Do Americans Get Enough Nutrients from Food? Assessing Nutrient Adequacy with NHANES 2013–2016 (P18-040-19)
Bibliographic record
Abstract
Following dietary recommendations should ensure adequate consumption of essential nutrients, including key nutrients that tend to be underconsumed. The objective of this analysis was to determine if Americans are meeting nutrient needs, especially for shortfall nutrients, as defined by the Dietary Guidelines for Americans, by assessing average food intakes with data from the National Health and Nutrition Examination Survey (NHANES) from 2013–2016. Twenty-four-hour dietary recall data from children age 2–18 years (n = 5670) and adults age 19–99 years (n = 10,112) participating in NHANES 2013–2014 and 2015–2016 were analyzed using day one sample weights. Usual intake of nutrients was determined using the National Cancer Institute method with two dietary recalls. The percentage of population with inadequate (intake below the Estimated Average Requirement) or sufficient (intakes above the Adequate Intake, AI) intake of shortfall nutrients was determined using the cut-point method. With iron, the probability method was used instead. Nearly half of the population does not consume adequate calcium (47.4 ± 1.8% children; 44.5 ± 1.1% adults). Even more of the population does not consume enough vitamin D (93.7 ± 0.8% children; 94.8 ± 0.5% adults). Non-Hispanic black children and adults had higher rates of inadequate calcium and vitamin D consumption than other ethnic groups. Large proportions of the population also do not consume enough magnesium (36.2 ± 1.4% children; 53.3 ± 1.2% adults), vitamin A (23.8 ± 2.0% children; 45.5 ± 1.1% adults), vitamin C (22.6 ± 1.6% children; 48.3 ± 1.3% adults) or vitamin E (67.2 ± 1.3% children; 79.0 ± 1.3% adults). Approximately 2.95 ± 0.47% children and 6.02 ± 0.30% adults had inadequate iron intake. Only a small proportion of children and adults consumed more dietary fiber and potassium than the AI for their age groups. Additionally, 20.0 ± 1.1% children and 8.31 ± 0.73% adults had choline intake above the AI. Large percentages of American children and adults do not meet recommendations for underconsumed “nutrients of public health concern” or shortfall nutrients. Encouraging children and adults to consume nutrient-rich foods, such as dairy, fruits and vegetables, can help close these gaps. National Dairy Council.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".