Prevalence of iron deficiency in 62,685 women of seven race/ethnicity groups: The HEIRS Study
Bibliographic record
Abstract
BACKGROUND: Few cross-sectional studies report iron deficiency (ID) prevalence in women of different race/ethnicity and ages in US or Canada. MATERIALS AND METHODS: We evaluated screening observations on women who participated between 2001-2003 in a cross-sectional, primary care-based sample of adults ages ≥25 y whose observations were complete: race/ethnicity; age; transferrin saturation; serum ferritin; and HFE p.C282Y and p.H63D alleles. We defined ID using a stringent criterion: combined transferrin saturation <10% and serum ferritin <33.7 pmol/L (<15 μg/L). We compared ID prevalence in women of different race/ethnicity subgrouped by age and determined associations of p.C282Y and p.H63D to ID overall, and to ID in women ages 25-44 y with or without self-reported pregnancy. RESULTS: These 62,685 women included 27,079 whites, 17,272 blacks, 8,566 Hispanics, 7,615 Asians, 449 Pacific Islanders, 441 Native Americans, and 1,263 participants of other race/ethnicity. Proportions of women with ID were higher in Hispanics and blacks than whites and Asians. Prevalence of ID was significantly greater in women ages 25-54 y of all race/ethnicity groups than women ages ≥55 y of corresponding race/ethnicity. In women ages ≥55 y, ID prevalence did not differ significantly across race/ethnicity. p.C282Y and p.H63D prevalence did not differ significantly in women with or without ID, regardless of race/ethnicity, age subgroup, or pregnancy. CONCLUSIONS: ID prevalence was greater in Hispanic and black than white and Asian women ages 25-54 y. p.C282Y and p.H63D prevalence did not differ significantly in women with or without ID, regardless of race/ethnicity, age subgroup, or pregnancy.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".