Socioeconomic Inequalities Impact the Ability of Pregnant Women and Women of Childbearing Age to Consume Nutrients Needed for Neurodevelopment: An Analysis of NHANES 2007–2018
Bibliographic record
Abstract
Adequate consumption of nutrients that support infant neurodevelopment is critical among pregnant women and women of childbearing age. Understanding the potential effects of socioeconomic inequalities on nutrient gaps in these life stages is thus important for informing strategies to mitigate negative health consequences. Usual intake (foods and dietary supplements) of neurodevelopment-related nutrients was determined from 24 h recalls among women of childbearing age and pregnant women (20−44 years) using data from 2007−2018 NHANES. Usual intake was compared across household food security, poverty-to-income ratio (PIR), and household participation in federal food and nutrition assistance programs. Intake of EPA + DHA was universally low with >95% of all women (pregnant and non-pregnant) below the DGA recommendation from foods alone. Women in households that participated in the Supplemental Nutrition Assistance Program had a significantly lower intake of multiple nutrients relative to those who did not participate. For example, 50% had intakes below the estimated average requirement (EAR) for vitamin A (versus 32%), 42% were below the EAR for calcium (versus 33%) and 65% were below the EAR for magnesium (versus 42%). Similar gradients were observed by PIR and household food security, and among pregnant women whereby gaps were more evident in those experiencing socioeconomic inequalities. The use of dietary supplements attenuated shortfalls for most nutrients. These findings highlight a critical need to support the nutritional requirements for women of childbearing age and pregnant women.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".