ferritin and hemoglobin (Hgb) and standardized parent-completed surveys were completed between December 2008 and February 2013 during routine health maintenance visits. Multiple regression analyses were used to evaluate the association between immigrant status (Canadian born, immigrant from non-industrialized regions, immigrant from industrialized regions) and serum ferritin, ID (serum ferritin <14 μg/L), and IDA (serum ferritin <14 ug/L and Hgb ≤110 g/L). Of 2614 children included in the analysis (median age of 37 months) 48.5% were non-immigrants. Median serum ferritin was 30 ug/L and 10.4% of children had ID and 1.9% had IDA. After adjusting for ethnicity, age, sex, education, income quintile, cow's milk intake, breastfeeding duration and bottle use, there were no significant associations between immigrant status and ferritin, ID or IDA. Significant predictors of low iron status included age, sex, cow's milk intake, and breastfeeding duration. We found no association between family immigrant status and iron status including serum ferritin, ID, and IDA. The relationships did not change when clinically and biologically important covariates were included in the models. These data suggest that children of immigrant families may not need enhanced screening for iron status or targeted interventions for iron supplementation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".