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Trade as a structural driver of dietary risk factors for noncommunicable diseases in the Pacific: an analysis of household income and expenditure survey data

BACKGROUND: Noncommunicable diseases are a health and development challenge. Pacific Island countries are heavily affected by NCDs, with diabetes and obesity rates among the highest in the world. Trade is one of multiple structural drivers of NCDs in the Pacific, but country-level data linking trade...

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Autores principales: Sahal Estimé, Michelle, Lutz, Brian, Strobel, Ferdinand
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4118652/
https://www.ncbi.nlm.nih.gov/pubmed/24927626
http://dx.doi.org/10.1186/1744-8603-10-48
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author Sahal Estimé, Michelle
Lutz, Brian
Strobel, Ferdinand
author_facet Sahal Estimé, Michelle
Lutz, Brian
Strobel, Ferdinand
author_sort Sahal Estimé, Michelle
collection PubMed
description BACKGROUND: Noncommunicable diseases are a health and development challenge. Pacific Island countries are heavily affected by NCDs, with diabetes and obesity rates among the highest in the world. Trade is one of multiple structural drivers of NCDs in the Pacific, but country-level data linking trade, diets and NCD risk factors are scarce. We attempted to illustrate these links in five countries. The study had three objectives: generate cross-country profiles of food consumption and expenditure patterns; highlight the main ‘unhealthy’ food imports in each country to inform targeted policymaking; and demonstrate the potential of HCES data to analyze links between trade, diets and NCD risk factors, such as obesity. METHODS: We used two types of data: obesity rates as reported by WHO and aggregated household-level food expenditure and consumption from Household Income and Expenditure Survey reports. We classified foods in HIES data into four categories: imported/local, ‘unhealthy’/’healthy’, nontraditional/traditional, processed/unprocessed. We generated cross-country profiles and cross-country regressions to examine the relationships between imported foods and unhealthy foods, and between imported foods and obesity. RESULTS: Expenditure on imported foods was considerable in all countries but varied across countries, with highest values in Kiribati (53%) and Tonga (52%) and lowest values in Solomon Islands and Vanuatu (30%). Rice and sugar accounted for significant amounts of imported foods in terms of expenditure and calories, ranking among the top 3 foods in most countries. We found significant or near-significant associations in expenditure and caloric intake between ‘unhealthy’ and imported foods as well as between imported foods and obesity, though inferences based on these associations should be made carefully due to data constraints. CONCLUSIONS: While additional research is needed, this study supports previous findings on trade as a structural driver of NCD risk and identifies the top imported foods that could serve as policy targets. Moreover, this analysis is proof-of-concept that the methodology is a cost-effective way for countries to use existing data to generate policy-relevant evidence on links between trade and NCDs. We believe that the methodology is replicable to other countries globally. A user-friendly Excel tool is available upon request to assist such analyses.
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spelling pubmed-41186522014-08-02 Trade as a structural driver of dietary risk factors for noncommunicable diseases in the Pacific: an analysis of household income and expenditure survey data Sahal Estimé, Michelle Lutz, Brian Strobel, Ferdinand Global Health Research BACKGROUND: Noncommunicable diseases are a health and development challenge. Pacific Island countries are heavily affected by NCDs, with diabetes and obesity rates among the highest in the world. Trade is one of multiple structural drivers of NCDs in the Pacific, but country-level data linking trade, diets and NCD risk factors are scarce. We attempted to illustrate these links in five countries. The study had three objectives: generate cross-country profiles of food consumption and expenditure patterns; highlight the main ‘unhealthy’ food imports in each country to inform targeted policymaking; and demonstrate the potential of HCES data to analyze links between trade, diets and NCD risk factors, such as obesity. METHODS: We used two types of data: obesity rates as reported by WHO and aggregated household-level food expenditure and consumption from Household Income and Expenditure Survey reports. We classified foods in HIES data into four categories: imported/local, ‘unhealthy’/’healthy’, nontraditional/traditional, processed/unprocessed. We generated cross-country profiles and cross-country regressions to examine the relationships between imported foods and unhealthy foods, and between imported foods and obesity. RESULTS: Expenditure on imported foods was considerable in all countries but varied across countries, with highest values in Kiribati (53%) and Tonga (52%) and lowest values in Solomon Islands and Vanuatu (30%). Rice and sugar accounted for significant amounts of imported foods in terms of expenditure and calories, ranking among the top 3 foods in most countries. We found significant or near-significant associations in expenditure and caloric intake between ‘unhealthy’ and imported foods as well as between imported foods and obesity, though inferences based on these associations should be made carefully due to data constraints. CONCLUSIONS: While additional research is needed, this study supports previous findings on trade as a structural driver of NCD risk and identifies the top imported foods that could serve as policy targets. Moreover, this analysis is proof-of-concept that the methodology is a cost-effective way for countries to use existing data to generate policy-relevant evidence on links between trade and NCDs. We believe that the methodology is replicable to other countries globally. A user-friendly Excel tool is available upon request to assist such analyses. BioMed Central 2014-06-13 /pmc/articles/PMC4118652/ /pubmed/24927626 http://dx.doi.org/10.1186/1744-8603-10-48 Text en Copyright © 2014 Sahal Estimé et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Sahal Estimé, Michelle
Lutz, Brian
Strobel, Ferdinand
Trade as a structural driver of dietary risk factors for noncommunicable diseases in the Pacific: an analysis of household income and expenditure survey data
title Trade as a structural driver of dietary risk factors for noncommunicable diseases in the Pacific: an analysis of household income and expenditure survey data
title_full Trade as a structural driver of dietary risk factors for noncommunicable diseases in the Pacific: an analysis of household income and expenditure survey data
title_fullStr Trade as a structural driver of dietary risk factors for noncommunicable diseases in the Pacific: an analysis of household income and expenditure survey data
title_full_unstemmed Trade as a structural driver of dietary risk factors for noncommunicable diseases in the Pacific: an analysis of household income and expenditure survey data
title_short Trade as a structural driver of dietary risk factors for noncommunicable diseases in the Pacific: an analysis of household income and expenditure survey data
title_sort trade as a structural driver of dietary risk factors for noncommunicable diseases in the pacific: an analysis of household income and expenditure survey data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4118652/
https://www.ncbi.nlm.nih.gov/pubmed/24927626
http://dx.doi.org/10.1186/1744-8603-10-48
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