Food Retail Inequities in the Capital with Greatest Income Inequality in Brazil
4 affiliations
- 1Observatório das Desigualdades em Alimentação e Nutrição, Departamento de Nutrição, Centro de Ciências da Saúde, Universidade Federal da Paraíba, Campus I, Cidade Universitária, João Pessoa 58051-900, PB, Brazil
- 2Fundação Oswaldo Cruz—Centro de Integração de Dados e Conhecimentos para Saúde/CIDACS, Parque Tecnológico da Bahia, R. Mundo, 121, Trobogy, Salvador 41745-715, BA, Brazil
- 3Grupo de Pesquisa de Intervenções em Nutrição, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Nutrition Building JK Campus—Highway BR 367—Km 583, Alto da Jacuba District, Diamantina 39100-000, MG, Brazil
- 4Grupo de Pesquisa de Intervenções em Nutrição, Departamento de Nutrição, Universidade Federal de Minas Gerais, Escola de Enfermagem, 190 Alfredo Balena Avenue, Room 316, Santa Efigênia, Belo Horizonte 30130-100, MG, Brazil
Abstract
Background/Objectives: The uneven distribution of food stores is a key determinant of health inequities. This ecological study aimed to map socioeconomic inequalities in the food environment of Joao Pessoa, Brazil. Methods: Stores were classified into three categories, according to the main type of food sold: unprocessed, processed, or ultra-processed. The Socioeconomic Index of Geographic Context for Health Studies (GeoSES) and its dimensions (Education, Income, Poverty, Material Deprivation, and Mobility) were used to analyze the territory. The association between the number of food stores and socioeconomic indicators was analyzed through thematic maps, correlation coefficients, and negative binomial regression models. Results: A total of 5087 food stores were mapped. A strong positive correlation was observed between the number of stores selling unprocessed foods and the GeoSES index (r = 0.85; p < 0.001), Education (r = 0.83; p < 0.001), Income (r = 0.85; p < 0.001), and Material Deprivation (r = 0.85; p < 0.001), while Mobility showed a moderate positive correlation (r = 0.59; p = 0.04). In the negative binomial regression model, each one-unit increase in the GeoSES index was associated with a 2.05-fold increase in the expected number of stores selling unprocessed foods (RR = 2.05; 95% CI: 1.48–2.84; p < 0.001). Conclusions: The food environment in Joao Pessoa, Brazil was associated with socioeconomic characteristics, with greater availability of unprocessed food stores in higher socioeconomic areas, while ultra-processed food stores were more evenly distributed across the municipality.
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BibTeX
@article{ciqueira2026food,
title = {{Food Retail Inequities in the Capital with Greatest Income Inequality in Brazil}},
author = {Joerika Ciqueira and Denise Sales and Patrícia Freitas and Cecília Craveiro and Aline Lopes and Mariana Lopes},
journal = {Nutrients},
year = {2026},
month = oct,
doi = {10.3390/nu18213472},
url = {https://www.mdpi.com/2072-6643/18/21/3472},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Food Retail Inequities in the Capital with Greatest Income Inequality in Brazil
AU - Joerika Ciqueira
AU - Denise Sales
AU - Patrícia Freitas
AU - Cecília Craveiro
AU - Aline Lopes
AU - Mariana Lopes
PY - 2026
DA - 2026/10/22
JO - Nutrients
DO - 10.3390/nu18213472
UR - https://www.mdpi.com/2072-6643/18/21/3472
AB - Background/Objectives: The uneven distribution of food stores is a key determinant of health inequities. This ecological study aimed to map socioeconomic inequalities in the food environment of Joao Pessoa, Brazil. Methods: Stores were classified into three categories, according to the main type of food sold: unprocessed, processed, or ultra-processed. The Socioeconomic Index of Geographic Context for Health Studies (GeoSES) and its dimensions (Education, Income, Poverty, Material Deprivation, and Mobility) were used to analyze the territory. The association between the number of food stores and socioeconomic indicators was analyzed through thematic maps, correlation coefficients, and negative binomial regression models. Results: A total of 5087 food stores were mapped. A strong positive correlation was observed between the number of stores selling unprocessed foods and the GeoSES index (r = 0.85; p < 0.001), Education (r = 0.83; p < 0.001), Income (r = 0.85; p < 0.001), and Material Deprivation (r = 0.85; p < 0.001), while Mobility showed a moderate positive correlation (r = 0.59; p = 0.04). In the negative binomial regression model, each one-unit increase in the GeoSES index was associated with a 2.05-fold increase in the expected number of stores selling unprocessed foods (RR = 2.05; 95% CI: 1.48–2.84; p < 0.001). Conclusions: The food environment in Joao Pessoa, Brazil was associated with socioeconomic characteristics, with greater availability of unprocessed food stores in higher socioeconomic areas, while ultra-processed food stores were more evenly distributed across the municipality.
SN - 2072-6643
ER -