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J Nutr Educ Behav ; 50(6): 573-581, 2018 06.
Artigo em Inglês | MEDLINE | ID: mdl-29496398

RESUMO

OBJECTIVE: Assess the consumer nutrition environment in midsize to large supermarkets by supermarket type and area-level socioeconomic variables. DESIGN: Cross-sectional census of 257 supermarkets using the Toronto Nutrition Environment Measures Survey in Stores. SETTING: Toronto, Canada. VARIABLES MEASURED: Availability; price and linear shelf space of fruits and vegetables vs energy-dense snack foods by supermarket type; after-tax, low-income measure; and neighborhood improvement area. ANALYSIS: Multivariate linear regression. RESULTS: There was a high availability of fruits (7.7 of 8) and vegetables (9.5 of 11). There was similar linear shelf space for fruits and vegetables vs energy-dense snack foods (ratio, 1.1 m). Adjusted fruit prices were lowest in quintiles 1 (ß = -$1.30; P = .008), 2 (ß = -$1.41; P = .005), and 3 (ß = -$1.89; P < .001) vs quintile 5 (lowest percentage of people living with low income) and in ethnic (ß = -$3.47; P < .001) and discount stores (ß = -$5.64; P < .001) vs conventional. Adjusted vegetable prices were lowest in quintiles 2 (ß = -$1.87; P = .04), 3 (ß = -$1.78; P = .03), and 4 (ß = -$2.65; P = .001) vs quintile 5 and in ethnic (ß = -$7.10; P < .001) and discount (ß = -$5.49; P < .001) stores. They were highest in other (ß = + $3.08; P = .003) vs conventional stores. Adjusted soda and chips prices were lower in discount (ß = -$1.16; P < .001) and higher in other stores (ß = + $0.67; P < .001) vs conventional. CONCLUSIONS AND IMPLICATIONS: Findings do not indicate inequities in shelf space, availability, or price across diverse neighborhoods. Practitioners can use findings to help consumers navigate supermarkets to make healthy choices.


Assuntos
Abastecimento de Alimentos , Alimentos/estatística & dados numéricos , Características de Residência , Censos , Comércio/estatística & dados numéricos , Estudos Transversais , Abastecimento de Alimentos/estatística & dados numéricos , Humanos , Modelos Lineares , Ontário , Características de Residência/estatística & dados numéricos , Fatores Socioeconômicos
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