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Algorithm-based mapping of products in a branded Canadian food and beverage database to their equivalents in Health Canada’s Canadian Nutrient File

INTRODUCTION: There is increasing recognition of the value of linking food sales databases to national food composition tables for population nutrition research. OBJECTIVES: Expanding upon automated and manual database mapping approaches in the literature, our aim was to match 1,179 food products in...

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Autores principales: Gilbert, Sappho Z., Morrison, Conor L., Chen, Qiuyu J., Punian, Jesman, Bernstein, Jodi T., Jessri, Mahsa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9980422/
https://www.ncbi.nlm.nih.gov/pubmed/36873256
http://dx.doi.org/10.3389/fnut.2022.1013516
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author Gilbert, Sappho Z.
Morrison, Conor L.
Chen, Qiuyu J.
Punian, Jesman
Bernstein, Jodi T.
Jessri, Mahsa
author_facet Gilbert, Sappho Z.
Morrison, Conor L.
Chen, Qiuyu J.
Punian, Jesman
Bernstein, Jodi T.
Jessri, Mahsa
author_sort Gilbert, Sappho Z.
collection PubMed
description INTRODUCTION: There is increasing recognition of the value of linking food sales databases to national food composition tables for population nutrition research. OBJECTIVES: Expanding upon automated and manual database mapping approaches in the literature, our aim was to match 1,179 food products in the Canadian data subset of Euromonitor International’s Passport Nutrition to their closest respective equivalents in Health Canada’s Canadian Nutrient File (CNF). METHODS: Matching took place in two major steps. First, an algorithm based on thresholds of maximal nutrient difference (between Euromonitor and CNF foods) and fuzzy matching was executed to offer match options. If a nutritionally appropriate match was available among the algorithm suggestions, it was selected. When the suggested set contained no nutritionally sound matches, the Euromonitor product was instead manually matched to a CNF food or deemed unmatchable, with the unique addition of expert validation to maximize meticulousness in matching. Both steps were independently performed by at least two team members with dietetics expertise. RESULTS: Of 1,111 Euromonitor products run through the algorithm, an accurate CNF match was offered for 65% of them; missing or zero-calorie data precluded 68 products from being run in the algorithm. Products with 2 or more algorithm-suggested CNF matches had higher match accuracy than those with one (71 vs. 50%, respectively). Overall, inter-rater agreement (reliability) rates were robust for matches chosen among algorithm options (51%) and even higher regarding whether manual selection would be required (71%); among manually selected CNF matches, reliability was 33%. Ultimately, 1,152 (98%) Euromonitor products were matched to a CNF equivalent. CONCLUSION: Our reported matching process successfully bridged a food sales database’s products to their respective CNF matches for use in future nutritional epidemiological studies of branded foods sold in Canada. Our team’s novel utilization of dietetics expertise aided in match validation at both steps, ensuring rigor and quality of resulting match selections.
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spelling pubmed-99804222023-03-03 Algorithm-based mapping of products in a branded Canadian food and beverage database to their equivalents in Health Canada’s Canadian Nutrient File Gilbert, Sappho Z. Morrison, Conor L. Chen, Qiuyu J. Punian, Jesman Bernstein, Jodi T. Jessri, Mahsa Front Nutr Nutrition INTRODUCTION: There is increasing recognition of the value of linking food sales databases to national food composition tables for population nutrition research. OBJECTIVES: Expanding upon automated and manual database mapping approaches in the literature, our aim was to match 1,179 food products in the Canadian data subset of Euromonitor International’s Passport Nutrition to their closest respective equivalents in Health Canada’s Canadian Nutrient File (CNF). METHODS: Matching took place in two major steps. First, an algorithm based on thresholds of maximal nutrient difference (between Euromonitor and CNF foods) and fuzzy matching was executed to offer match options. If a nutritionally appropriate match was available among the algorithm suggestions, it was selected. When the suggested set contained no nutritionally sound matches, the Euromonitor product was instead manually matched to a CNF food or deemed unmatchable, with the unique addition of expert validation to maximize meticulousness in matching. Both steps were independently performed by at least two team members with dietetics expertise. RESULTS: Of 1,111 Euromonitor products run through the algorithm, an accurate CNF match was offered for 65% of them; missing or zero-calorie data precluded 68 products from being run in the algorithm. Products with 2 or more algorithm-suggested CNF matches had higher match accuracy than those with one (71 vs. 50%, respectively). Overall, inter-rater agreement (reliability) rates were robust for matches chosen among algorithm options (51%) and even higher regarding whether manual selection would be required (71%); among manually selected CNF matches, reliability was 33%. Ultimately, 1,152 (98%) Euromonitor products were matched to a CNF equivalent. CONCLUSION: Our reported matching process successfully bridged a food sales database’s products to their respective CNF matches for use in future nutritional epidemiological studies of branded foods sold in Canada. Our team’s novel utilization of dietetics expertise aided in match validation at both steps, ensuring rigor and quality of resulting match selections. Frontiers Media S.A. 2023-02-16 /pmc/articles/PMC9980422/ /pubmed/36873256 http://dx.doi.org/10.3389/fnut.2022.1013516 Text en Copyright © 2023 Gilbert, Morrison, Chen, Punian, Bernstein and Jessri. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Nutrition
Gilbert, Sappho Z.
Morrison, Conor L.
Chen, Qiuyu J.
Punian, Jesman
Bernstein, Jodi T.
Jessri, Mahsa
Algorithm-based mapping of products in a branded Canadian food and beverage database to their equivalents in Health Canada’s Canadian Nutrient File
title Algorithm-based mapping of products in a branded Canadian food and beverage database to their equivalents in Health Canada’s Canadian Nutrient File
title_full Algorithm-based mapping of products in a branded Canadian food and beverage database to their equivalents in Health Canada’s Canadian Nutrient File
title_fullStr Algorithm-based mapping of products in a branded Canadian food and beverage database to their equivalents in Health Canada’s Canadian Nutrient File
title_full_unstemmed Algorithm-based mapping of products in a branded Canadian food and beverage database to their equivalents in Health Canada’s Canadian Nutrient File
title_short Algorithm-based mapping of products in a branded Canadian food and beverage database to their equivalents in Health Canada’s Canadian Nutrient File
title_sort algorithm-based mapping of products in a branded canadian food and beverage database to their equivalents in health canada’s canadian nutrient file
topic Nutrition
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9980422/
https://www.ncbi.nlm.nih.gov/pubmed/36873256
http://dx.doi.org/10.3389/fnut.2022.1013516
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