Socio-Demographic Determinant Factors for Serum Iron, Copper, Zinc, and Selenium Concentrations Among U.S. Women of Childbearing Age
Bibliographic record
Abstract
Background: Trace elements (TEs) are essential nutrients for the human body and have a significant impact on fertility and hormone levels in women of reproductive age, underscoring the importance of understanding sociodemographic variations in their concentrations within this population. Objective: To investigate the socio-demographic factors influencing blood concentrations of four essential TEs, including iron, zinc, copper, and selenium among women of reproductive age. Methods: A cross-sectional analysis of women aged 20–44 years was performed using the National Health and Nutrition Examination Survey, 1999–2018. Serum iron data were analyzed for 9211 women across 10 cycles, while serum copper, zinc, and selenium data were available for 1027 women across 3 cycles. Generalized linear and logistic regressions examined the individual associations of socio-demographic factors, including age, race and ethnicity, education, and poverty index ratio, with iron, zinc, copper, and selenium concentrations treated as continuous and categorical outcomes, respectively. A qualitative heatmap explored the joint associations between the socio-demographic factors and the four essential TEs. Results: Reduced iron concentrations and increased risks of insufficiency occurred in older, Black, low-education, or low-income women. Black women were more likely to have lower zinc and selenium concentrations and an increased risk of zinc insufficiency but higher copper concentrations. The qualitative heatmap found that older, Black, low-education, and low-income women generally had lower concentrations of the four TEs, particularly iron (β = −0.10; p < 0.01). Conclusions: Socially disadvantaged women are more likely to present with lower TE concentrations, and these specific population groups should be targeted by replenishment planning by public health initiatives.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".