The Level of Processing, Nutritional Composition and Prices of Canadian Packaged Foods and Beverages with and without Gluten-Free Claims

Nutrients. 2021 Apr 2;13(4):1183. doi: 10.3390/nu13041183.

Abstract

Little is known about the healthfulness and cost of gluten-free (GF) foods, relative to non-GF alternatives, in Canada. This study compared the extent of processing, nutritional composition and prices of Canadian products with and without GF claims. Data were sourced from the University of Toronto Food Label Information Program (FLIP) 2013 (n = 15,285) and 2017 (n = 17,337) databases. Logistic regression models examined the association of NOVA processing category with GF claims. Calorie/nutrient contents per 100 g (or mL) were compared between GF and non-GF products. Generalized linear models compared adjusted mean prices per 100 g (or mL) of products with and without GF claims. The prevalence of GF claims increased from 7.1% in 2013 to 15.0% in 2017. GF claims appeared on 17.0% of ultra-processed foods, which were more likely to bear GF claims products than less-processed categories. Median calories and sodium were significantly higher in GF products; no significant differences were observed for saturated fat or sugars. Compared to non-GF products, adjusted mean prices of GF products were higher for 10 food categories, lower for six categories and not significantly different for six categories. Overall, GF claims are becoming increasingly prevalent in Canada; however, they are often less healthful and more expensive than non-GF alternatives, disadvantaging consumers following GF diets.

Keywords: food processing; gluten-free; nutrition claim; nutritional composition; price.

Publication types

  • Comparative Study

MeSH terms

  • Canada
  • Commerce / statistics & numerical data*
  • Databases, Factual
  • Diet, Gluten-Free / statistics & numerical data*
  • Food Analysis
  • Food Handling / statistics & numerical data*
  • Food Labeling / statistics & numerical data
  • Food Packaging / statistics & numerical data
  • Foods, Specialized / statistics & numerical data*
  • Humans
  • Logistic Models
  • Nutrients / analysis*
  • Nutritive Value