Examining the Double Burden of Underweight, Overweight/Obesity and Iron Deficiency among Young Children in a Canadian Primary Care Setting
Bibliographic record
Abstract
There is little evidence on the prevalence of the double burden and association between body mass index (BMI) and iron deficiency among young children living in high-income countries. We conducted a cross-sectional study of healthy children, 12–29 months of age, recruited during health supervision visits in Toronto, Canada, and concurrently measured BMI and serum ferritin. The prevalence of a double burden of underweight (zBMI < −2) and iron deficiency or overweight/obesity (zBMI > 2) and iron deficiency was calculated. Regression models examined BMI and serum ferritin as continuous and categorical variables, adjusted for covariates. We found the following in terms of prevalence among 1953 children (mean age 18.3 months): underweight 2.6%, overweight/obesity 4.9%, iron deficiency 13.8%, iron-deficiency anemia 5.4%, underweight and iron deficiency 0.4%, overweight/obesity and iron deficiency 1.0%. The change in median serum ferritin for each unit of zBMI was −1.31 µg/L (95% CI −1.93, −0.68, p < 0.001). Compared with normal weight, we found no association between underweight and iron deficiency; meanwhile, overweight/obesity was associated with a higher odds of iron deficiency (OR 2.15, 95% CI 1.22, 3.78, p = 0.008). A double burden of overweight/obesity and iron deficiency occurs in about 1.0% of young children in this high-income setting. For risk stratification and targeted screening in young children, overweight/obesity should be added to the list of important risk factors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".