Front-of-package (FOP) and shelf nutrition labeling of food products can potentially improve diets and health of the U.S. population if they are easy to use and understand and accurately reflect the nutritional quality of foods. The Department of Health and Human Services (DHHS) and the Food and Drug Administration (FDA) are interested in ensuring that nutrition criteria that manufacturers use on FOP or shelf labels are based on evidence. The Assistant Secretary for Planning and Evaluation (ASPE) contracted with RTI International to examine the nutrient criteria behind existing summary FOP or shelf systems and develop and test an algorithm for a summary FOP system.
A summary FOP or shelf system rates the overall nutritional quality of a food with an overall numeric score, a multiple category rating, or a single icon to indicate whether a food meets specified nutrient criteria. Summary systems are one of two general types of FOP systems; the other type is a nutrient-specific system that displays a few nutrients and their amounts per serving and sometimes includes the percentage of a daily recommended value. In some instances, both a summary and nutrient-specific system may be applied to a food item (e.g., a summary check mark and the number of calories per serving or a summary score on a shelf tag and a nutrient-specific label on the front of the food package). On the one hand, nutrient-specific systems can help consumers identify key nutrients in a food but do not provide an overall assessment of the product. Therefore, the consumer may focus on a single nutrient (e.g., calories) and not get a sense of the overall nutritional quality of the food. On the other hand, summary systems can provide an overall assessment of the nutritional quality of a food but do not tell the consumer why the food received the score or rating (e.g., high calcium and low saturated fat content of low-fat milk). Few studies have directly compared how consumers understand the two types of systems, but it appears consumers can identify healthier food choices more easily with a nutrient-specific system with traffic light colors of red, yellow, and green than a summary check mark type system. This report considers only summary systems.
RTI developed and tested a nutrient density-based algorithm that included positive scores for nutrients that should be encouraged and negative scores for nutrients that should be limited in the diet. We scored a set of foods using the algorithm and compared the average scores of food groupings. As expected, nutrient-dense foods (i.e., foods with substantial amounts of vitamins and minerals and few calories) scored high, and foods that are low in nutrient density (i.e., that supply calories but relatively small amounts of micronutrients) scored low. Fruits, vegetables, and legumes and nuts had the highest group scores, and the lowest group scores were seen with fats and oils, sweets, and beverages (e.g., coffee, tea, soft drinks, and fruit drinks). A series of modifications were made to the algorithm, adding and removing various nutrients and food components, and effects on food scores and the ability of the algorithm to predict overall dietary quality were assessed. As a measure of overall dietary quality, we used the Healthy Eating Index (HEI) developed by the Center for Nutrition Policy and Promotion, U.S. Department of Agriculture (USDA) and the National Cancer Institute, U.S. Department of Health and Human Services. HEI scores are calculated by assigning points for adherence to food group-based guidelines in MyPyramid and are based primarily on recommended volume amounts per 1,000 kcal. Scores for each food-group or component of the HEI are capped and the highest HEI score is 100. Although the HEI scores total diet, it is inherently different from a food-scoring algorithm; nevertheless, it provides a mechanism to evaluate how well the individual food scores of an individual's diet relate to overall diet quality. In this report, we demonstrate that a final algorithm incorporated weighting factors to nutrients that were derived from statistical analyses of nutrient intakes of the U.S. population resulted in higher prediction of dietary quality than seen with existing nutrient density algorithms that have been tested similarly. Our final algorithm explained two-thirds of the variation in HEI scores, compared with one-third to one-half with other nutrient density algorithms. Our algorithm included nutrients or food components with positive weighting factors for protein, unsaturated fat, fiber, calcium, and vitamin C and with negative weighting factors for saturated fat, sodium, and added sugars. The use of nutrient values per 100 kcal was slightly better at predicting overall dietary quality than using nutrients per reference serving sizes (reference amounts customarily consumed, RACC). Among the top-scoring foods were raw and leafy green vegetables on a per 100 kcal basis and avocado, almonds, oranges, and strawberries on a per RACC basis. The algorithm worked well in predicting dietary quality across various population groups, such as age, ethnicity, socioeconomic status, and weight status.
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Summary systems can be simplified for the consumer by categorizing a score into different levels. For example, categories used by others have included three levels, such as traffic light colors or text signifying "high," "medium," or "low." We assessed categories that used both three- and five-point categorization of scores using the final algorithm that resulted in reasonable rankings of foods based on three- or five-point ratings. The three-category system performed as well as the five-category system in distinguishing between common foods (e.g., whole grain vs. white bread, nonfat vs. whole-milk yogurt).
This project examined various summary FOP systems, the selection of nutrients and nutrient criteria for FOP systems, issues in developing and testing a summary system, and the subsequent development and testing of a system. The results from this project reveal relatively small differences in predicting overall dietary quality with inclusion or exclusion of various nutrients; therefore, using a "best model" approach to determine which algorithm explains the greatest variation in dietary quality is one approach to determining the selection of nutrients. Consumers selecting foods in the supermarket may not think in terms of how a food fits into their overall diet; therefore, it remains to be determined if this type of system will assist consumers in the supermarket.
The ability of consumers to understand and select healthier food choices using summary systems needs further testing before further recommendations can be made. This project began to explore a new approach to identify nutrients and weighting factors to include in a scoring system; however, additional nutrients or alternative weighting of nutrients could be explored further. A summary system is not necessarily intuitive or transparent regarding nutrients that influence the score or rating; therefore, it is important that it be accepted by a respected regulatory agency such as FDA.