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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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Detalles Bibliográficos
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
Descripción
Sumario: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.