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Food Compass Score-10: validation of a method for evaluating the healthfulness of foods and beverages using
Eden M Barrett1, Frederick Cudhea2, Erin Washbon2
1Food is Medicine Institute, Friedman School of Nutrition Science & Policy, Tufts University, Boston, MA, United States; The George Institute for Global Health, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia.
Background:
The Food Compass, a novel food profiling system, provides a holistic, validated assessment of the healthfulness of foods, beverages, and meals using 54 attributes across 9 domains. However, information on several of these attributes is not commonly available.
Objectives:
We aimed to develop and validate an approach, Food Compass Score-10 (FCS-10), to estimate FCSs using information commonly available on package labels.
Methods:
Missing attributes were calculated using weighted scores of each product's ingredients, derived from a dataset of ∼10,000 foods and beverages. The final FCS-10 was scaled from 1 (least healthful) to 10 (most healthful). As part of this validation study, diagnostic accuracy analysis was conducted to evaluate the performance of the FCS-10 compared with the original score. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated by comparing the FCS-10 recommendation categorizations with the FCS recommendation categorizations (≥7 for foods to encourage, 4-6 for foods to consume in moderation, ≤3 for foods to limit).
Results:
FCS-10 produced scores within 1 unit of the original score (when rescaled 1-10 for comparison) for 89% of products (n = 481/538); none deviated >2 units. The correlation between FCS-10 and the original score was high (r = 0.93). FCS-10 also performed well in identifying products to encourage, moderate, or limit, with overall sensitivity and specificity of 87% and 93%, respectively.
Conclusions:
FCS-10 offers a practical approach for estimating the healthfulness of diverse packaged foods and beverages using readily available label data while maintaining the strengths of the original system.
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