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Role of different nutrient profiling models in identifying targeted foods for front-of-package food labelling in Brazil
OBJECTIVE: To compare the degree of strictness and agreement of different nutrient profiling models (NPM) used to identify which foods would be required to show front-of-package (FOP) warning labels. DESIGN: Using data of 11 434 packaged foods found in the five largest food retailers in Brazil, we u...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8025091/ https://www.ncbi.nlm.nih.gov/pubmed/32515717 http://dx.doi.org/10.1017/S1368980019005056 |
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author | Duran, Ana Clara Ricardo, Camila Zancheta Mais, Laís Amaral Bortoletto Martins, Ana Paula |
author_facet | Duran, Ana Clara Ricardo, Camila Zancheta Mais, Laís Amaral Bortoletto Martins, Ana Paula |
author_sort | Duran, Ana Clara |
collection | PubMed |
description | OBJECTIVE: To compare the degree of strictness and agreement of different nutrient profiling models (NPM) used to identify which foods would be required to show front-of-package (FOP) warning labels. DESIGN: Using data of 11 434 packaged foods found in the five largest food retailers in Brazil, we used two published NPM: the Pan American Health Organization (PAHO) model and the NPM used in the Chilean nutritional FOP labelling policy, and compared them with a NPM proposed by the Brazilian National Health Surveillance Agency (Anvisa). The proportion of foods that would be required to show FOP warning labels was calculated overall and by food category. We also tested whether a modified version of the PAHO NPM would behave similarly to the original version. SETTING: Brazil. RESULTS: Two-thirds of the packaged products (62 %) would receive FOP warning labels under the PAHO NPM, as compared with 45 % of products using the proposed Anvisa NPM and 41 % if the Chilean NPM was applied. The PAHO NPM identified more foods high in critical nutrients such as sweetened dairy and non-dairy beverages, canned vegetables and convenience foods. Overall agreement between models was considered good with kappa coefficient ranging from 0·57 to 0·92 but was lower for some food categories. CONCLUSIONS: We found variations in the degree of strictness and agreement between assessed NPM. The PAHO NPM identified more foods and beverages high in sugar which are among the top contributors to sugar and energy intake in Brazil. |
format | Online Article Text |
id | pubmed-8025091 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-80250912021-04-15 Role of different nutrient profiling models in identifying targeted foods for front-of-package food labelling in Brazil Duran, Ana Clara Ricardo, Camila Zancheta Mais, Laís Amaral Bortoletto Martins, Ana Paula Public Health Nutr Research Paper OBJECTIVE: To compare the degree of strictness and agreement of different nutrient profiling models (NPM) used to identify which foods would be required to show front-of-package (FOP) warning labels. DESIGN: Using data of 11 434 packaged foods found in the five largest food retailers in Brazil, we used two published NPM: the Pan American Health Organization (PAHO) model and the NPM used in the Chilean nutritional FOP labelling policy, and compared them with a NPM proposed by the Brazilian National Health Surveillance Agency (Anvisa). The proportion of foods that would be required to show FOP warning labels was calculated overall and by food category. We also tested whether a modified version of the PAHO NPM would behave similarly to the original version. SETTING: Brazil. RESULTS: Two-thirds of the packaged products (62 %) would receive FOP warning labels under the PAHO NPM, as compared with 45 % of products using the proposed Anvisa NPM and 41 % if the Chilean NPM was applied. The PAHO NPM identified more foods high in critical nutrients such as sweetened dairy and non-dairy beverages, canned vegetables and convenience foods. Overall agreement between models was considered good with kappa coefficient ranging from 0·57 to 0·92 but was lower for some food categories. CONCLUSIONS: We found variations in the degree of strictness and agreement between assessed NPM. The PAHO NPM identified more foods and beverages high in sugar which are among the top contributors to sugar and energy intake in Brazil. Cambridge University Press 2021-04 2020-06-09 /pmc/articles/PMC8025091/ /pubmed/32515717 http://dx.doi.org/10.1017/S1368980019005056 Text en © The Authors 2020 http://creativecommons.org/licenses/by/4.0/ This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Paper Duran, Ana Clara Ricardo, Camila Zancheta Mais, Laís Amaral Bortoletto Martins, Ana Paula Role of different nutrient profiling models in identifying targeted foods for front-of-package food labelling in Brazil |
title | Role of different nutrient profiling models in identifying targeted foods for front-of-package food labelling in Brazil |
title_full | Role of different nutrient profiling models in identifying targeted foods for front-of-package food labelling in Brazil |
title_fullStr | Role of different nutrient profiling models in identifying targeted foods for front-of-package food labelling in Brazil |
title_full_unstemmed | Role of different nutrient profiling models in identifying targeted foods for front-of-package food labelling in Brazil |
title_short | Role of different nutrient profiling models in identifying targeted foods for front-of-package food labelling in Brazil |
title_sort | role of different nutrient profiling models in identifying targeted foods for front-of-package food labelling in brazil |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8025091/ https://www.ncbi.nlm.nih.gov/pubmed/32515717 http://dx.doi.org/10.1017/S1368980019005056 |
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