The community food environment as an effect modifier of the relationship between racism and food insecurity among adults in Southern Brazil

The community food environment as an effect modifier of the relationship between racism and food insecurity among adults in Southern Brazil | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The community food environment as an effect modifier of the relationship between racism and food insecurity among adults in Southern Brazil Emanuele Bottega de Vargas, Mariane da Silva Dias, Ilaine Schuch, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4674282/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Oct, 2024 Read the published version in International Journal for Equity in Health → Version 1 posted 9 You are reading this latest preprint version Abstract Background We analyzed the relationship between racism, community food environment, and food insecurity in adults of different socioeconomic status. Methods This was a cross-sectional study of 400 adults aged 20–70 years residing in the central area of ​​Porto Alegre, the capital of Rio Grande do Sul state. Racism was measured by self-reported race/skin color and by using the Experiences of Discrimination scale (EOD). Food environment was assessed using the Nutrition Environment Measures Survey in Stores (NEMS-S) tool. Food insecurity was assessed using the short version of the Brazilian Food Insecurity Scale (EBIA for short, in Portuguese). Poisson regression with robust variance was employed for the multivariate analysis. Results The prevalence of food insecurity was higher in areas with a poorer food environment (areas 1 and 3; 56.6% and 58.8%, respectively). Racial discrimination was associated with food insecurity, where every 1-point increase in the discrimination score increased the likelihood of food insecurity by 7% (prevalence ratio [PR] 1.07; 95% CI, 1.03–1.20). When stratifying the analyses by food environment, racial discrimination was associated with food insecurity only in areas with a poorer food environment (PR 1.06; 95% CI, 1.01–1.10). Conclusions Experiences of racial discrimination were associated with a higher prevalence of food insecurity in the study population. The community food environment was an effect modifier of this relationship, highlighting the relevance of interventions in the food environment focused on areas with a greater presence of Black people as a way of combating racism and food insecurity. Racism Food Insecurity Social Determinants of Health Food Supply Food Deserts Figures Figure 1 Background Food and nutrition security is the right to have physical and economic access to sufficient, safe, and healthy food that meets one’s dietary needs and food preferences without compromising access to other essential needs. (1) Food insecurity occurs when the realization of this right is not achieved at the individual or population level and/or when the dietary quality is compromised. (2) About 29.6% of the world’s population was food insecure in 2022, for a total of 2.4 billion people. (3) In Brazil, 58.7% of the population was food insecure, corresponding to 125.2 million people. (4) Food insecurity has multiple social causes. A recent survey conducted by the Brazilian Research Network on Food and Nutrition Security and Sovereignty showed that 65.0% of households headed by Black people, mainly women-headed households, are affected by food insecurity, against 46.8% of households headed by White people. (5) In the United States, households headed by Black people are also the most affected by food insecurity, accounting for 22.4%, against 9.3% of households headed by White people. (6) This disparity is attributed to the consequences of racial discrimination against Black people observed in both countries. Racism can be defined as a system of unfair and avoidable oppression and discrimination, which assigns power and privilege to one group over another based on their race or ethnicity. (7, 8) Racism is often understood based on 3 main concepts: interpersonal racism (occurs as an ethical or psychological phenomenon, reduced to internalization or prejudiced expressions and behaviors by individuals), institutional racism (results from policies and practices carried out by institutions), and structural racism (occurs as the interconnection of organizational conditions and structures of society itself, at its social, economic, political, ideological, and ecological levels, resulting from political and historical processes). (9, 10, 11) Generally, when manifestations from all 3 concepts are taken together in society, racism is defined as systemic. Given that Brazil is the country that most compulsorily received enslaved African people into its territory and was the last one to abolish slavery, the African Brazilian population still faces the consequences of the legacy of this historical process with a wide range of persistent inequalities in different social domains, including food insecurity. (12) Alongside the association of racism with food insecurity, Black people also live in more degraded social environments. (13, 14, 15) Food environment serves as an interface between the food system and dietary practices, including the availability, affordability, convenience, and desirability of food. (16) Studies in the United States and Brazil have shown that the food environment in neighborhoods with predominantly Black residents is associated with a reduced number and variety of retail food stores, (13, 17, 18, 19, 20, 21) an increased number of fast food restaurants, (14) and reduced availability of fruits and vegetables. (22) These factors can contribute to the worsening of food insecurity and health of the Black population and reinforce structural mechanisms of racism in urban dynamics. (23) Also, a food environment with poor availability of food, especially healthy foods, and high food prices further contributes to food insecurity. (24) The occurrence of food insecurity has already been associated with the purchase of food in grocery stores and convenience stores and limited physical access to food stores in the neighborhood, situations also observed in the Brazilian reality. (21) In Brazil, one of the few studies to explore the association between food environment and food insecurity demonstrated that food insecurity is more common in populations living in regions with unhealthy food environments. (8) Therefore, racism appears to be related to both unhealthy food environments and higher prevalence of food insecurity. However, to our knowledge, no study has jointly explored the interrelated effects of racism and unhealthy food environments on the occurrence of food insecurity. The objective of this study was to analyze the relationship between racism, community food environment, and food insecurity in adults living in a capital city in southern Brazil. We also aimed to investigate the potential modifying effect of the food environment on the relationship between racism and food insecurity. Methods Study design This cross-sectional study included a sample of the population residing in the territory covered by the Santa Cecília primary health care (PHC) unit, located in the central area of ​​Porto Alegre, the capital of Rio Grande do Sul state, the southernmost state of Brazil. This was a 2-stage study. In the first stage, data were collected from the population residing in the coverage territory, followed by the identification and audit of all food retailers in the territory. This study is part of a larger research project titled “Social and environmental determinants of food and nutrition: an ecosocial approach,” which was approved by the Research Ethics Committee of Universidade Federal do Rio Grande do Sul, Brazil, (CAAE 46934015.3.0000.5347) in accordance with the Declaration of Helsinki. Each participant provided written informed consent prior to inclusion in the study. Study population Three PHC units are responsible for serving more than 260,000 residents of the central area of Porto Alegre, including the Santa Cecilia PHC unit, which serves 30,000 people. Part of these families lives in four geographically well-defined areas of lower socio-economic status (average per capita income, R $ 1700·00), whereas the remaining families live in higher-income areas (average/capita income, R $ 4000·00). Sample and sampling The sample size was calculated (n = 400) for a larger study. (25) For the present study, this sample size had an 80% power to detect a 10% difference in the prevalence ratio (PR) of the association between experiences of racial discrimination (exposure) and food insecurity (outcome). The inclusion criterion was individuals aged between 20 and 70 years of both sexes. The exclusion criteria were individuals with any physical or mental limitations that impeded data collection, as well as pregnant women. To ensure representation across different socioeconomic and environmental strata, a proportional sample of residents from lower and higher income areas was obtained. In the lower income areas (areas 1 and 3), with only 250 households, all eligible individuals were invited to participate in the study (census sampling); 201 individuals who agreed to participate were included (refusal rate, 16%). In the higher income areas (areas 2 and 4), an equal number of participants was included to ensure a proportional sample. A random sampling procedure was used to select the main sampling units (households) in these areas (refusal rate, 22%). Only one person per household was included. When more than one person in a household met the inclusion criteria, one individual was randomly selected for the interview, alternating between men and women in each household to enhance representation (i.e., whenever a woman was included, an attempt was made to include a man in the neighboring house and vice versa). The assessment of the food environment included all food retailers in the 4 areas under study. Data collection Participant data were collected in person between October 2018 and June 2019 using a standardized, pre-tested, pre-coded questionnaire with questions about sociodemographic and economic status, in addition to questions about racial discrimination. The first stage consisted of mapping the areas for addresses and locations of families served by the PHC unit, with the help of community health workers. The team then identified individuals who met the inclusion criteria and invited them to participate in the study. The questionnaire was administered either at the time of invitation or, if necessary, during an interview scheduled for completion at the participant’s own home or at the PHC unit. The questionnaire was applied only after the participant had agreed to participate and signed the informed consent form. Data from food retailers were collected between December 2019 and February 2020 by a team trained in the use and application of the tools. In the first stage, all food retailers in the territory covered by the PHC unit were mapped, identified, and audited by 2 researchers working in pairs, where one identified the food store and recorded its geographic coordinates while the other characterized it in terms of type, address, and business name. In the second stage, the food stores were visited and a standardized, pre-tested, pre-coded form was used to collect data on the price, quality, and availability of food items sold in the store. Assessment of food insecurity Food insecurity was assessed using the short version of the Brazilian Food Insecurity Scale (EBIA for short, in Portuguese), developed in 2014, tested and validated for use in the country. (26) The EBIA is a psychometric scale and its short version consists of 5 yes/no questions that generate a dichotomous assessment (presence or absence). In the absence of positive responses, the family is classified as food secure. In the presence of any positive response, the family is classified as food insecure. Assessment of racism Racism was assessed in 2 different ways: self-reported race/skin color and experiences of self-perceived racial discrimination. Self-reported race/skin color followed the classification of the Brazilian Institute of Geography and Statistics, in which the participants identified their race/skin color among the following options: White, Black, Brown (“ pardo” in Portuguese), Yellow, or Indigenous. For analysis, the responses were categorized into White, Black, and Brown due to the lack of Yellow or Indigenous responses. It is important to note that self-reported race/skin color serves as a proxy for racism, that is, as a social marker of groups that share experiences of oppression and discrimination based on race/ethnicity and historical and social processes of racialization. At no time should this variable be understood as a biological marker. (27) Experiences of racial discrimination were assessed using an adapted Portuguese version of the Experiences of Discrimination scale (EOD), developed in 1990 by Nancy Krieger and updated in 2005 by the author. The EOD is an 18-item self-report questionnaire that measures perceived experiences of discrimination based on race/ethnicity or skin color for population health research. (28) The scale was adapted and validated for use in the Brazilian population, (29) consisting of 13 items that cover 2 dimensions: experiences of discrimination (9 items) and worry about discrimination (4 items). Dimension 1 encompasses personal experiences of maltreatment or unfair treatment based on self-identified race, ethnicity, or skin color. The 9 items cover discrimination encountered across the following situations: at school, getting a job, at work, getting housing, getting medical care, getting service in a store or restaurant, getting credit, on the street or in a public setting, and from the police or in the courts. The response options for each situation are: never, once, 2 or 3 times, and 4 or more times. In this study, we only used dimension 1. The scores assigned to each response (0 to “never,” 1 to “once,” 2.5 to “2–3 times,” and 4 to “4 or more times”) were summed across the items, for a total score ranging from 0 to 30. Higher scores indicate more experiences of racial discrimination throughout the lifespan. The scale showed good validity for this population in a previous analysis. (11) Assessment of the community food environment The community food environment encompasses the distribution of food sources, that is, the number, type, location, and accessibility of food retailers, as well as their hours of operation and drive-through windows. (30) The assessment of the community food environment was based on the number and type of food retailers, categorized into 7 types, and the quality of food retailers through audit and application of a tool based on the Nutrition Environment Measures Survey in Stores (NEMS-S), developed by Glanz et al. and adapted and validated to assess the food environment in urban areas in Brazil. (31) The tool consists of a scoring system that classifies food stores according to the availability and price of 108 food items and the quality of fruits and vegetables, if available in the stores. The tool was adapted to the local food context by replacing some foods with others from the same food category, a replacement based on previous publications. (32, 33) The tool also allows us to classify food composition and characterize food items as unhealthy, intermediate, or healthy, assigning a negative score to unhealthy foods and a positive score to intermediate and healthy foods. The total score ranges from − 30 to 100 points for each food store, with higher scores indicating healthier food items available in the store. In this study, scores were obtained for the total territory and for each of the 4 household areas, categorized according to the NEMS-S score. Assessment of socioeconomic and demographic variables A questionnaire was used to assess the participants’ socioeconomic and demographic characteristics by asking questions about sex (female/male), age in completed years (categorized into age groups: 19–36, 37–49, 50–59, and > 60 years), marital status (with a partner [married/ consensual union] or without a partner [single/ separated/ divorced/ widowed]), education in completed years of schooling ( 11 years), and monthly family income (categorized according to the Brazilian minimum monthly salary: 5 minimum monthly salaries). Of note, the Brazilian minimum monthly salary denotes government regulation for a minimum monthly rate paid for a worker who works, on average, 44 hours per week for 4 weeks in a month. Statistical analysis The data were double entered and checked using EpiData version 3.5. Descriptive statistics were used to compare the characteristics of different household areas. Categorical variables were expressed as absolute (n) and relative frequencies (%), and numerical variables were expressed by measures of central tendency (mean and median) and dispersion (SD, IQR, and minimum and maximum). Pearson’s chi-square test or Kruskal-Wallis H test were used to evaluate the heterogeneity of proportions as needed. The multivariate model was based on a previously constructed directed acyclic graph (DAG) (Fig. 1 ). Crude and adjusted PRs were calculated for the associations between food environment (NEMS-S score), race/skin color, and racial discrimination using Poisson regression with robust variance, including the respective 95% CIs and Wald test for linear restrictions. The adjusted analysis controlled for the demographic and socioeconomic characteristics associated with food insecurity in the bivariate analysis and single-level theoretical model. Income and education were not considered confounding factors, as they were associated with the outcome and exposures and were part of the causal chain of associations. Therefore, they were not included in the adjusted model. Stratified analyses were performed to investigate the potential modifying effect of the food environment on the relationship between racial discrimination and food insecurity after a statistically significant interaction test (p < 0.001). All data were analyzed using Stata (StataCorp, College Station, TX, USA), version 18.0, and a p-value less than 5% (p < 0.05) was considered statistically significant. Results Table 1 shows the sociodemographic, food insecurity, racial discrimination, and community food environment characteristics of the total sample and by household area. Most participants were women (75%), with a mean (SD) age of 47 (13.98) years, did not have a partner (62.8%), had 11 years of schooling (39.9%), and had a monthly family income of 3 to 5 minimum monthly salaries (48.4%); 37.8% of the sample self-identified as Black or Brown. When stratifying by household area, area 1 (lower socioeconomic status) had a higher proportion of Black and Brown residents, lower education, and lower income. Table 1 Description of sociodemographic characteristics, racial discrimination, food insecurity, and community food environment. Socioeconomic status of household areas Lower Higher Total Area 1 (n = 108) Area 3 (n = 137) Area 2 (n = 51) Area 4 (n = 104) p-value Sex n,% 0.743 Men 100 25.0 24 22.2 33 24.1 15 29.4 28 26.9 Women 300 75.0 84 77.8 104 75.9 36 70.6 76 73.1 Age group (years) n,% < 0.001 19–36 105 26.3 31 28.7 46 33.6 16 31.4 12 11.5 37–49 97 24.3 32 29.6 46 33.6 12 23.5 15 14.4 50–59 110 27.5 25 23.2 38 27.7 6 11.8 38 36.5 ≥ 60 88 22.0 20 18.5 41 29.9 17 33.3 39 37.5 Marital status n,% 0.131 With a partner 149 37.3 40 37.0 58 42.3 12 23.5 39 37.5 Without a partner 251 62.8 68 63.0 79 57.7 39 76.5 65 62.5 Education (years of schooling), n = 395 n,% < 0.001 11 103 26.1 12 11.5 28 20.6 22 43.1 41 39.4 Family income (MMS) n,% < 0.001 5 72 18.0 8 7.4 21 15.4 15 29.4 28 26.9 Food insecurity n,% 0.002 Yes 205 51.2 61 56.5 80 58.8 26 51.0 37 35.6 No 195 47.8 47 43.5 57 41.2 25 49.0 67 64.4 Race/skin color n,% 0.001 White 249 62.3 51 47.2 82 59.9 41 80.4 75 72.1 Black 78 19.5 30 27.8 28 20.4 6 11.8 14 13.5 Brown 73 18.3 27 25.0 27 19.7 4 7.8 15 14.4 EOD Mean, SD 2.40 5.43 3.25 6.20 2.94 5.98 0.74 2.39 1.63 4.62 < 0.001** Food environment NEMS-S 0.424** Mean (SD) 17.6 23.9 6.6 19.6 17.9 25.7 23.2 23.6 19.6 25.4 Median (IQR) 21 (-7 to 39) 1.5 (-8 to 21) 22 (-12 to 39) 31 (-1 to 38) 20 (-1 to 43) Min-Max -17 to 62 -16 to 43 -16 to 54 -17 to 58 -15 to 62 NEMS-S, Nutrition Environment Measures Survey in Stores; MMS: minimum monthly salary; EOD: Experiences of Discrimination scale. ** P-value of the Kruskal–Wallis H test for comparison scores between areas. Regarding self-perceived racial discrimination, the mean (SD) EOD score was 2.4 (5.43), with areas 1 and 3 showing the highest means of discrimination, with mean (SD) scores of 3.25 (6.20) and 2.94 (5.98), respectively. Food insecurity was present in more than half of the participants (51.1%) and was also more prevalent in areas 1 (56.5%) and 3 (58.8%). As measured by the NEMS-S tool, the mean (SD) score of the community food environment for the availability of healthy and unhealthy items in the food retailers was 17.6 (23.9), with lower scores being observed in lower income areas (area 1: 6.6 points, SD 19.6; area 3: 17.9 points, SD 25.7) (Table 1 ). Table 2 shows the associations between socioeconomic variables and food insecurity. Food insecurity was more prevalent in women (55.3%), in those aged 19 to 49 years (56.9%), in lower income people (77.2%), and in Brown people (57.5%), although without statistical significance. The prevalence of food insecurity was higher in areas with a community food environment of poorer quality (area 1: 56.5%; area 3: 58.8%). The mean racial discrimination score was higher in households that were food insecure (2.93 points, SD 5.91) than food secure (1.84 points, SD 4.82). Table 2 Participants’ sociodemographic and economic characteristics and associations with food insecurity. Food Insecurity Yes No p-value Total n,% 400 205 51.2 195 47.8 Sex n,% 0.007 Men 100 25.0 39 39.0 61 61.0 Women 300 75.0 166 55.3 134 44.7 Age group (years) n,% < 0.001 19–36 105 26.3 58 55.2 47 44.7 37–49 97 24.3 57 58.8 40 41.2 50–59 110 27.5 66 60.0 44 40.0 ≥ 60 88 22.0 24 27.3 64 72.7 Education (years of schooling), n = 395 n,% 0.004 11 103 26.1 38 53.2 65 46.8 Family income (MMS) n,% < 0.001 5 72 18.0 17 23.6 55 76.4 Race/skin color n,% 0.372 White 249 62.3 121 48.6 128 51.4 Black 78 19.5 41 53.2 37 46.8 Brown 73 18.3 42 57.5 31 42.5 EOD Mean, SD 2.40 5.43 2.93 5.91 1.84 4.82 < 0.05 Food environment (NEMS-S) n,% 0.002 Area 1 (lowest score) 108 27.0 61 56.5 47 43.5 Area 3 137 34.3 80 58.8 57 41.2 Area 2 51 12.7 26 51.0 25 49.0 Area 4 (highest score) 104 26.0 37 35.6 67 64.4 NEMS-S, Nutrition Environment Measures Survey in Stores; MMS: minimum monthly salary; EOD: Experiences of Discrimination scale. As shown in Table 3 , there was an association between experiences of racial discrimination and higher prevalence of food insecurity, even after adjusting for potential confounders (PR 1.07; 95% CI, 1.03–1.20). Regarding household areas, participants residing in area 4 had a lower prevalence of food insecurity than those residing in area 1. When stratifying the analyses of the association between racial discrimination and food insecurity by the community food environment, the association remained statistically positive only for the households in areas with more unhealthy community food environment (PR 1.06; 95% CI, 1.01–1.10), even after adjusting for potential confounders. Table 3 Crude and adjusted prevalence ratios (PRs) and their respective 95% CIs for the association between race/skin color and experiences of racial discrimination and the prevalence of food insecurity according to the community food environment. Variable Food insecurity Crude PR (95% CI) p-value Adjusted PR* (95% CI) p-value* Racial discrimination 1.01 (1.00–1.03) 0.026 1.07 (1.03–1.20) < 0.001 Race/skin color White Black Brown 1 0.82 (0.43–1.56) 0.87 (0.46–1.63) 0.560 1 1.08 (0.84–1.38) 1.16 (0.93–1.45) 0.170 Food environment (NEMS-S) Area 1 (lowest score) Area 3 Area 2 Area 4 (highest score) 1 1.04 (0.84–1.29) 0.90 (0.65–1.23) 0.63 (0.46–0.85) 0.007 1 1.03 (0.83–1.28) 0.95 (0.70–1.29) 0.69 (0.50–0.95) 0.074 Lower NEMS score areas (1 + 3) EOD 1.06 (1.01–1.11) 0.007 1.06 (1.01–1.10) 0.015 Higher NEMS score areas (2 + 4) EOD 1.01 (0.99–1.03) 0.236 1.09 (0.98–1.22) 0.105 NEMS-S, Nutrition Environment Measures Survey in Stores, EOD: Experiences of Discrimination scale. *Adjusted for sex and age; Wald test. Discussion Our results indicate that racism is positively associated with a less healthy community food environment. Experiences of racial discrimination, which encompass the expression of interpersonal and institutional racism, were positively associated with food insecurity and, when evaluating this association stratified by household area, we observed a positive association only for households in areas with a poorer food environment (this association remained statistically significant even after adjusting for potential confounders). This result demonstrates that the food environment can be a modifier of the effect of the association. Based on our literature review, this is the first study to evaluate how the food environment can modify the effect of the association between racism and food insecurity. Previous studies have demonstrated a consistent association between a greater presence of Black people in the neighborhood and a poorer community food environment, (13, 14, 17, 18, 19, 20, 21, 22) in line with the results obtained in the present study. A scoping review conducted in 2023, including only studies carried out in the United States, concluded that 30% of the included studies associated one or more indicator of structural racism, such as gentrification and racial residential segregation, with geographic access to food retailers, while 70% of studies documented disparities in access to food retailers according to the neighborhood racial/ethnic composition. (34) Likewise, in Brazil, studies based on secondary data conducted in the Southeast, (35) Northeast, (21) and South (13) regions documented that a greater presence of Black people in the neighborhood was associated with the presence of food deserts, that is, regions characterized by limited availability and access to healthy and fresh food. In Porto Alegre (South region), areas with a higher percentage of Black and Indigenous people are twice as likely to be classified as a food desert. (13) The literature has characterized the poor availability of healthy food options in areas with a greater proportion of Black residents as a manifestation of structural racism. (13, 23) By using race/skin color as a variable to describe the household areas with a greater or lesser percentage of Black residents, we were able to identify an important part of the action of structural racism, which occurs through spatial segregation by providing residents with poor access to food products, services, and options in different areas or keeping access narrowed to ecologically degraded areas. In this study, structural racism operated through Black people’s difficulty in having access to healthy foods, resulting in a higher prevalence of food insecurity. (9) Our findings regarding the association between racial discrimination and food insecurity corroborate those of international studies. In 2019, Phojanakong et al. performed a cross-sectional study in the United States showing that household food insecurity in mothers who reported experiences of discrimination was twice that in mothers without such experiences. (36) Our study demonstrated that each 1-point increase in the discrimination score was associated with a 7% increase in the likelihood of food insecurity. In line with this finding, Burke et al., in a cross-sectional study of Black people living in South Carolina, United States, found that each 1-point increase in the self-perceived racial discrimination scale was associated with a 5% increase in the odds of being food insecure. (37) In Brazil, to our knowledge, there are no studies relating measures of experiences of racial discrimination to food insecurity. However, nationally representative data have previously demonstrated a higher prevalence of food insecurity among Black individuals, which indicates, as stated earlier, its relationship with structural racism. (38, 39, 40, 41, 42, 43) In our study, although prevalence rates were also higher among Brown and Black people, the result was not statistically significant. Black people in Brazil have lower wages, about 30% lower than those of White people, occupy only 29.5% of management positions, have higher poverty rates, reaching 34.5% for Black people and 38.4% for Brown people, and experience more physical, psychological, and sexual violence. (36, 44) The whole scenario, consisting of different indicators of institutional and structural racism, increases the vulnerability of the Black population to food insecurity in a country already widely affected by it. (9) Considering the multiple causes of food insecurity, the community food environment was shown to be an important factor in the relationship with racism, since the association between food insecurity and racial discrimination was only present among residents of areas with a poorer food environment. Physical access to food retailers that primarily sell healthy and culturally appropriate foods is essential to ensure food security. (45) However, people experiencing racism may have difficulty accessing these food stores, a factor that contributes to increased food insecurity. Racial residential segregation in the city of Porto Alegre, a product of institutional and structural racism, has hampered urban mobility, access to retailers and services, and the availability of better employment and housing opportunities for Black people for decades. (46) There are 2 main ways of measuring the consequences of racism in the health field, both of which were used in this study to analyze food insecurity. The first is through the association between race/skin color and the respective study outcome, considered a measure of structural racism, as it is not possible to identify the specific mechanisms and interventions for the outcome. These studies define race as a proxy for racism, identifying specific groups or populations that experience certain forms of oppression and discrimination and, as a result, also experience health inequalities. The second is based on psychometric questionnaires that measure experiences of discrimination, considered a more direct way of assessing racial discrimination in its multiple expressions and dimensions (interpersonal, internalized, or institutional racism). (27, 47, 48) Direct assessment of discrimination using psychometric instruments is a growing trend in research. Their use is supported by the need to understand the stressful experiences related to discrimination and their association with negative health outcomes. This allows for the investigation and construction of more detailed mechanisms on how discrimination operates in the study population. (47) In the present study, only the variable ‘experiences of racial discrimination’ was associated with food insecurity, unlike the variable ‘race/skin color’. This finding suggests that instruments that measure racial discrimination should be incorporated into future studies investigating the health of the Black population. (49) Conversely, it also suggests that other instruments and indicators are needed to understand the role of structural racism. Finally, the findings revealed a high prevalence of food insecurity across the entire study population. The results are in line with reports in Brazil and worldwide. In Brazil, food insecurity increased from 20.6% in 2017 to 32.8% in 2022, while globally its prevalence increased from 21.7% in 2015 to 29.6% in 2022. (50) This demonstrates the growing importance of monitoring and understanding the factors associated with food insecurity to implement public food and nutrition policies focused on the most vulnerable populations. Limitations and strengths of the study This study has limitations and strengths. Strengths include (1) the use of 2 variables to measure and analyze the effects of the multiple dimensions of racism (interpersonal, institutional, and structural) on the study outcome, (2) the use of validated questionnaires for the study population to measure the outcome and exposures, and (3) the assessment of the community food environment through an audit of all food retailers, with this measure being more robust than assessment using secondary data, which only evaluates the presence of retailers. Limitations include (1) the enrollment of a study population that is not representative of the general population, but the explanatory mechanisms of associations may be used to help understand similar contexts in low- and middle-income countries, (2) the use of the variable ‘race/skin color’ as a proxy for structural racism, which hinders the development of detailed mechanisms about its effects and the most appropriate interventions, and (3) the use of the short version of the EBIA, which did not allow us to classify individuals according to different levels of food insecurity. Conclusion This study aimed to analyze the relationship between racism and food insecurity. We found that racism, as an experience of racial discrimination at the interpersonal and institutional levels, was associated with a higher prevalence of food insecurity in the study population. We also found that the food environment, in areas with a greater presence of Black people, was an effect modifier of this relationship, which indicates the action of structural racism. The results highlight the need for public food security policies focused on areas with a greater presence of Black people as a way to reduce racial inequalities in health and to promote access to adequate and healthy food for all. Abbreviations DAG directed acyclic graph EBIA Brazilian Food Insecurity Scale EOD Experiences of Discrimination scale NEMS S–Nutrition Environment Measures Survey in Stores PHC primary health care PR prevalence ratio Declarations Ethics approval and consent to participate This study is part of a larger research project titled “Study of the social and environmental determinants of food and nutrition: an ecosocial approach,” which was approved by the Research Ethics Committee of Universidade Federal do Rio Grande do Sul (UFRGS), under number CAAE 46934015.3.0000.5347. Each participant provided written informed consent prior to inclusion in the study. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study received financial support from the Brazilian National Council for Scientific and Technological Development (CNPq, Grant number. 420342/2018-4) and Foundation for Research Support of the State of Rio Grande do Sul - FAPERGS. Grant number: 21/2551-0000520-3. The funders had no role in study design, data collection and analysis, decision to publish and the preparation or approval of the manuscript. Authors' contributions I.S., P.B.Z.R. and R.C. conceived the objective and processed data of this study; E.B.V., P.B.Z.R., R.C. and I.S. collected data. E.B.V, R.C. and M.S.D. analyzed data and drafted the manuscript; E.B.V., R.C., M.S.D. and M.F. interpreted the findings and reviewed the manuscript for important intellectual content. All authors read and approved the final manuscript. Acknowledgements Not applicable. References ‌ 1. Food and Agriculture Organization of the United Nations. Rome Declaration and Plan of Action on World Food Security. FAO. 1996. https://www.fao.org/3/w3613e/w3613e00.htm. Accessed 21 Jan. 2024. Food and Agriculture Organization of the United Nations. Hunger and food insecurity. Food and Agriculture Organization of the United Nations. 2022. https://www.fao.org/hunger/en/FAO. Accessed 21 Jan 2024. FAO, IFAD, UNICEF, WFP, WHO. The State of Food Security and Nutrition in the World 2023. Urbanization, agrifood systems transformation and healthy diets across the rural-urban continuum. Food and Agriculture Organization. 2023. https://openknowledge.fao.org/bitstreams/8b27c570-2f8b-4350-8d5a-8e82432e6db7/download. 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Public Health Nutrition. 2022; doi:10.1017/S1368980022002130. Rosa PBZ, Schuch I, Cunha CML, Rodrigues YE, Fanton M, Canuto R. Social environment perception and associations with overweight in the city of Porto Alegre, Brazil. J Biosoc Sci. 2022; doi: 10.1017/S0021932021000419. dos Santos LP, Lindemann IL, Motta JVdS, Mintem G, Bender E, Gigante DP. Proposal of a short-form version of the Brazilian Food Insecurity Scale. Rev Saúde Pública. 2014; doi: 10.1590/S0034-8910.2014048005195. Lett E, Asabor E, Beltrán S, Cannon AM, Arah OA. Conceptualizing, Contextualizing, and Operationalizing Race in Quantitative Health Sciences Research. Ann Fam Med. 2022; doi: 10.1370/afm.2792. Krieger N, Smith K, Naishadham D, Hartman C, Barbeau EM. Experiences of discrimination: validity and reliability of a self-report measure for population health research on racism and health. Soc Sci Med. 2005; doi: 10.1016/j.socscimed.2005.03.006. Fattore GL, Teles CA, Santos DN dos, Santos LM, Reichenheim ME, Barreto ML. Validade de constructo da escala Experiences of Discrimination em uma população brasileira. Cad Saúde Pública. 2016; doi: 10.1590/0102-311X00102415. Glanz K, Sallis JF, Saelens BE, Frank LD. Healthy Nutrition Environments: Concepts and Measures. American Journal of Health Promotion. 2005; doi: 10.4278/0890-1171-19.5.330 Martins PA, Cremm EC, Leite FH, Maron LR, Scagliusi FB, Oliveira MA. Validation of an adapted version of the nutrition environment measurement tool for stores (NEMS-S) in an urban area of Brazil. J Nutr Educ Behav. 2013; doi: 10.1016/j.jneb.2013.02.010. Perozzo G, Olinto MTA, Dias-da-Costa JS, Henn RL, Sarriera J, Pattussi MP. Associação dos padrões alimentares com obesidade geral e abdominal em mulheres residentes no Sul do Brasil. Cad Saúde Pública. 2008; doi: 10.1590/S0102-311X2008001000023. Hoffmann JF, Nunes MA, Schmidt MI, Olinto MT, Melere C, Ozcariz SG, Buss C, Drhemer M, Manzolli P, Soares RM, Pinheiro AP, Camey S. Dietary patterns during pregnancy and the association with sociodemographic characteristics among women attending general practices in southern Brazil: the ECCAGe Study. Cad Saude Publica. 2013; doi: 10.1590/S0102-311X2013000500014. Singleton CR, Wright LA, McDonald M, Archer IG, Bell CN, McLoughlin GM, Houghtaling B, Cooksey Stowers K, Anderson Steeves E. Structural racism and geographic access to food retailers in the United States: A scoping review. Health Place. 2023; doi: 10.1016/j.healthplace.2023.103089. Honório OS, Pessoa MC, Gratão LHA, Rocha LL, de Castro IRR, Canella DS, et al. Social inequalities in the surrounding areas of food deserts and food swamps in a Brazilian metropolis. International Journal for Equity in Health. 2021; doi: 10.1186/s12939-021-01501-7. Phojanakong P, Brown Weida E, Grimaldi G, Lê-Scherban F, Chilton M. Experiences of Racial and Ethnic Discrimination Are Associated with Food Insecurity and Poor Health. Int J Environ Res Public Health. 2019; doi: 10.3390/ijerph16224369. Burke MP, Jones SJ, Frongillo EA, Fram MS, Blake CE, Freedman DA. Severity of household food insecurity and lifetime racial discrimination among African-American households in South Carolina. Ethnicity & health. 2016; doi: 10.1080/13557858.2016.1263286. da Silva SO, dos Santos SMC, Gama CM, Coutinho GR, dos Santos MEP, Silva NdJ. A cor e o sexo da fome: análise da insegurança alimentar sob o olhar da interseccionalidade. Cad Saúde Pública. 2022; doi: 10.1590/0102-311XPT255621. Rosa TEdC, Mondini L, Gubert MB, Sato GS, Benício MHD. Segurança alimentar em domicílios chefiados por idosos, Brasil. Rev bras geriatr gerontol. 2012; doi: 10.1590/S1809-98232012000100008. Panigassi G, Segall-Corrêa AM, Marin-León L, Pérez-Escamilla R, Sampaio MdFA, Maranha LK. Insegurança alimentar como indicador de iniqüidade: análise de inquérito populacional. Cad Saúde Pública. 2008; doi: 10.1590/S0102-311X2008001000018. Santos LA, Ferreira AA, Pérez-Escamilla R, Sabino LL, de Oliveira LG, Salles-Costa R. Interseções de gênero e raça/cor em insegurança alimentar nos domicílios das diferentes regiões do Brasil. Cad Saúde Pública. 2022; doi: 10.1590/0102-311XPT130422. Coutinho GR, dos Santos SMC, Gama CM, da Silva SO, dos Santos MEP, Silva NdJ. Fatores demográficos e socioambientais associados à insegurança alimentar domiciliar nos diferentes territórios da cidade de Salvador, Bahia, Brasil. Cadernos de Saúde Pública. 2022; doi: 10.1590/0102-311XPT280821. Fernandes RC, Manera F, Boing L, Höfelmann DA. Socioeconomic, demographic, and obstetric inequalities in food insecurity in pregnant women. Rev Bras Saude Mater Infant. 2018; doi: 10.1590/1806-93042018000400008. IBGE. Instituto Brasileiro de Geografia e Estatística. Desigualdades sociais por cor ou raça no Brasil. IBGE. 2022. https://educa.ibge.gov.br/jovens/conheca-o-brasil/populacao/18319-cor-ou-raca.html. Accessed 21 Jan 2024. Karpyn AE, Riser D, Tracy T, Wang R, Shen YE. The changing landscape of food deserts. UNSCN Nutr. 2019;44:46-53. Polidoro M, Canavese D. Porto Alegre’s sociopolitical urbanism and neoliberal economic dynamics: Perspectives from a local community in the Afro-Brazilian periphery. Local Economy: The Journal of the Local Economy Policy Unit. 2017; doi: 10.1177/0269094217735477. Bastos JL, Celeste RK, Faerstein E, Barros AJD. Racial discrimination and health: A systematic review of scales with a focus on their psychometric properties. Social Science & Medicine. 2010; doi: 10.1016/j.socscimed.2009.12.020. Cuevas AG, Boen C. Tip of the iceberg: measuring racial discrimination in studies of health. Stress And Health. 2021; doi: 10.1002/smi.3047. Rodrigues YE, Fanton M, Novossat RS, Canuto R. Perceived racial discrimination and eating habits: a systematic review and conceptual models. Nutr Rev. 2022; doi: 10.1093/nutrit/nuac001. Food and Agriculture Organization. FAO Hunger Map [Internet]. 2023. https://www.fao.org/interactive/state-of-food-security-nutrition/2-1-1/en/. Accessed 05 Jan 2024. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 31 Oct, 2024 Read the published version in International Journal for Equity in Health → Version 1 posted Editorial decision: Revision requested 30 Aug, 2024 Reviews received at journal 29 Aug, 2024 Reviews received at journal 29 Aug, 2024 Reviewers agreed at journal 20 Aug, 2024 Reviewers agreed at journal 19 Aug, 2024 Reviewers invited by journal 19 Aug, 2024 Editor assigned by journal 09 Jul, 2024 Submission checks completed at journal 04 Jul, 2024 First submitted to journal 02 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4674282","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":328308005,"identity":"4dcf1b0d-d747-4af5-9d1c-03af29862262","order_by":0,"name":"Emanuele Bottega de Vargas","email":"","orcid":"","institution":"Universidade Federal do Rio Grande do Sul (UFRGS)","correspondingAuthor":false,"prefix":"","firstName":"Emanuele","middleName":"Bottega","lastName":"de Vargas","suffix":""},{"id":328308006,"identity":"eb22a4bf-6af8-467f-95a1-12df2e6e7217","order_by":1,"name":"Mariane da Silva 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12:37:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4674282/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4674282/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12939-024-02311-3","type":"published","date":"2024-10-31T16:20:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61306707,"identity":"fbac31c4-f404-4ec9-bcf8-565800854b33","added_by":"auto","created_at":"2024-07-29 10:12:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80717,"visible":true,"origin":"","legend":"\u003cp\u003eDirected acyclic graph (DAG) representing the association between racism, community food environment, and food insecurity.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4674282/v1/e29f537002e721883f59785f.png"},{"id":68207224,"identity":"fc9db4b3-33dd-4323-9779-1899639fa6cb","added_by":"auto","created_at":"2024-11-04 16:35:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":944410,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4674282/v1/93fc6b75-1fc2-41a1-bdd1-ff0de821bb42.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The community food environment as an effect modifier of the relationship between racism and food insecurity among adults in Southern Brazil","fulltext":[{"header":"Background","content":"\u003cp\u003eFood and nutrition security is the right to have physical and economic access to sufficient, safe, and healthy food that meets one\u0026rsquo;s dietary needs and food preferences without compromising access to other essential needs.\u003csup\u003e(1)\u003c/sup\u003e Food insecurity occurs when the realization of this right is not achieved at the individual or population level and/or when the dietary quality is compromised.\u003csup\u003e(2)\u003c/sup\u003e About 29.6% of the world\u0026rsquo;s population was food insecure in 2022, for a total of 2.4\u0026nbsp;billion people.\u003csup\u003e(3)\u003c/sup\u003e In Brazil, 58.7% of the population was food insecure, corresponding to 125.2\u0026nbsp;million people.\u003csup\u003e(4)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFood insecurity has multiple social causes. A recent survey conducted by the Brazilian Research Network on Food and Nutrition Security and Sovereignty showed that 65.0% of households headed by Black people, mainly women-headed households, are affected by food insecurity, against 46.8% of households headed by White people.\u003csup\u003e(5)\u003c/sup\u003e In the United States, households headed by Black people are also the most affected by food insecurity, accounting for 22.4%, against 9.3% of households headed by White people.\u003csup\u003e(6)\u003c/sup\u003e This disparity is attributed to the consequences of racial discrimination against Black people observed in both countries.\u003c/p\u003e \u003cp\u003eRacism can be defined as a system of unfair and avoidable oppression and discrimination, which assigns power and privilege to one group over another based on their race or ethnicity.\u003csup\u003e(7, 8)\u003c/sup\u003e Racism is often understood based on 3 main concepts: interpersonal racism (occurs as an ethical or psychological phenomenon, reduced to internalization or prejudiced expressions and behaviors by individuals), institutional racism (results from policies and practices carried out by institutions), and structural racism (occurs as the interconnection of organizational conditions and structures of society itself, at its social, economic, political, ideological, and ecological levels, resulting from political and historical processes).\u003csup\u003e(9, 10, 11)\u003c/sup\u003e Generally, when manifestations from all 3 concepts are taken together in society, racism is defined as systemic. Given that Brazil is the country that most compulsorily received enslaved African people into its territory and was the last one to abolish slavery, the African Brazilian population still faces the consequences of the legacy of this historical process with a wide range of persistent inequalities in different social domains, including food insecurity.\u003csup\u003e(12)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAlongside the association of racism with food insecurity, Black people also live in more degraded social environments.\u003csup\u003e(13, 14, 15)\u003c/sup\u003e Food environment serves as an interface between the food system and dietary practices, including the availability, affordability, convenience, and desirability of food.\u003csup\u003e(16)\u003c/sup\u003e Studies in the United States and Brazil have shown that the food environment in neighborhoods with predominantly Black residents is associated with a reduced number and variety of retail food stores,\u003csup\u003e(13, 17, 18, 19, 20, 21)\u003c/sup\u003e an increased number of fast food restaurants,\u003csup\u003e(14)\u003c/sup\u003e and reduced availability of fruits and vegetables.\u003csup\u003e(22)\u003c/sup\u003e These factors can contribute to the worsening of food insecurity and health of the Black population and reinforce structural mechanisms of racism in urban dynamics.\u003csup\u003e(23)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAlso, a food environment with poor availability of food, especially healthy foods, and high food prices further contributes to food insecurity.\u003csup\u003e(24)\u003c/sup\u003e The occurrence of food insecurity has already been associated with the purchase of food in grocery stores and convenience stores and limited physical access to food stores in the neighborhood, situations also observed in the Brazilian reality.\u003csup\u003e(21)\u003c/sup\u003e In Brazil, one of the few studies to explore the association between food environment and food insecurity demonstrated that food insecurity is more common in populations living in regions with unhealthy food environments.\u003csup\u003e(8)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTherefore, racism appears to be related to both unhealthy food environments and higher prevalence of food insecurity. However, to our knowledge, no study has jointly explored the interrelated effects of racism and unhealthy food environments on the occurrence of food insecurity. The objective of this study was to analyze the relationship between racism, community food environment, and food insecurity in adults living in a capital city in southern Brazil. We also aimed to investigate the potential modifying effect of the food environment on the relationship between racism and food insecurity.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis cross-sectional study included a sample of the population residing in the territory covered by the Santa Cec\u0026iacute;lia primary health care (PHC) unit, located in the central area of ​​Porto Alegre, the capital of Rio Grande do Sul state, the southernmost state of Brazil.\u003c/p\u003e \u003cp\u003eThis was a 2-stage study. In the first stage, data were collected from the population residing in the coverage territory, followed by the identification and audit of all food retailers in the territory. This study is part of a larger research project titled \u0026ldquo;Social and environmental determinants of food and nutrition: an ecosocial approach,\u0026rdquo; which was approved by the Research Ethics Committee of Universidade Federal do Rio Grande do Sul, Brazil, (CAAE 46934015.3.0000.5347) in accordance with the Declaration of Helsinki. Each participant provided written informed consent prior to inclusion in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThree PHC units are responsible for serving more than 260,000 residents of the central area of Porto Alegre, including the Santa Cecilia PHC unit, which serves 30,000 people. Part of these families lives in four geographically well-defined areas of lower socio-economic status (average per capita income, R\u003cspan\u003e$\u003c/span\u003e 1700\u0026middot;00), whereas the remaining families live in higher-income areas (average/capita income, R\u003cspan\u003e$\u003c/span\u003e 4000\u0026middot;00).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSample and sampling\u003c/h2\u003e \u003cp\u003eThe sample size was calculated (n\u0026thinsp;=\u0026thinsp;400) for a larger study.\u003csup\u003e(25)\u003c/sup\u003e For the present study, this sample size had an 80% power to detect a 10% difference in the prevalence ratio (PR) of the association between experiences of racial discrimination (exposure) and food insecurity (outcome).\u003c/p\u003e \u003cp\u003eThe inclusion criterion was individuals aged between 20 and 70 years of both sexes. The exclusion criteria were individuals with any physical or mental limitations that impeded data collection, as well as pregnant women. To ensure representation across different socioeconomic and environmental strata, a proportional sample of residents from lower and higher income areas was obtained.\u003c/p\u003e \u003cp\u003eIn the lower income areas (areas 1 and 3), with only 250 households, all eligible individuals were invited to participate in the study (census sampling); 201 individuals who agreed to participate were included (refusal rate, 16%). In the higher income areas (areas 2 and 4), an equal number of participants was included to ensure a proportional sample. A random sampling procedure was used to select the main sampling units (households) in these areas (refusal rate, 22%). Only one person per household was included. When more than one person in a household met the inclusion criteria, one individual was randomly selected for the interview, alternating between men and women in each household to enhance representation (i.e., whenever a woman was included, an attempt was made to include a man in the neighboring house and vice versa).\u003c/p\u003e \u003cp\u003eThe assessment of the food environment included all food retailers in the 4 areas under study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eParticipant data were collected in person between October 2018 and June 2019 using a standardized, pre-tested, pre-coded questionnaire with questions about sociodemographic and economic status, in addition to questions about racial discrimination.\u003c/p\u003e \u003cp\u003eThe first stage consisted of mapping the areas for addresses and locations of families served by the PHC unit, with the help of community health workers. The team then identified individuals who met the inclusion criteria and invited them to participate in the study. The questionnaire was administered either at the time of invitation or, if necessary, during an interview scheduled for completion at the participant\u0026rsquo;s own home or at the PHC unit. The questionnaire was applied only after the participant had agreed to participate and signed the informed consent form.\u003c/p\u003e \u003cp\u003eData from food retailers were collected between December 2019 and February 2020 by a team trained in the use and application of the tools. In the first stage, all food retailers in the territory covered by the PHC unit were mapped, identified, and audited by 2 researchers working in pairs, where one identified the food store and recorded its geographic coordinates while the other characterized it in terms of type, address, and business name.\u003c/p\u003e \u003cp\u003eIn the second stage, the food stores were visited and a standardized, pre-tested, pre-coded form was used to collect data on the price, quality, and availability of food items sold in the store.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of food insecurity\u003c/h2\u003e \u003cp\u003eFood insecurity was assessed using the short version of the Brazilian Food Insecurity Scale (EBIA for short, in Portuguese), developed in 2014, tested and validated for use in the country.\u003csup\u003e(26)\u003c/sup\u003e The EBIA is a psychometric scale and its short version consists of 5 yes/no questions that generate a dichotomous assessment (presence or absence). In the absence of positive responses, the family is classified as food secure. In the presence of any positive response, the family is classified as food insecure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of racism\u003c/h2\u003e \u003cp\u003eRacism was assessed in 2 different ways: self-reported race/skin color and experiences of self-perceived racial discrimination. Self-reported race/skin color followed the classification of the Brazilian Institute of Geography and Statistics, in which the participants identified their race/skin color among the following options: White, Black, Brown (\u0026ldquo;\u003cem\u003epardo\u0026rdquo;\u003c/em\u003e in Portuguese), Yellow, or Indigenous. For analysis, the responses were categorized into White, Black, and Brown due to the lack of Yellow or Indigenous responses. It is important to note that self-reported race/skin color serves as a proxy for racism, that is, as a social marker of groups that share experiences of oppression and discrimination based on race/ethnicity and historical and social processes of racialization. At no time should this variable be understood as a biological marker.\u003csup\u003e(27)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eExperiences of racial discrimination were assessed using an adapted Portuguese version of the Experiences of Discrimination scale (EOD), developed in 1990 by Nancy Krieger and updated in 2005 by the author. The EOD is an 18-item self-report questionnaire that measures perceived experiences of discrimination based on race/ethnicity or skin color for population health research.\u003csup\u003e(28)\u003c/sup\u003e The scale was adapted and validated for use in the Brazilian population,\u003csup\u003e(29)\u003c/sup\u003e consisting of 13 items that cover 2 dimensions: experiences of discrimination (9 items) and worry about discrimination (4 items). Dimension 1 encompasses personal experiences of maltreatment or unfair treatment based on self-identified race, ethnicity, or skin color. The 9 items cover discrimination encountered across the following situations: at school, getting a job, at work, getting housing, getting medical care, getting service in a store or restaurant, getting credit, on the street or in a public setting, and from the police or in the courts. The response options for each situation are: never, once, 2 or 3 times, and 4 or more times. In this study, we only used dimension 1. The scores assigned to each response (0 to \u0026ldquo;never,\u0026rdquo; 1 to \u0026ldquo;once,\u0026rdquo; 2.5 to \u0026ldquo;2\u0026ndash;3 times,\u0026rdquo; and 4 to \u0026ldquo;4 or more times\u0026rdquo;) were summed across the items, for a total score ranging from 0 to 30. Higher scores indicate more experiences of racial discrimination throughout the lifespan. The scale showed good validity for this population in a previous analysis.\u003csup\u003e(11)\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eAssessment of the community food environment\u003c/h2\u003e \u003cp\u003eThe community food environment encompasses the distribution of food sources, that is, the number, type, location, and accessibility of food retailers, as well as their hours of operation and drive-through windows.\u003csup\u003e(30)\u003c/sup\u003e The assessment of the community food environment was based on the number and type of food retailers, categorized into 7 types, and the quality of food retailers through audit and application of a tool based on the Nutrition Environment Measures Survey in Stores (NEMS-S), developed by Glanz et al. and adapted and validated to assess the food environment in urban areas in Brazil.\u003csup\u003e(31)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e The tool consists of a scoring system that classifies food stores according to the availability and price of 108 food items and the quality of fruits and vegetables, if available in the stores. The tool was adapted to the local food context by replacing some foods with others from the same food category, a replacement based on previous publications.\u003csup\u003e(32, 33)\u003c/sup\u003e The tool also allows us to classify food composition and characterize food items as unhealthy, intermediate, or healthy, assigning a negative score to unhealthy foods and a positive score to intermediate and healthy foods. The total score ranges from \u0026minus;\u0026thinsp;30 to 100 points for each food store, with higher scores indicating healthier food items available in the store. In this study, scores were obtained for the total territory and for each of the 4 household areas, categorized according to the NEMS-S score.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of socioeconomic and demographic variables\u003c/h2\u003e \u003cp\u003eA questionnaire was used to assess the participants\u0026rsquo; socioeconomic and demographic characteristics by asking questions about sex (female/male), age in completed years (categorized into age groups: 19\u0026ndash;36, 37\u0026ndash;49, 50\u0026ndash;59, and \u0026gt;\u0026thinsp;60 years), marital status (with a partner [married/ consensual union] or without a partner [single/ separated/ divorced/ widowed]), education in completed years of schooling (\u0026lt;\u0026thinsp;8, 8\u0026ndash;10, 11, and \u0026gt;\u0026thinsp;11 years), and monthly family income (categorized according to the Brazilian minimum monthly salary: \u0026lt; 1, 1\u0026ndash;2, 3\u0026ndash;5, and \u0026gt;\u0026thinsp;5 minimum monthly salaries). Of note, the Brazilian minimum monthly salary denotes government regulation for a minimum monthly rate paid for a worker who works, on average, 44 hours per week for 4 weeks in a month.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe data were double entered and checked using EpiData version 3.5. Descriptive statistics were used to compare the characteristics of different household areas. Categorical variables were expressed as absolute (n) and relative frequencies (%), and numerical variables were expressed by measures of central tendency (mean and median) and dispersion (SD, IQR, and minimum and maximum). Pearson\u0026rsquo;s chi-square test or Kruskal-Wallis H test were used to evaluate the heterogeneity of proportions as needed.\u003c/p\u003e \u003cp\u003eThe multivariate model was based on a previously constructed directed acyclic graph (DAG) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Crude and adjusted PRs were calculated for the associations between food environment (NEMS-S score), race/skin color, and racial discrimination using Poisson regression with robust variance, including the respective 95% CIs and Wald test for linear restrictions. The adjusted analysis controlled for the demographic and socioeconomic characteristics associated with food insecurity in the bivariate analysis and single-level theoretical model. Income and education were not considered confounding factors, as they were associated with the outcome and exposures and were part of the causal chain of associations. Therefore, they were not included in the adjusted model. Stratified analyses were performed to investigate the potential modifying effect of the food environment on the relationship between racial discrimination and food insecurity after a statistically significant interaction test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAll data were analyzed using Stata (StataCorp, College Station, TX, USA), version 18.0, and a p-value less than 5% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the sociodemographic, food insecurity, racial discrimination, and community food environment characteristics of the total sample and by household area. Most participants were women (75%), with a mean (SD) age of 47 (13.98) years, did not have a partner (62.8%), had 11 years of schooling (39.9%), and had a monthly family income of 3 to 5 minimum monthly salaries (48.4%); 37.8% of the sample self-identified as Black or Brown. When stratifying by household area, area 1 (lower socioeconomic status) had a higher proportion of Black and Brown residents, lower education, and lower income.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of sociodemographic characteristics, racial discrimination, food insecurity, and community food environment.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c12\" namest=\"c4\"\u003e \u003cp\u003eSocioeconomic status of household areas\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eArea 1 (n\u0026thinsp;=\u0026thinsp;108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eArea 3 (n\u0026thinsp;=\u0026thinsp;137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eArea 2 (n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eArea 4 (n\u0026thinsp;=\u0026thinsp;104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex n,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e26.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e70.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e73.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group (years) n,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u0026ndash;36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e36.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status n,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith a partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e42.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout a partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e76.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (years of schooling), n\u0026thinsp;=\u0026thinsp;395 n,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e45.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e40.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e43.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e39.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily income (MMS) n,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e57.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e26.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood insecurity n,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e35.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e41.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e64.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/skin color n,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e59.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e80.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e72.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEOD Mean, SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood environment NEMS-S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.424**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-7 to 39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-8 to 21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-12 to 39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(-1 to 38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(-1 to 43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMin-Max\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-17 to 62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e-16 to 43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e-16 to 54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e-17 to 58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e-15 to 62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eNEMS-S, Nutrition Environment Measures Survey in Stores; MMS: minimum monthly salary; EOD: Experiences of Discrimination scale.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e** P-value of the Kruskal\u0026ndash;Wallis H test for comparison scores between areas.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding self-perceived racial discrimination, the mean (SD) EOD score was 2.4 (5.43), with areas 1 and 3 showing the highest means of discrimination, with mean (SD) scores of 3.25 (6.20) and 2.94 (5.98), respectively. Food insecurity was present in more than half of the participants (51.1%) and was also more prevalent in areas 1 (56.5%) and 3 (58.8%). As measured by the NEMS-S tool, the mean (SD) score of the community food environment for the availability of healthy and unhealthy items in the food retailers was 17.6 (23.9), with lower scores being observed in lower income areas (area 1: 6.6 points, SD 19.6; area 3: 17.9 points, SD 25.7) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the associations between socioeconomic variables and food insecurity. Food insecurity was more prevalent in women (55.3%), in those aged 19 to 49 years (56.9%), in lower income people (77.2%), and in Brown people (57.5%), although without statistical significance. The prevalence of food insecurity was higher in areas with a community food environment of poorer quality (area 1: 56.5%; area 3: 58.8%). The mean racial discrimination score was higher in households that were food insecure (2.93 points, SD 5.91) than food secure (1.84 points, SD 4.82).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipants\u0026rsquo; sociodemographic and economic characteristics and associations with food insecurity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eFood Insecurity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal n,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex n,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group (years) n,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u0026ndash;36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (years of schooling), n\u0026thinsp;=\u0026thinsp;395 n,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily income (MMS) n,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/skin color n,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEOD Mean, SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood environment (NEMS-S) n,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea 1 (lowest score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea 4 (highest score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNEMS-S, Nutrition Environment Measures Survey in Stores; MMS: minimum monthly salary; EOD: Experiences of Discrimination scale.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, there was an association between experiences of racial discrimination and higher prevalence of food insecurity, even after adjusting for potential confounders (PR 1.07; 95% CI, 1.03\u0026ndash;1.20). Regarding household areas, participants residing in area 4 had a lower prevalence of food insecurity than those residing in area 1. When stratifying the analyses of the association between racial discrimination and food insecurity by the community food environment, the association remained statistically positive only for the households in areas with more unhealthy community food environment (PR 1.06; 95% CI, 1.01\u0026ndash;1.10), even after adjusting for potential confounders.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCrude and adjusted prevalence ratios (PRs) and their respective 95% CIs for the association between race/skin color and experiences of racial discrimination and the prevalence of food insecurity according to the community food environment.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eFood insecurity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude PR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted PR* (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRacial discrimination\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.00\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 (1.03\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/skin color\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhite\u003c/p\u003e \u003cp\u003eBlack\u003c/p\u003e \u003cp\u003eBrown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.82 (0.43\u0026ndash;1.56)\u003c/p\u003e \u003cp\u003e0.87 (0.46\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.08 (0.84\u0026ndash;1.38)\u003c/p\u003e \u003cp\u003e1.16 (0.93\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFood environment (NEMS-S)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eArea 1 (lowest score)\u003c/p\u003e \u003cp\u003eArea 3\u003c/p\u003e \u003cp\u003eArea 2\u003c/p\u003e \u003cp\u003eArea 4 (highest score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.04 (0.84\u0026ndash;1.29)\u003c/p\u003e \u003cp\u003e0.90 (0.65\u0026ndash;1.23)\u003c/p\u003e \u003cp\u003e0.63 (0.46\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.03 (0.83\u0026ndash;1.28)\u003c/p\u003e \u003cp\u003e0.95 (0.70\u0026ndash;1.29)\u003c/p\u003e \u003cp\u003e0.69 (0.50\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLower NEMS score areas (1\u0026thinsp;+\u0026thinsp;3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06 (1.01\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (1.01\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigher NEMS score areas (2\u0026thinsp;+\u0026thinsp;4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.99\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 (0.98\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNEMS-S, Nutrition Environment Measures Survey in Stores, EOD: Experiences of Discrimination scale.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Adjusted for sex and age; Wald test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur results indicate that racism is positively associated with a less healthy community food environment. Experiences of racial discrimination, which encompass the expression of interpersonal and institutional racism, were positively associated with food insecurity and, when evaluating this association stratified by household area, we observed a positive association only for households in areas with a poorer food environment (this association remained statistically significant even after adjusting for potential confounders). This result demonstrates that the food environment can be a modifier of the effect of the association. Based on our literature review, this is the first study to evaluate how the food environment can modify the effect of the association between racism and food insecurity.\u003c/p\u003e \u003cp\u003ePrevious studies have demonstrated a consistent association between a greater presence of Black people in the neighborhood and a poorer community food environment,\u003csup\u003e(13, 14, 17, 18, 19, 20, 21, 22)\u003c/sup\u003e in line with the results obtained in the present study. A scoping review conducted in 2023, including only studies carried out in the United States, concluded that 30% of the included studies associated one or more indicator of structural racism, such as gentrification and racial residential segregation, with geographic access to food retailers, while 70% of studies documented disparities in access to food retailers according to the neighborhood racial/ethnic composition.\u003csup\u003e(34)\u003c/sup\u003e Likewise, in Brazil, studies based on secondary data conducted in the Southeast,\u003csup\u003e(35)\u003c/sup\u003e Northeast,\u003csup\u003e(21)\u003c/sup\u003e and South\u003csup\u003e(13)\u003c/sup\u003e regions documented that a greater presence of Black people in the neighborhood was associated with the presence of food deserts, that is, regions characterized by limited availability and access to healthy and fresh food. In Porto Alegre (South region), areas with a higher percentage of Black and Indigenous people are twice as likely to be classified as a food desert.\u003csup\u003e(13)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe literature has characterized the poor availability of healthy food options in areas with a greater proportion of Black residents as a manifestation of structural racism.\u003csup\u003e(13, 23)\u003c/sup\u003e By using race/skin color as a variable to describe the household areas with a greater or lesser percentage of Black residents, we were able to identify an important part of the action of structural racism, which occurs through spatial segregation by providing residents with poor access to food products, services, and options in different areas or keeping access narrowed to ecologically degraded areas. In this study, structural racism operated through Black people\u0026rsquo;s difficulty in having access to healthy foods, resulting in a higher prevalence of food insecurity.\u003csup\u003e(9)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur findings regarding the association between racial discrimination and food insecurity corroborate those of international studies. In 2019, Phojanakong et al. performed a cross-sectional study in the United States showing that household food insecurity in mothers who reported experiences of discrimination was twice that in mothers without such experiences.\u003csup\u003e(36)\u003c/sup\u003e Our study demonstrated that each 1-point increase in the discrimination score was associated with a 7% increase in the likelihood of food insecurity. In line with this finding, Burke et al., in a cross-sectional study of Black people living in South Carolina, United States, found that each 1-point increase in the self-perceived racial discrimination scale was associated with a 5% increase in the odds of being food insecure.\u003csup\u003e(37)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn Brazil, to our knowledge, there are no studies relating measures of experiences of racial discrimination to food insecurity. However, nationally representative data have previously demonstrated a higher prevalence of food insecurity among Black individuals, which indicates, as stated earlier, its relationship with structural racism.\u003csup\u003e(38, 39, 40, 41, 42, 43)\u003c/sup\u003e In our study, although prevalence rates were also higher among Brown and Black people, the result was not statistically significant. Black people in Brazil have lower wages, about 30% lower than those of White people, occupy only 29.5% of management positions, have higher poverty rates, reaching 34.5% for Black people and 38.4% for Brown people, and experience more physical, psychological, and sexual violence.\u003csup\u003e(36, 44)\u003c/sup\u003e The whole scenario, consisting of different indicators of institutional and structural racism, increases the vulnerability of the Black population to food insecurity in a country already widely affected by it.\u003csup\u003e(9)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eConsidering the multiple causes of food insecurity, the community food environment was shown to be an important factor in the relationship with racism, since the association between food insecurity and racial discrimination was only present among residents of areas with a poorer food environment. Physical access to food retailers that primarily sell healthy and culturally appropriate foods is essential to ensure food security.\u003csup\u003e(45)\u003c/sup\u003e However, people experiencing racism may have difficulty accessing these food stores, a factor that contributes to increased food insecurity. Racial residential segregation in the city of Porto Alegre, a product of institutional and structural racism, has hampered urban mobility, access to retailers and services, and the availability of better employment and housing opportunities for Black people for decades.\u003csup\u003e(46)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThere are 2 main ways of measuring the consequences of racism in the health field, both of which were used in this study to analyze food insecurity. The first is through the association between race/skin color and the respective study outcome, considered a measure of structural racism, as it is not possible to identify the specific mechanisms and interventions for the outcome. These studies define race as a proxy for racism, identifying specific groups or populations that experience certain forms of oppression and discrimination and, as a result, also experience health inequalities. The second is based on psychometric questionnaires that measure experiences of discrimination, considered a more direct way of assessing racial discrimination in its multiple expressions and dimensions (interpersonal, internalized, or institutional racism).\u003csup\u003e(27, 47, 48)\u003c/sup\u003e Direct assessment of discrimination using psychometric instruments is a growing trend in research. Their use is supported by the need to understand the stressful experiences related to discrimination and their association with negative health outcomes. This allows for the investigation and construction of more detailed mechanisms on how discrimination operates in the study population.\u003csup\u003e(47)\u003c/sup\u003e In the present study, only the variable \u0026lsquo;experiences of racial discrimination\u0026rsquo; was associated with food insecurity, unlike the variable \u0026lsquo;race/skin color\u0026rsquo;. This finding suggests that instruments that measure racial discrimination should be incorporated into future studies investigating the health of the Black population.\u003csup\u003e(49)\u003c/sup\u003e Conversely, it also suggests that other instruments and indicators are needed to understand the role of structural racism.\u003c/p\u003e \u003cp\u003eFinally, the findings revealed a high prevalence of food insecurity across the entire study population. The results are in line with reports in Brazil and worldwide. In Brazil, food insecurity increased from 20.6% in 2017 to 32.8% in 2022, while globally its prevalence increased from 21.7% in 2015 to 29.6% in 2022.\u003csup\u003e(50)\u003c/sup\u003e This demonstrates the growing importance of monitoring and understanding the factors associated with food insecurity to implement public food and nutrition policies focused on the most vulnerable populations.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and strengths of the study\u003c/h2\u003e \u003cp\u003eThis study has limitations and strengths. Strengths include (1) the use of 2 variables to measure and analyze the effects of the multiple dimensions of racism (interpersonal, institutional, and structural) on the study outcome, (2) the use of validated questionnaires for the study population to measure the outcome and exposures, and (3) the assessment of the community food environment through an audit of all food retailers, with this measure being more robust than assessment using secondary data, which only evaluates the presence of retailers. Limitations include (1) the enrollment of a study population that is not representative of the general population, but the explanatory mechanisms of associations may be used to help understand similar contexts in low- and middle-income countries, (2) the use of the variable \u0026lsquo;race/skin color\u0026rsquo; as a proxy for structural racism, which hinders the development of detailed mechanisms about its effects and the most appropriate interventions, and (3) the use of the short version of the EBIA, which did not allow us to classify individuals according to different levels of food insecurity.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study aimed to analyze the relationship between racism and food insecurity. We found that racism, as an experience of racial discrimination at the interpersonal and institutional levels, was associated with a higher prevalence of food insecurity in the study population. We also found that the food environment, in areas with a greater presence of Black people, was an effect modifier of this relationship, which indicates the action of structural racism. The results highlight the need for public food security policies focused on areas with a greater presence of Black people as a way to reduce racial inequalities in health and to promote access to adequate and healthy food for all.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDAG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edirected acyclic graph\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEBIA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBrazilian Food Insecurity Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEOD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExperiences of Discrimination scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNEMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eS\u0026ndash;Nutrition Environment Measures Survey in Stores\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprimary health care\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprevalence ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study is part of a larger research project titled “Study of the social and environmental determinants of food and nutrition: an ecosocial approach,” which was approved by the Research Ethics Committee of Universidade Federal do Rio Grande do Sul (UFRGS), under number CAAE 46934015.3.0000.5347. Each participant provided written informed consent prior to inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study received financial support from the Brazilian National Council for Scientific and Technological Development (CNPq, Grant number. 420342/2018-4) and Foundation for Research Support of the State of Rio Grande do Sul - FAPERGS. Grant number: 21/2551-0000520-3. The funders had no role in study design, data collection and analysis, decision to publish and the preparation or approval of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors' contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eI.S., P.B.Z.R. and R.C. conceived the objective and processed data of this study; E.B.V., P.B.Z.R., R.C. and I.S. collected data. E.B.V, R.C. and M.S.D. analyzed data and drafted the manuscript; E.B.V., R.C., M.S.D. and M.F. interpreted the findings and reviewed the manuscript for important intellectual content. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u0026zwnj; 1. Food and Agriculture Organization of the United Nations. Rome Declaration and Plan of Action on World Food Security. FAO. 1996. https://www.fao.org/3/w3613e/w3613e00.htm. 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Preventive Medicine. 2010; doi: 10.1016/j.ypmed.2010.04.009.\u003c/li\u003e\n\u003cli\u003eGripper AB, Nethery R, Cowger TL, White M, Kawachi I, Adamkiewicz G. Community solutions to food apartheid: A spatial analysis of community food-growing spaces and neighborhood demographics in Philadelphia. Social Science \u0026amp; Medicine. 2022; doi: 10.1016/j.socscimed.2022.115221.\u003c/li\u003e\n\u003cli\u003eAgarwal S, Fertig AR, Trofholz AC, Tate AD, Robinson J, Berge JM. Exploring the associations between neighbourhood food environment, household food insecurity and child weight-related outcomes in socio-economically and racially/ethnically diverse families. Public Health Nutrition. 2022; doi:10.1017/S1368980022002130.\u003c/li\u003e\n\u003cli\u003eRosa PBZ, Schuch I, Cunha CML, Rodrigues YE, Fanton M, Canuto R. Social environment perception and associations with overweight in the city of Porto Alegre, Brazil. J Biosoc Sci. 2022; doi: 10.1017/S0021932021000419.\u003c/li\u003e\n\u003cli\u003edos Santos LP, Lindemann IL, Motta JVdS, Mintem G, Bender E, Gigante DP. Proposal of a short-form version of the Brazilian Food Insecurity Scale. Rev Sa\u0026uacute;de P\u0026uacute;blica. 2014; doi: 10.1590/S0034-8910.2014048005195.\u003c/li\u003e\n\u003cli\u003eLett E, Asabor E, Beltr\u0026aacute;n S, Cannon AM, Arah OA. Conceptualizing, Contextualizing, and Operationalizing Race in Quantitative Health Sciences Research. Ann Fam Med. 2022; doi: 10.1370/afm.2792.\u003c/li\u003e\n\u003cli\u003eKrieger N, Smith K, Naishadham D, Hartman C, Barbeau EM. Experiences of discrimination: validity and reliability of a self-report measure for population health research on racism and health. Soc Sci Med. 2005; doi: 10.1016/j.socscimed.2005.03.006.\u003c/li\u003e\n\u003cli\u003eFattore GL, Teles CA, Santos DN dos, Santos LM, Reichenheim ME, Barreto ML. Validade de constructo da escala Experiences of Discrimination em uma popula\u0026ccedil;\u0026atilde;o brasileira. Cad Sa\u0026uacute;de P\u0026uacute;blica. 2016; doi: 10.1590/0102-311X00102415.\u003c/li\u003e\n\u003cli\u003eGlanz K, Sallis JF, Saelens BE, Frank LD. Healthy Nutrition Environments: Concepts and Measures. American Journal of Health Promotion. 2005; doi: 10.4278/0890-1171-19.5.330\u003c/li\u003e\n\u003cli\u003eMartins PA, Cremm EC, Leite FH, Maron LR, Scagliusi FB, Oliveira MA. Validation of an adapted version of the nutrition environment measurement tool for stores (NEMS-S) in an urban area of Brazil. J Nutr Educ Behav. 2013; doi: 10.1016/j.jneb.2013.02.010.\u003c/li\u003e\n\u003cli\u003ePerozzo G, Olinto MTA, Dias-da-Costa JS, Henn RL, Sarriera J, Pattussi MP. Associa\u0026ccedil;\u0026atilde;o dos padr\u0026otilde;es alimentares com obesidade geral e abdominal em mulheres residentes no Sul do Brasil. Cad Sa\u0026uacute;de P\u0026uacute;blica. 2008; doi: 10.1590/S0102-311X2008001000023.\u003c/li\u003e\n\u003cli\u003eHoffmann JF, Nunes MA, Schmidt MI, Olinto MT, Melere C, Ozcariz SG, Buss C, Drhemer M, Manzolli P, Soares RM, Pinheiro AP, Camey S. Dietary patterns during pregnancy and the association with sociodemographic characteristics among women attending general practices in southern Brazil: the ECCAGe Study. Cad Saude Publica. 2013; doi: 10.1590/S0102-311X2013000500014.\u003c/li\u003e\n\u003cli\u003eSingleton CR, Wright LA, McDonald M, Archer IG, Bell CN, McLoughlin GM, Houghtaling B, Cooksey Stowers K, Anderson Steeves E. Structural racism and geographic access to food retailers in the United States: A scoping review. Health Place. 2023; doi: 10.1016/j.healthplace.2023.103089.\u003c/li\u003e\n\u003cli\u003eHon\u0026oacute;rio OS, Pessoa MC, Grat\u0026atilde;o LHA, Rocha LL, de Castro IRR, Canella DS, et al. 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Instituto Brasileiro de Geografia e Estat\u0026iacute;stica. Desigualdades sociais por cor ou ra\u0026ccedil;a no Brasil. IBGE. 2022. https://educa.ibge.gov.br/jovens/conheca-o-brasil/populacao/18319-cor-ou-raca.html. Accessed 21 Jan 2024.\u003c/li\u003e\n\u003cli\u003eKarpyn AE, Riser D, Tracy T, Wang R, Shen YE. The changing landscape of food deserts. UNSCN Nutr. 2019;44:46-53.\u003c/li\u003e\n\u003cli\u003ePolidoro M, Canavese D. Porto Alegre\u0026rsquo;s sociopolitical urbanism and neoliberal economic dynamics: Perspectives from a local community in the Afro-Brazilian periphery. Local Economy: The Journal of the Local Economy Policy Unit. 2017; doi: 10.1177/0269094217735477.\u003c/li\u003e\n\u003cli\u003eBastos JL, Celeste RK, Faerstein E, Barros AJD. Racial discrimination and health: A systematic review of scales with a focus on their psychometric properties. Social Science \u0026amp; Medicine. 2010; doi: 10.1016/j.socscimed.2009.12.020.\u003c/li\u003e\n\u003cli\u003eCuevas AG, Boen C. Tip of the iceberg: measuring racial discrimination in studies of health. Stress And Health. 2021; doi: 10.1002/smi.3047.\u003c/li\u003e\n\u003cli\u003eRodrigues YE, Fanton M, Novossat RS, Canuto R. Perceived racial discrimination and eating habits: a systematic review and conceptual models. Nutr Rev. 2022; doi: 10.1093/nutrit/nuac001.\u003c/li\u003e\n\u003cli\u003eFood and Agriculture Organization. FAO Hunger Map [Internet]. 2023. https://www.fao.org/interactive/state-of-food-security-nutrition/2-1-1/en/. Accessed 05 Jan 2024.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-for-equity-in-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijeh","sideBox":"Learn more about [International Journal for Equity in Health](http://equityhealthj.biomedcentral.com)","snPcode":"12939","submissionUrl":"https://submission.nature.com/new-submission/12939/3","title":"International Journal for Equity in Health","twitterHandle":"@equityhealthj","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Racism, Food Insecurity, Social Determinants of Health, Food Supply, Food Deserts","lastPublishedDoi":"10.21203/rs.3.rs-4674282/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4674282/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWe analyzed the relationship between racism, community food environment, and food insecurity in adults of different socioeconomic status.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis was a cross-sectional study of 400 adults aged 20\u0026ndash;70 years residing in the central area of ​​Porto Alegre, the capital of Rio Grande do Sul state. Racism was measured by self-reported race/skin color and by using the Experiences of Discrimination scale (EOD). Food environment was assessed using the Nutrition Environment Measures Survey in Stores (NEMS-S) tool. Food insecurity was assessed using the short version of the Brazilian Food Insecurity Scale (EBIA for short, in Portuguese). Poisson regression with robust variance was employed for the multivariate analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of food insecurity was higher in areas with a poorer food environment (areas 1 and 3; 56.6% and 58.8%, respectively). Racial discrimination was associated with food insecurity, where every 1-point increase in the discrimination score increased the likelihood of food insecurity by 7% (prevalence ratio [PR] 1.07; 95% CI, 1.03\u0026ndash;1.20). When stratifying the analyses by food environment, racial discrimination was associated with food insecurity only in areas with a poorer food environment (PR 1.06; 95% CI, 1.01\u0026ndash;1.10).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eExperiences of racial discrimination were associated with a higher prevalence of food insecurity in the study population. The community food environment was an effect modifier of this relationship, highlighting the relevance of interventions in the food environment focused on areas with a greater presence of Black people as a way of combating racism and food insecurity.\u003c/p\u003e","manuscriptTitle":"The community food environment as an effect modifier of the relationship between racism and food insecurity among adults in Southern Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-29 10:12:54","doi":"10.21203/rs.3.rs-4674282/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-30T06:46:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-29T17:37:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-29T10:04:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127322963118126059855900174420749695715","date":"2024-08-20T07:05:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50112189393598051969728383843592103015","date":"2024-08-19T21:16:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-19T13:39:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-09T15:38:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-04T07:36:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal for Equity in Health","date":"2024-07-02T12:36:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-for-equity-in-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijeh","sideBox":"Learn more about [International Journal for Equity in Health](http://equityhealthj.biomedcentral.com)","snPcode":"12939","submissionUrl":"https://submission.nature.com/new-submission/12939/3","title":"International Journal for Equity in Health","twitterHandle":"@equityhealthj","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5e033421-1646-4f83-87c5-60fd927560d9","owner":[],"postedDate":"July 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-04T16:26:39+00:00","versionOfRecord":{"articleIdentity":"rs-4674282","link":"https://doi.org/10.1186/s12939-024-02311-3","journal":{"identity":"international-journal-for-equity-in-health","isVorOnly":false,"title":"International Journal for Equity in Health"},"publishedOn":"2024-10-31 16:20:17","publishedOnDateReadable":"October 31st, 2024"},"versionCreatedAt":"2024-07-29 10:12:54","video":"","vorDoi":"10.1186/s12939-024-02311-3","vorDoiUrl":"https://doi.org/10.1186/s12939-024-02311-3","workflowStages":[]},"version":"v1","identity":"rs-4674282","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4674282","identity":"rs-4674282","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}