Defining “High-In” Saturated Fat, Sugar, and Sodium to Help Inform Front-of-Pack Labeling Efforts for Packaged Foods and Beverages in the United States
Bibliographic record
Abstract
Background: To help consumers make healthier choices, the US Food and Drug Administration (FDA) has been charged with developing a front-of-package label (FOPL) to appear on US packaged foods and beverages. One option being explored is the use of “high-in” FOPLs for added sugar, sodium, and saturated fat using a threshold of ≥20% of the recommended daily value (%DV) per portion/serving size to define “high-in”. While research has addressed what FOPL designs are most effective at visually communicating “high-in”, less attention has been paid to the nutrient profile model (NPM) used to decide which products should receive these labels. In addition, several established regional NPMs already exist that identify products that are high in nutrients of concern, but it is unclear how these compare to the FDA’s %DV approach. Methods: We used a dataset of 51,809 US products from Mintel’s Global New Products Database to examine how the FDA’s current definition of “high-in” compares to three established regional NPMs: the Canadian NPM, the Pan American Health Organization (PAHO) NPM, and Chile’s NPM. Results: Overall agreement between the four NPMs was 51% for foods and 72% for beverages, with highest agreement in categories such as sweetened sodas (87%), and lowest agreement in categories such as bread (14%) and salty snacks (29%). The Canadian NPM showed the highest agreement to the FDA “high-in” criteria while the Chilean and PAHO models had lower agreement. For many food categories, the FDA’s definition of “high-in” would require the fewest products to carry a “high-in” label. This issue was particularly pronounced in categories that tend to be served in small portions (e.g., salty snacks, bars), but disappeared or reversed for categories that are served in larger portions (e.g., frozen and non-frozen main dishes). Conclusions: The NPM chosen has important policy implications for an FOPL system’s ability to identify unhealthy foods and incentivize companies to reformulate products. Based on these results, the FDA should consider using a stronger NPM similar to those used elsewhere in the Americas region when deciding the final thresholds for “high-in” for US packaged foods and beverages.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".