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Ecological study of the association between socioeconomic inequality and food deserts and swamps around schools in Rio de Janeiro, Brazil
BMC Public Health volume 23, Article number: 120 (2023)
Previous research suggests that unhealthy community food environments around schools contribute to unhealthy eating habits and negative health outcomes among the youth. However, little is known about how socioeconomic inequalities in those community food environments are associated with food deserts and food swamps across schools’ neighborhoods.
An ecological study was carried out in all 3,159 public and private schools in Rio de Janeiro, Brazil. Three measures of socioeconomic inequality were evaluated: per capita income, segregation index and deprivation index. The community school food environment was analyzed by metrics of food swamps and food deserts.
Food deserts and food swamps were simultaneously more prevalent in neighborhoods of the lowest income, high deprivation, and high segregation. Spatial socioeconomic disparities at the neighborhoods of schools were associated with food deserts and food swamps in Rio de Janeiro.
Our results point to a spatial socioeconomic inequality of establishments that sell food around schools in a Brazilian metropolis, indicating that the areas of greatest deprivation of food services are also the areas with the worst socioeconomic characteristics.
The community food environment has been conceptualized as an important driver of individuals’ eating patterns . This is also true for the school settings in cities, which are placed inside communities – or neighborhoods – with access to healthy and plentiful unhealthy food outlets. School vicinities are places where social interactions and commuting take place . Moreover, they are usually located in neighborhoods characterized by both food deserts and food swamps, which are correlated with obesity  and unhealthy eating behaviors among the youth [4,5,6,7].
Socioeconomic and spatial disparities of the food environments, in turn, are associated with dietary choices  and racial segregation , with direct impact on the youth health status [5, 9]. Specifically, neighborhood school food environments in low-and-middle-income countries (LMIC) predominantly retail unhealthy foods, which contribute to unhealthy eating habits among the youth [10,11,12,13,14]. Thus, shedding light into how inequalities play a role in shaping the availability of healthful food choices in the community school food environment  is crucial to further deepen our understanding of the so-called nutritional inequality [16,17,18], which not only influences food behaviors of children and adolescents, but also helps shaping their eating habits throughout life. Further, while most studies describing socioeconomic disparities at the school or the community around the school food environments are focused on the US and other high-income countries [14, 19], we also add to the body of research by extending the findings to the LMIC context.
We thus analyze how different measures of inequalities at the neighbourhood level – measured by per capita income, public vs. private schools, and indices of spatial segregation and deprivation – are associated with disparities in food availability in the community food environment around schools in Rio de Janeiro, Brazil. Finally, we investigate the prevalence of food deserts and food swamps, stratified by each measure of spatial inequality.
We conducted an ecological study across all public and private schools of the second largest city in Brazil. Rio de Janeiro has 6,775.561 inhabitants and a Human Development Index (HDI) of 0.799. While 31.4% of its citizens have a per capita income of half a minimum wage, the average per capita income is around 4.2 minimum wages.
Secondary database from 2019 was extracted from the State Education Secretariat (Secretaria Estadual de Educação – SEE). Seventy-nine schools were excluded from the final sample. 38 offered only professional education, 7 that attended only special education, and 34 with missing segregation data. The final sample comprised 3,159 schools, which are categorized into public and private.
Neighborhood inequality measures
We collected three measures of socioeconomic spatial inequality, and aggregated at the neighborhood level—which is composed of an aggregate of census tracts: (i) per capita income, (ii) segregation index, and (iii) deprivation index. Data to build the indicators was collected from the last 2010 country census. Besides, we converted all current values from Brazilian Real to US Dollars, using 2010 as the conversion date. Importantly, census tracts are defined in Brazil as the smallest territorial unit, formed by a continuous area, fully contained in an urban or rural area and determined according to the number of households .
Per capita income was calculated as a ratio between total income of the neighborhood and its population. We categorized per capita income into terciles: lowest tercile ranging from U$249.21 and U$593.43; Middle tercile from U$593.44 and U$1,022.44; and highest tercile from U$1,022.55 and U$6,510.32.
As per the segregation index, we used the Getis-Ord Local Gi*statistic (or Gi*statistic). It encompasses a spatially weighted Z-score that represents how much a neighborhood’s income composition deviates from the larger metropolitan areas in its surroundings. Therefore, this index is unique because it (a) takes into account the spatial clustering of segregation within cities, and (b) considers racial composition and social inequalities within and between neighborhoods.
The segregation index is calculated as the standard deviations (SD) between the economic composition of the neighborhoods—assessed by the proportion of heads of household in neighborhoods that earn a monthly income within 0 to 3 SD of the minimum wage—in relation to the surrounding neighborhoods. Thus, we can detect segregation at neighborhood-level and thereby examine segregation within metropolitan areas. Data from the 2010 Brazilian Census were used to determine the proportion of heads of households in a neighborhoods earning a monthly income within 0–3 minimum wage (approximately US$ 0·00–US$ 900·00 in 2010) .
The census tracts were weighted using a first-order rook spatial weight matrix and three categories of segregation were created: (1) High: Gi * statistic ≥ 1·96; (2) Medium: Gi * statistic between 0 and 1·96 and (3) Low: Gi * statistic < 0 according to the distribution of the Z-score. Higher, positive scores represented census tracts that are more segregated—meaning that the proportion of households receiving 0–3 minimum wages are overrepresented in the neighborhood, while lower, negative scores suggest the opposite. Values close to 0 represent neighborhoods in which spatial segregation is low.
Finally, the deprivation index was retrieved from a recently published technical report . It is calculated as a combination of three main indicators: (i) percentage of households receiving less than ½ minimum wage, (ii) percentage of illiterate inhabitants age 7 or older, and (iii) average of individuals with inadequate access to sanitation. Because this index is available for census tracts and our unit of analysis is the neighborhood, we average those sectors and aggregate their value to fit the neighborhood unit of analysis. We then use as thresholds the average of deprivation +-½ SD. Low deprivation areas are those below the lower end of the average vulnerability subtracted by ½ SD, while high deprivation is above the average + ½ SD. Medium deprivation is the interval between the SD from the average. Then, we also categorized this index into three, based on the Health Vulnerability Index : high, medium, and low deprivation neighborhoods.
Community school food environment
Secondary database of food establishments from 2019 was extracted from the Rio de Janeiro State Treasure Secretariat (Secretaria Estadual da Fazenda). We classified establishments as food stores based on the National Classification of Economic Activities (Classificação Nacional de Atividades Econômicas, CNAEFootnote 1), which informs the main economic activity of each registered establishment.
Food establishments were classified in accordance with the Technical Study on Mapping Food Deserts in Brazil—CAISAN), in which categorizes establishments in three: (i) in natura establishments, which included butcheries, seafood shops, and fruits and vegetables establishments; (ii) ultraprocessed establishments, which included bars, snack bars, convenience stores, candy shops, and street vendors; and (iii) mixed establishments, which included markets, hypermarkets, mini markets, bakeries, restaurants and food stores (general food stores as natural and dietetic products, frozen foods, ice-cream stores, cake factories, warehouses, and commercial food stores with predominant retail of processed foods) .
Furthermore, the food environment around the schools was analyzed considering food deserts and swamps. Food deserts are defined as neighborhoods with limited access to healthy food, while food swamps are defined as neighborhoods with high availability of unhealthy foods . To determine food deserts, we adopted the methodology proposed by CAISAN, which calculates the density of healthy establishments per 10,000 inhabitants. Healthy establishments correspond to the sum of in natura acquisition and mixed establishments. Given the above, food deserts are the neighborhoods that are below the percentile for the distribution of the density of healthy establishments . In the present study, the 25th percentile corresponds to 27.35. To determine food swamps, on the other hand, we calculated the sum of convenience stores, snack bars, grocery stores and candy stores. When the sum of these establishments in the neighborhood was greater than four establishments, the neighborhood was classified as a food swamp [5, 6]. We thus used the adapted metric of Hager and colleagues (2017), first utilized by Peres and colleagues (2017). In this adapted metric, we account for the Brazilian context by adding Cafeterias (‘lanchonetes’)—food establishments largely characterized by the abundance of ultraprocessed foods. Additional results using mRFEI can be found in the Appendix.
Descriptive statistics are presented in absolute and relative frequencies. We calculated measures of central tendency as the median and the interquartile range (p25-p75). We compared relative frequencies using chi-square tests. The analyses were performed in SPSS 19.0, and maps were elaborated in QGis 2.14.9.
Figure 1 presents the distribution of public and private schools in the city of Rio de Janeiro, as well as the neighborhood spatial distributions of the inequality measures. From a visual inspection, we can observe that the wealthier and less segregated area is located in the south-east region, while the poorer and more segregated area is in the north-west of the city. However, we can also observe that there are regions of the city in which wealthy and poor regions nearly coexist.
Summary statistics of the distribution of schools and the three inequality measures are presented in Table 1. Most schools are public (55.0%), and located in neighborhoods of the highest income bracket (40.2%), medium deprivation index (64.1%) and medium segregation (44.7%).
Figure 2 displays the spatial distribution of food deserts and food swamps. Visually, we observe that there is a high prevalence of food swamps across neighborhoods of the city of Rio de Janeiro, but not quite as many food deserts.
Table 2 presents descriptive statistics of the neighborhood community school food environment. Most schools have at least one food establishment in its neighborhood (99.7%), in which 98.5% had a presence of at least one ultraprocessed establishment and 93.3% at least one in natura establishment. Across all establishment classifications, private schools have more establishments in their neighborhoods considering median values and interquartile ranges. While public schools had in their neighborhood a median of one in natura establishments, private ones had two. The same was true for the median of ultraprocessed (6 vs. 13) and mixed (10 vs. 17) establishments in public vs. private schools. Results from Table 2 also reveal a higher prevalence of establishments selling either ultraprocessed or mixed foods, when compared to predominantly in natura establishments.
Table 3 displays the presence of food deserts, food swamps, and both simultaneously at the school neighborhood community food environments. Overall, 474 (15%) of schools were in neighborhoods categorized as food deserts, 3094 (97%) were in neighborhoods categorized as food swamps, and 380 (12%) were in neighborhoods categorized by both food deserts and swamps simultaneously. Food deserts are more prevalent in neighborhoods of public vs. private schools (17.0 vs. 12.6%, p = 0.001); in neighborhoods of the lowest income tercile (34.5%) vs. middle tercile (9.2%) and highest tercile (8.7%, p < 0.001); in neighborhoods of high deprivation (32.7%) vs. medium (11.6%) and low (10.7%, p < 0.001); and in neighborhoods of high segregation (34.1%), vs. medium (12.6%) and low (12.0, p < 0.001).Food swamps, on the other hand, are much more prevalent overall, showing no statistical difference on the deprivation index (p = 0.231), and only a marginal difference on the segregation index (p = 0.079). However, food swamps are slightly more prevalent in private when compared to public schools (98.3% vs. 95.9%, p < 0.001), and in the middle and highest income terciles, compared to the first (99.6%, 99.1%, and 89.6%, respectively, p = 0.016).When assessing both food deserts and food swamps combined, we observe a similar pattern of results as in the food deserts. While we observe no significant differences in public and private schools (12.9% vs. 11%, p = 0.098), food deserts and food swamps are simultaneously more prevalent in neighborhoods of the lowest income tercile (24.2%) vs. middle tercile (8.7%) and highest tercile (7.8%, p < 0.001); in neighborhoods of high deprivation (25.7%) vs. medium (9.0%) and low (10.1%, p < 0.001); and in neighborhoods of high segregation (24.4%), vs. medium (9.7%) and low (10.9, p < 0.001).
Our study documents that the spatial socioeconomic disparities across neighborhoods of schools in Rio de Janeiro—measured by income terciles, and indices of segregation and deprivation – are associated with higher prevalence of both food deserts and food swamps. We find that food deserts and swamps are simultaneously more present in school’s neighborhoods of lower-income, higher deprivation, and higher segregation. This result offers additional evidence that spatial socioeconomic inequalities may influence school food environments, which thus far has not been a consensus in the literature [14, 16,17,18], which has mostly focused on high-income countries [14, 19].
Our contributions to the literature on spatial inequalities and community school food environments are twofold. First, we assess a unique context of LMIC countries with higher levels of inequality—which is the case of Brazil in general, and Rio de Janeiro in particular, a large city that is famous for its near coexistence of slum conglomerates and wealthy neighborhoods. Second, by utilizing novel measures of spatial inequality, we seek to help the development and the validation of new indices and propose a new lens to analyze the spatial inequalities within cities, and how they may be associated with food environments around schools.
While some studies show that more deprived schools display a higher prevalence of overall food outlets  and fast-food outlets  compared to higher-income ones in the US, others document the opposite in schools of New Zealand . In another Brazilian metropolis, Belo Horizonte, higher-income schools displayed a higher prevalence of all food stores (including restaurants, bars, and snack bars) in their vicinity, except for markets and supermarkets . Interestingly, having access to convenience stores with higher availability of energy-dense and nutrient-poor foods was frequently associated with overweight and obesity in Hispanic and Black youth. A systematic review shows that the higher presence of fast food outlets near US schools was associated with a higher prevalence of obesity among Latino, Black, and White students, with mixed results for Asian students . Furthermore, a recent systematic review presents mixed results  by documenting only few significant associations between food environment features and health outcomes.
In a cross-sectional study that examined the New York City food environment around homes and public schools, stratified by race/ethnicity and poverty status, low-income Black, Hispanic, and Asian students lived and attended schools located closer to nearly all food outlets (including corner stores, fast food outlets, wait service restaurants, and supermarkets), when compared to low-income White students , which implies that low-income non-white children were more likely to both live and study nearer food stores. In a sample of all public schools in the US, Hispanic students were also more likely to attend schools surrounded by convenience stores, restaurants, snack stores, and off-licenses . Using national data from public schools in the US, another study shows that students in higher poverty-level schools and those with majority Black and majority Hispanic students had lower access to unhealthy food outlets than students in higher income, majority White, and diverse schools. The results further suggest that high poverty and black and hispanic majority schools tend to be exposed to healthier school food environments than other types of school, even though nutritional quality of meals offered did not significantly differ between groups . Although we do not directly assess racial inequalities in our study, in Rio de Janeiro (and in Brazil) the most deprived areas are also those with higher proportions of Black residents.
Finally, our study is closely related to previous findings from Mexico  and Spain . In both studies and ours, we can observe that the amplification of deprivation and segregation lead to lower and poorer access to services and goods in the surroundings. In the context of three Mexican cities, school food environments of regions with higher poverty and lower educational attainment were associated with higher availability of ultraprocessed foods and beverages . In Madrid, schools located in poorer neighborhoods presented a higher density of unhealthy food establishments in their vicinity . In our study, we find that neighborhoods of lower-income, and highly deprived and segregated schools are more likely to present both food deserts and food swamps.
We provide novel evidence of a different set of inequalities mostly focused on income, spatial inequalities, and access to basic services. We show a consistency of prevalence in food deserts and swamps when considering all inequality measures: income, segregation, and deprivation. Taken together, our findings suggest that socioeconomic and spatial inequality measures can foster the understanding of the community food environments. Further research, however, is required to address mechanisms and causal links through longitudinal analyses, and to test the effectiveness of policies aiming at reducing such food availability inequalities. This would fill a gap that this study leaves as limitations. Though our study offers several insights, it provides correlational evidence of the association between socioeconomic indices and the community food environment around schools.
As an example of public policies tailored to improve the community food environment, member States of the World Health Organization (WHO) approved in 2010 a package of recommendations regarding targeted commercialization of food and non-alcoholic beverages to children and adolescents, including specific restrictions to the exposure of publicity of such foods and beverages at the school food environment—which comprises all spaces and facilities in and around schools where food and beverages are available to be sold and purchased . It is worth noting that Brazil is a reference country in creating effective policies that help ensure the supply of a healthy and adequate diet inside the public schools, mainly by the implementation of the National School Feeding Program , but not as much in private ones .
Further, the literature highlights the need to improve not only food environments inside schools, but also in its vicinities. For instance, modifying zoning regulations that restrict access to fast-food outlets around schools has been suggested as an effective policy to reduce unhealthy eating among school children in Quebec . More recently, the Food and Agriculture Organization of the United Nations (FAO) published a guide to develop law proposals targeting improvements of food security and nutrition at the school food environments, including their neighborhood’s food environments. The most relevant recommendations include urban public planning and new legislation that regulates commercial licensing that defines incentives and rules for a healthy retail food system, both inside and at the school’s vicinity . Importantly, by increasing access and availability of healthy products, such regulation may ensure (or at least contribute to) a healthier and more adequate diet for children and adolescents [1, 10,11,12, 14].
Therefore, our results, in line with recent UN guidelines, suggest that policy implications may involve increasing regulation in food environments across school’s neighborhoods and, finally, creating targeted and effective programs to reduce nutritional inequality across cities.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. All original data used is public. Data on schools are available at https://inepdata.inep.gov.br/analytics/saw.dll?dashboard. Data on neighborhood inequality measures are available at https://censo2010.ibge.gov.br/resultados.html, and on Deprivation index available at: https://researchdata.gla.ac.uk/980/. Data on the community food environment of Rio de Janeiro is not opened to the public, but available upon request at https://www.seeduc.rj.gov.br.
Glanz K, Sallis JF, Saelens BE, Frank LD. Healthy nutrition environments: concepts and measures. Am J Health Promot. 2005;19:330.
FAO. FAO School Food and Nutrition Framework. 2019;40.
Cooksey-Stowers K, Schwartz MB, Brownell KD. Food swamps predict obesity rates better than food deserts in the United States. Int J Environ Res Public Health. 2017;14:1–20.
Azeredo CM, de Rezende LFM, Canella DS, Claro RM, Peres MFT, Luiz O do C, et al. Food environments in schools and in the immediate vicinity are associated with unhealthy food consumption among Brazilian adolescents. Preventive Medicine. 2016;88:73–9.
Hager ER, Cockerham A, O’Reilly N, Harrington D, Harding J, Hurley KM, et al. Food swamps and food deserts in Baltimore City, MD, USA: Associations with dietary behaviours among urban adolescent girls. Public Health Nutr. 2017;20:2598–607.
Peres CM da C, Costa BV de L, Pessoa MC, Honório OS, Carmo AS do, Silva TPR da, et al. O ambiente alimentar comunitário e a presença de pântanos alimentares no entorno das escolas de uma metrópole brasileira. Cadernos de Saúde Pública. 2021;37.
Gebauer H, Laska MN. Convenience stores surrounding urban schools: An assessment of healthy food availability, advertising and product placement. J Urban Health. 2011;88:616–22.
Stowers KC, Jiang Q, Atoloye A, Lucan S, Gans K. Racial differences in perceived food swamp and food desert exposure and disparities in self-reported dietary habits. Int J Environ Res Public Health. 2020;17:1–14.
Jeffroy-Meynard A-C. Obesity, Food Swamp , and the Youth of Obesity, Food Swamps, and the Youth of Guatemala City. Seattle University Undergraduate Research Journal Volume. 3.
Barquera S, Hernández-Barrera L, Rothenberg SJ, Cifuentes E. The obesogenic environment around elementary schools: Food and beverage marketing to children in two Mexican cities. BMC Public Health. 2018;18:1–9.
Boone-Heinonen J, Gordon-Larsen P. Obesogenic environments in youth: Concepts and methods from a longitudinal national sample. Am J Prev Med. 2012;42:e37-46.
Chew A, Moran A, Barnoya J. Food swamps surrounding schools in three areas of guatemala. Prev Chronic Dis. 2018;2020(17):2018–21.
Cutumisu N, Traoré I, Paquette M-C, Cazale L, Camirand H, Lalonde B, et al. Association between junk food consumption and fast-food outlet access near school among Quebec secondary-school children: findings from the Quebec Health Survey of High School Students (QHSHSS) 2010–11. Public Health Nutr. 2017;20:927–37.
da Costa Peres CM, Gardone DS, Costa BV de L, Duarte CK, Pessoa MC, Mendes LL. Retail food environment around schools and overweight: a systematic review. Nutr Rev. 2020;0:1–16.
Lopes MS, Caiaffa WT, Andrade AC de S, do Carmo AS, Barber S, Mendes LL, et al. Spatial inequalities of retail food stores may determine availability of healthful food choices in a Brazilian metropolis. Public Health Nutr. 2021;1–12.
Drewnowski A. The cost of US foods as related to their nutritive value. Am J Clin Nutr. 2010;92:1181–8.
Larson NI, Story MT, Nelson MC. Neighborhood Environments. Disparities in Access to Healthy Foods in the U.S. Am J Prev Med. 2009;36:74–81.e10.
Vega-Salas MJ, Caro P, Johnson L, Papadaki A. Socioeconomic Inequalities in Dietary Intake in Chile: A Systematic Review. 2021.
Matsuzak M, Sánchez BN, Acosta ME, Botkin J, Sanchez-Vaznaugh EV. Food environment near schools and body weight — a systematic review of associations by race/ethnicity, gender, grade, and socioeconomic factors. Obes Rev. 2020;21:257–67.
IBGE. Metodologia do censo demográfico 2010. Rio de Janeiro: INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA - IBGE; 2016.
Barber S, Diez Roux AV, Cardoso L, Santos S, Toste V, James S, et al. At the intersection of place, race, and health in Brazil: Residential segregation and cardio-metabolic risk factors in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Soc Sci Med. 2018;199:67–76.
Allik M, Ramos D, Agranonik M, Ichihara MY, Barreto ML, Leyland AH, et al. Developing a small-area Deprivation Measure for Brazil. Technical Report. 2020.
Saúde SM de. Índice de Vulnerabilidade da Saúde 2012. Belo Horizonte; 2013.
CAISAN. Estudo Técnico Mapeamento dos Desertos Alimentares no Brasil. 2018.
CDC. Census Tract Level State Maps of the Modified Retail Food Environment Index (mRFEI). Atlanta; 2011.
Day PL, Pearce J. Obesity-promoting food environments and the spatial clustering of food outlets around schools. Am J Prev Med. 2011;40:113–21.
Sanchez-Vaznaugh EV, Weverka A, Matsuzaki M, Sánchez BN. Changes in Fast Food Outlet Availability Near Schools: Unequal Patterns by Income, Race/Ethnicity, and Urbanicity. Am J Prev Med. 2019;57:338–45.
Vandevijvere S, Sushil Z, Exeter DJ, Swinburn B. Obesogenic Retail Food Environments Around New Zealand Schools: A National Study. Am J Prev Med. 2016;51:e57-66.
Kraft AN, Thatcher EJ, Zenk SN. Neighborhood food environment and health outcomes in U.S. low-socioeconomic status, racial/ ethnic minority, and rural populations: A systematic review. J Health Care for the Poor and Underserved. 2020;31:1078–114.
Elbel B, Tamura K, McDermott ZT, Duncan DT, Athens JK, Wu E, et al. Disparities in food access around homes and schools for New York City children. PLoS ONE. 2019;14:1–17.
Sturm R. Disparities in the food environment surrounding US middle and high schools. Public Health. 2008;122:681–90.
Bardin S, Washburn L, Gearan E. Disparities in the healthfulness of school food environments and the nutritional quality of school lunches. Nutrients. 2020;12:1–15.
Barrera LH, Rothenberg SJ, Barquera S, Cifuentes E. The Toxic Food Environment Around Elementary Schools and Childhood Obesity in Mexican Cities. Am J Prev Med. 2016;51:264–70.
Díez J, Cebrecos A, Rapela A, Borrell LN, Bilal U, Franco M. Socioeconomic inequalities in the retail food environment around schools in a Southern European context. Nutrients. 2019;11.
Organização Pan-Americana da Saúde. Recomendações da consulta de especialistas da Organização Pan-Americana da Saúde sobre a promoção e a publicidade de alimentos e bebidas não alcoólicas para crianças nas Américas. Washington DC; 2012.
Benchmarking NZ Food environment policies against international best practice. Evidence summary for expert panel 2017–2019.
Andretti B, Goldszmidt RB, Andrad EB. How changes in menu quality associate with subsequent expenditure on (un)healthy foods and beverages in school cafeterias: A three-year longitudinal study. Preventive Medicine. 2021;146:10645.
FAO, IFAD, UNICEF, WFP, WHO. The State of Food Security and Nutrition in the World 2020. Transforming food systems for affordable healthy diets. 2020.
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Andretti, B., Cardoso, L.O., Honório, O.S. et al. Ecological study of the association between socioeconomic inequality and food deserts and swamps around schools in Rio de Janeiro, Brazil. BMC Public Health 23, 120 (2023). https://doi.org/10.1186/s12889-023-14990-8
- Community food environment
- Spatial neighborhood inequalities
- Food deserts
- Food swamps