Sources of Added Sugars Intake Among the U.S. Population: Analysis by Selected Sociodemographic Factors Using the National Health and Nutrition Examination Survey 2011–18
Bibliographic record
Abstract
Recent estimates of added sugars intake among the U.S. population show intakes are above recommended levels. Knowledge about the sources of added sugars contributing to intakes is required to inform dietary guidance, and understanding how those sources vary across sociodemographic subgroups could also help to target guidance. The purpose of this study was to provide a comprehensive update on sources of added sugars among the U.S. population, and to examine variations in sources according to sociodemographic factors. Regression analyses on intake data from NHANES 2011–18 were used to examine sources of added sugars intake among the full sample (N = 30,678) and among subsamples stratified by age, gender, ethnicity, and income. Results showed the majority of added sugars in the diet (61–66%) came from a few sources, and the top two sources were sweetened beverages and sweet bakery products, regardless of age, ethnicity, or income. Sweetened beverages, including soft drinks and fruit drinks, as well as tea, were the largest contributors to added sugars intake. There were some age-, ethnic-, and income-related differences in the relative contributions of added sugars sources, highlighting the need to consider sociodemographic contexts when developing dietary guidance or other supports for healthy eating.
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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 | 0.003 |
| 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) | 0.001 | 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".