The paradox of privilege: intersectional inequalities in ultra-processed food consumption in Brazil

The paradox of privilege: intersectional inequalities in ultra-processed food consumption in 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 paradox of privilege: intersectional inequalities in ultra-processed food consumption in Brazil Rosália Garcia Neves, Karla Machado, Mirelle Oliveira Saes, Niely Galeão da Rosa Moraes, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9441587/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Objective To analyze the prevalence of ultra-processed food consumption and to investigate its association with sociodemographic characteristics considered from an intersectional perspective among adults living in Brazilian state capitals. Methods This was a cross-sectional study using data from the 2023 Surveillance System of Risk and Protective Factors for Chronic Diseases, conducted with adults aged 18 years or older. The outcome was defined as the consumption of at least five groups of ultra-processed foods on the day prior to the interview. The exposure variables were sex, skin color, and education, and the intersectionality of these characteristics was constructed using the Jeopardy Index. Results The sample comprised 21,690 individuals. The prevalence of consumption of five or more groups of ultra-processed foods was 17.7% (95% CI 16.6–18.9). Black, brown, yellow, and Indigenous women with low educational levels showed a 42% lower prevalence of consumption of these foods compared to white men with higher education. Conclusion Approximately one-fifth of the population consumed at least five groups of ultra-processed foods on the previous day, with the most privileged group (men, white individuals, and those with higher education) showing the highest prevalence of consumption. Public policies aimed at reducing the consumption of ultra-processed foods are needed, taking into account the intersectionality of social markers, since the occurrence of consumption differs across groups. Eating Epidemiologic Studies Food Processed Health Inequities Intersectional Framework Figures Figure 1 Figure 2 Figure 3 Introduction The consumption of ultra-processed foods (UPFs) has risen notably in recent decades, making it one of the major current public health issues ( 1 , 2 ). These foods are characterized by industrial formulations that include refined ingredients, additives, and low amounts of fresh foods, often with poor nutritional profiles that feature high levels of free sugars, saturated fats, sodium, and energy density, along with low fiber and micronutrients ( 1 ). Scientific evidence consistently links the intake of ultra-processed foods with several negative health outcomes, such as cardiovascular diseases, type 2 diabetes, obesity, certain cancers, and higher all-cause mortality ( 2 – 9 ). In Brazil, the increase in ultra-processed food consumption occurs alongside the nutritional transition and changes in food systems, characterized by shifts in food production, distribution, and consumption patterns ( 4 , 10 ). Data from national surveys, such as the Household Budget Survey (POF 2017–2018), show that ultra-processed foods already make up a significant part of the Brazilian diet, accounting for about 20% of the calories consumed, with a 1.02 percentage point rise compared to 2008–2009. This increase was more notable among men, black people, rural residents, individuals with less education, and those in the lowest income quintile, while a decline in intake was seen among people with higher education and income levels ( 10 , 11 ). These results emphasize the importance of targeting ultra-processed foods in public policies aimed at promoting adequate and healthy eating, particularly for populations facing greater social vulnerability ( 8 ). However, the distribution of food consumption in the population is not uniform. Sociodemographic characteristics such as gender, race/skin color, and education play a key role in shaping eating habits, reflecting historical, economic, and cultural processes that create social inequalities in health( 10 , 12 ). Previous research has found significant differences in the consumption of ultra-processed foods based on these social markers, indicating that dietary patterns are closely linked to individuals' social conditions and life contexts ( 10 – 13 ). Traditionally, these characteristics have been examined separately. However, this method can oversimplify complex social phenomena by ignoring that different social markers do not act independently; instead, they are interconnected and overlap in creating inequalities, often intensifying their effects. In this way, the concept of intersectionality has become increasingly common in social epidemiology to understand how various dimensions of social identity, such as gender, race/skin color, and socioeconomic status, interact and result in specific patterns of exposure and vulnerability related to health. Despite the growth of research on the health effects of ultra-processed foods, few nationwide studies examine their consumption from an intersectional perspective, especially using recent and comprehensive population data. Incorporating this approach can enhance understanding of inequalities in food consumption, helping to identify population groups with different patterns of exposure to ultra-processed foods. In this context, the present study aimed to analyze the prevalence of ultra-processed food consumption and examine its association with sociodemographic characteristics considered through an intersectional lens in adults living in Brazilian capitals, based on data from VIGITEL 2023. Methods Cross-sectional study using data from the VIGITEL survey—Surveillance System of Risk and Protective Factors for Chronic Diseases by Telephone Survey—covering data from 2023. VIGITEL is an annual survey conducted by the Brazilian Ministry of Health through telephone interviews with individuals aged 18 or older living in the 26 state capitals and the Federal District ( 14 ). In 2023, for logistical reasons, a minimum of 800 interviews was established in each location. Due to the significant deterioration of landline telephone services in the country, half of the interviews were conducted via mobile phone. This approach ensured adequate representativeness and precision of estimates, with a final sample of 400 landline and 400 mobile phone interviews in each location, aiming for a 95% confidence level and a maximum margin of error of approximately 4 percentage points for any risk factor measured in the study. More details on the sampling process and methods used in this edition of VIGITEL are available in the official publications ( 14 ). Ethical approval was obtained from the National Committee for Ethics in Research with Human Beings of the Ministry of Health (65610017.1.0000.0008). VIGITEL databases are publicly available (< http://svs.aids.gov.br/download/Vigitel/%3E ). In this study, the outcome was the consumption of ultra-processed foods, assessed through a self-reported questionnaire that included 13 subgroups ingested the day before the interview: "Now I am going to list some foods and I would like you to tell me if you ate any of them YESTERDAY (from when you woke up to when you went to sleep)." The foods included in the questionnaire were soft drinks; fruit juice in boxes, cartons, or cans; powdered soft drinks; chocolate drinks; flavored yogurts; packaged snacks (or chips) or crackers; cookies/sweet crackers, stuffed cookies, or packaged cookies; chocolate, ice cream, gelatin, flan, or other industrialized desserts; sausage, processed meats (e.g., sausage, mortadella, or ham); sliced bread; hot dogs or hamburgers; mayonnaise, ketchup, or mustard; margarine; instant noodles, packaged soup, frozen lasagna, or other ready-to-eat dishes bought from frozen. The answer options were "yes" and "no." Based on positive responses to the consumption of these foods, ranging from 0 to 13, a cutoff point was established at 5 or more subgroups ( 15 , 16 ). The self-reported covariates used were sex (female/male), skin color (Caucasian, Black, Brown, Yellow, and Indigenous), and schooling in full years classified as low, middle, and high education (0–8, 9–11, and 12 or more). These variables were used to construct a measure of social inequality based on the "multiple risk" principle, known as the Jeopardy index. This approach guides research on the theory of intersectionality and social phenomena. The multiple risk framework shows that various aspects of an individual's identity, such as sex, class, or race, which lead to discrimination or oppression, are interdependent and exert a compound or cumulative effect ( 17 ). The intersectionality of these sociodemographic characteristics was categorized into five levels (0 to 4) and created by combining the categories of the variables mentioned: variables with two categories were coded as "0 and 1," and those with three categories as "0, 1, and 2." Within the framework of intersectionality, a score of 0 indicates the lowest inequality (male, Caucasian, and with a high level of education), while a score of 4 indicates the highest degree of inequality (female, Black/Brown/Yellow people, and Indigenous people with a low level of education). Data analysis was conducted using Stata 15.0 statistical software. Prevalence rates and their 95% confidence intervals (95% CIs) for the outcomes were calculated based on exposures using the chi-square test. Poisson regression was used to estimate prevalence ratios (PRs) and 95% CIs. For all analyses, the survey (svy) command was employed for weighting. Intersectionality analyses of sociodemographic characteristics, both in the total sample and stratified by sex, were performed using the Jeopardy index, which considers the score (the higher the score, the greater the risk), along with creating exposure variables that examine the intersection of social markers such as sex and skin color, using schooling as a stratifying variable. Results The sample included 21,690 adults, with 54.0% identifying as female, 60.2% as Black, Brown, Yellow, or Indigenous, and 41.3% having 9 to 11 years of education. Regarding intersectional sociodemographic traits, 8.2% were White men with higher education, and 8.7% were Black, Brown, Yellow, or Indigenous women with lower education. Table 1 shows the prevalence of ultra-processed food consumption. The most common items were margarine (43.4%), sliced bread/hot dog buns/hamburger buns (36.5%), chocolate/ice cream/gelatin/flan (29.0%), and soft drinks (28.2%). Less frequently consumed foods included powdered soft drinks (10.5%), chocolate drinks (10.2%), and instant noodles/packaged soup/lasagna (6.1%). The percentage of individuals who ate five or more of the evaluated ultra-processed foods the day before the interview was 17.7% (95%CI 16.6;18.9). Table 1 Prevalence and 95% confidence interval of ultra-processed food consumption on the day before the interview. Brazil, VIGITEL, 2023 (N = 21,690). Ultra-processed foods Prevalence (CI95%) Margarine 43.4 (41.9–44.9) Sliced ​​bread, hot dog buns, or hamburger buns 36.5 (35.0-37.9) Chocolate, ice cream, gelatin, flan, or other commercially produced desserts 29.0 (27.6–30.4) Soft drink 28.2 (26.8–29.7) Sausage, chorizo, mortadella, or ham 26.6 (25.3–27.9) Packaged snack or savory biscuit 25.0 (23.7–26.3) Sweet biscuit, filled biscuit, or packaged cake 21.5 (20.3–22.8) Mayonnaise, ketchup, or mustard 18.4 (17.2–19.6) Flavored yogurt 14.2 (13.1–15.2) Fruit juice in a carton or can 13.3 (12.3–14.4) Powdered soft drink 10.5 (9.6–11.5) Chocolate drink 10.2 (9.2–11.3) Instant noodles, packaged soup, lasagna, or other ready-made meals purchased frozen 6.1 (5.4–6.8) Consumption of five or more ultra-processed foods 17.7 (16.6;18.9) Values ​​with svy weighting When evaluating ultra-processed food consumption based on intersectional sociodemographic factors, it is seen that the most privileged group (men, whites, and those with higher education) consumes a larger share of these foods compared to the most vulnerable group (women, Black/Brown/Yellow/Indigenous people with lower education), except for margarine, packaged snacks or savory biscuits, and powdered soft drinks (Fig. 1 ). Regarding the consumption of five or more ultra-processed foods on the previous day, the adjusted analysis shows that women had a 36% lower prevalence of ultra-processed food consumption compared to men. Additionally, people with 9 to 11 years of schooling showed a 25% higher prevalence of consumption compared to those with more schooling (12 or more years). According to the Jeopardy index, black, brown, yellow, or indigenous women with low schooling have a prevalence of consuming at least five of these foods that is 42% lower compared to white men with high schooling (Table 2 ). Table 2 Prevalence and 95% confidence interval of consuming five or more ultra-processed foods on the day before the interview, according to sociodemographic characteristics and Jeopardy index. Brazil. VIGITEL, 2023 (N = 21,690). Variable Prevalence ratio (CI95%) Prevalence ratio (CI95%) Sex Male 22.0 (20.1;24.1) 1.00 Female 14.1 (12.9;15.4) 0.64 (0.56;0.72) Skin color Caucasian 17.1 (15.2;19.3) 1.00 Black/Brown/Yellow/Indigenous 18.4 (17.0;19.9) 1.05 (0.91;1.20) Education (years) 0–8 13.6 (11.7;15.8) 0.81 (0.67;0.99) 9–11 21.1 (19.3;23.1) 1.25 (1.08;1.45) 12 or more 16.7 (14.8;18.8) 1.00 Jeopardy index 0 (less risk) 22.9 (17.7;28.9) 1.00 1 18.8 (16.3;21.6) 0.82 (0.62;1.09) 2 18.7 (16.9;20.8) 0.82 (0.63;1.07) 3 16.1 (14.2;18.3) 0.70 (0.53;0.93) 4 (more risk) 13.2 (10.3;16.8) 0.58 (0.41;0.82) Model: sex, skin color, and education level Additionally, when analyzing the occurrence of consuming five or more ultra-processed foods based on sex and skin color in an intersectional way across different education levels, a higher prevalence was observed in men with medium education (9 to 11 years), with 26.7% in black/brown/yellow/indigenous men and 27.3% in whites. Overall, those with less education showed lower consumption frequencies, which was also seen in women with higher education (Fig. 2 ). Figure 3 shows the prevalence of consuming five or more ultra-processed foods according to the Jeopardy index (which considers skin color and education), broken down by sex. It was observed that the relationship between consumption and the Jeopardy index differs by gender, with a stronger association seen among men. Discussion This study examined ultra-processed food consumption among adults in Brazilian state capitals, considering sociodemographic factors through an intersectional lens. It was found that about two in ten individuals ate five or more ultra-processed foods the day before the interview; ultra-processed food consumption does not seem to be strongly influenced by social factors in Brazil. Our findings show that the most privileged group (men, whites, and those with higher education) had higher intake of ultra-processed foods compared to the most vulnerable group (women, Black/Brown/Yellow/Indigenous people with low education). Although the prevalence of ultra-processed food consumption observed in this study may seem moderate, it indicates frequent consumption of low-nutritional-value foods, which tends to accumulate over time and has significant implications for the risk of chronic diseases ( 2 ). Women had a 36% lower prevalence of ultra-processed food consumption compared to men. This shows that the consumption of ultra-processed foods varies by sex and race, supporting previous findings indicating higher consumption among men, especially black/brown men ( 18 ). A possible explanation for this result relates to gender differences in eating habits and household chores. Women are traditionally more encouraged to develop cooking skills and often assume greater responsibility for meal preparation at home ( 19 ). Furthermore, studies show that women tend to consume more natural or minimally processed foods, such as fruits and vegetables, compared to men ( 12 , 20 ). In this regard, culinary knowledge and cooking at home have been linked to higher diet quality ( 21 ). The lower prevalence of high ultra-processed food consumption among women from minority racial groups and with less education might initially be seen as a healthier dietary pattern. However, this finding should be approached with caution. Evidence from national studies suggests that lower consumption of ultra-processed foods may mainly result from economic constraints, lower purchasing power, and limited access to these products, rather than a greater commitment to a healthy diet. In such contexts, reduced consumption of ultra-processed foods can occur alongside other forms of food inequality, such as food insecurity, dietary monotony, and low food diversity, emphasizing the importance of considering the broader social environment in interpretations. On the other hand, the lower prevalence of high consumption among women with higher education may be linked to greater access to health and nutrition information, a greater appreciation of healthy eating practices, and a better ability to incorporate nutritional recommendations into daily life. However, it is important to emphasize that such eating practices are connected to more favorable social conditions and greater choices, which facilitate the adoption of behaviors aligned with healthy eating guidelines ( 1 , 13 ). The lack of social factors influencing the consumption of ultra-processed foods identified in this study may be explained by several factors. This finding can be attributed to several factors ( 10 , 22 – 25 ). The first reason may be the high availability and easier access to ultra-processed foods across the country, leading to a recent increase in consumption among all population groups ( 10 , 11 ), including men, rural residents ( 25 ), and the most vulnerable groups identified in this study ( 24 ). The results observed in this study also reinforce the idea that the consumption of these foods may be becoming a habit in Brazilian society and that this dietary pattern is associated with food addiction ( 10 , 11 , 25 ). However, it is also important to note that these findings could be linked to other factors, such as the possible lack of access to these foods among people in vulnerable social conditions ( 22 ), since analyses indicate that populations living in more disadvantaged neighborhoods have greater access to both healthy and unhealthy food outlets ( 26 ). Additionally, cultural factors may play a role, as many families still value preparing foods at home, such as rice and beans, which are culturally central to the Brazilian diet ( 27 ). It is also known that most of the Brazilian population regularly consumes fruits and vegetables and follows recommendations to limit red meat intake, without regularly replacing meals with snacks ( 28 ). Conversely, families in more socially vulnerable contexts often maintain higher consumption of basic foods and traditional preparations, such as rice and beans, which continue to form the foundation of the Brazilian diet ( 27 ). This dietary pattern may partly explain the lower prevalence of high ultra-processed food consumption observed among certain population groups. Along with the known health risks of ultra-processed foods demonstrated in the literature ( 29 , 30 , 31 ), increased consumption of these products is also linked to environmental impacts. The production of ultra-processed foods significantly contributes to greenhouse gas emissions, which in turn lead to deforestation, soil degradation, and biodiversity loss ( 32 ). Therefore, policies should be tailored to populations that consume the most, considering health, lifestyle, and sociodemographic characteristics. In this context, implementing public policies to address the increasing consumption of ultra-processed foods becomes essential. In Brazil, initiatives such as the Food Guide for the Brazilian Population represent significant progress by making the degree of food processing a key factor in guiding food choices ( 33 ). However, further efforts are necessary, especially in regulating the food industry and the food environment. Successful public health strategies, like tobacco control policies, demonstrate that regulatory measures targeting the industry can substantially reduce risk behaviors at the population level ( 34 ). Reducing the influence of the ultra-processed food industry would involve disrupting their business models, reallocating resources to other types of food producers, and establishing strong safeguards against conflicts of interest in policymaking, research, and professional practice. Additionally, it requires framing ultra-processed foods as a critical global health issue, building legal, research, and communication capacities, and promoting a fairer, more encouraging transition to diets low in ultra-processed foods ( 35 ). The present study has some limitations that should be considered when interpreting the results. First, data were collected only in Brazil's capitals, which may not reflect conditions in smaller municipalities or rural areas across the country. Additionally, food consumption was assessed through recall of the day before the interview, which may not reflect individuals' habitual eating patterns, especially given potential variations in food intake between weekdays and weekends, times when people tend to eat different foods. However, this method is widely used in population surveys and allows for the estimation of food consumption in large samples with relative ease ( 36 – 38 ). Furthermore, the information collected is self-reported, which may be subject to bias. Finally, categorizing the skin color variable grouped different racial groups into the same category for analysis, which may mask important differences between these groups. Nonetheless, we had to operationalize it this way for statistical purposes. Even with these limitations, the study has notable strengths. Using data from a nationwide population survey, with a large sample size and standardized data collection methods, improves the reliability of the estimates. Additionally, analyzing UPFs consumption through social markers in an intersectional way advances beyond traditional methods that examine social markers separately, enabling a more comprehensive understanding of inequalities in food consumption among Brazilian adults. Conclusion In this study, two out of ten people consumed five or more ultra-processed foods the day before the interview. Consumption was higher among men, white individuals, and those with higher education, highlighting significant inequalities. Given the widespread availability of these foods in the food environment, the study's findings reinforce the need for regulatory and intersectoral policies to develop industry-level strategies targeting adults, similar to those implemented for tobacco control. Declarations Ethics approval and consent to participate The data is public and was used for research purposes, respecting ethical principles and the confidentiality of information provided by respondents. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding Not applicable. Author Contribution R.G.N. performed the statistical analyses. R.G.N, K.M., N.G.R.M. and T.R.F. wrote all sections of the article and worked on the literature review. M.O.S and J.K.P. critically reviewed the entire manuscript. All authors approved this version of the manuscript. Acknowledgments ​All people who participated in the survey and CAPES (Coordination for the Improvement of Higher Education Personnel). Data Availability The datasets analyzed during the current study are publicly available from the Brazilian Ministry of Health. Data from the VIGITEL (Surveillance System of Risk and Protective Factors for Chronic Diseases by Telephone Survey), including the 2023 edition, as well as microdata, data dictionaries, and technical documentation, are available at:https://www.gov.br/saude/pt-br/composicao/svsa/inqueritos-de-saude/vigitel/vigitelThe VIGITEL dataset is fully anonymized and publicly accessible; therefore, no ethical approval was required for this secondary data analysis. References Monteiro CA, Cannon G, Levy RB, Moubarac JC, Louzada ML, Rauber F, Jaime PC. Ultra-processed foods: what they are and how to identify them. Public Health Nutr. 2019;22(5):936–41. Monteiro CA, Louzada ML, Steele-Martinez E, Cannon G, Andrade GC, Baker P, Touvier M. (2025). Ultra-processed foods and human health: the main thesis and the evidence. The Lancet , 406 (10520), 2667–2684. Martini D, Godos J, Bonaccio M, Vitaglione P, Grosso G. 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Avaliação do consumo alimentar e da ingestão de nutrientes na prática clínica. Arquivos Brasileiros de Endocrinologia Metabologia. 2009;53:617–24. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 14 May, 2026 Reviews received at journal 14 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviewers invited by journal 27 Apr, 2026 Editor assigned by journal 21 Apr, 2026 Editor invited by journal 20 Apr, 2026 Submission checks completed at journal 17 Apr, 2026 First submitted to journal 17 Apr, 2026 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-9441587","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631707817,"identity":"0b9e21dc-aa3c-4dd1-8d7f-261acfc8eb47","order_by":0,"name":"Rosália Garcia Neves","email":"data:image/png;base64,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","orcid":"","institution":"Federal University of Rio Grande","correspondingAuthor":true,"prefix":"","firstName":"Rosália","middleName":"Garcia","lastName":"Neves","suffix":""},{"id":631707819,"identity":"cf05fcd9-1b51-45eb-b135-0eefb2ffa263","order_by":1,"name":"Karla Machado","email":"","orcid":"","institution":"Federal University of Pelotas","correspondingAuthor":false,"prefix":"","firstName":"Karla","middleName":"","lastName":"Machado","suffix":""},{"id":631707820,"identity":"27c75fc4-ecaa-4a17-a775-7921049ccb26","order_by":2,"name":"Mirelle Oliveira Saes","email":"","orcid":"","institution":"Federal University of Rio Grande","correspondingAuthor":false,"prefix":"","firstName":"Mirelle","middleName":"Oliveira","lastName":"Saes","suffix":""},{"id":631707823,"identity":"bd752b3f-cbe7-436f-a4bf-8d351fdda895","order_by":3,"name":"Niely Galeão da Rosa Moraes","email":"","orcid":"","institution":"Federal University of Rio Grande","correspondingAuthor":false,"prefix":"","firstName":"Niely","middleName":"Galeão da Rosa","lastName":"Moraes","suffix":""},{"id":631707826,"identity":"c1f62ec2-4da6-49f1-b965-1d10f9e038b3","order_by":4,"name":"Júlia Kruger Peres","email":"","orcid":"","institution":"Catolic University of Pelotas","correspondingAuthor":false,"prefix":"","firstName":"Júlia","middleName":"Kruger","lastName":"Peres","suffix":""},{"id":631707828,"identity":"7b776273-dd6a-4445-9b65-e0f6f1bbddf4","order_by":5,"name":"Thaynã Ramos Flores","email":"","orcid":"","institution":"University of Illinois","correspondingAuthor":false,"prefix":"","firstName":"Thaynã","middleName":"Ramos","lastName":"Flores","suffix":""}],"badges":[],"createdAt":"2026-04-16 19:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9441587/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9441587/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108530127,"identity":"b433d61a-4ef8-4084-8b93-be6e7693266b","added_by":"auto","created_at":"2026-05-05 15:45:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":582711,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of ultra-processed food consumption on the day before the interview according to intersectional sociodemographic characteristics Brazil, VIGITEL, 2023 (N=21,690).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9441587/v1/2ef342308560a0b1fb98d144.png"},{"id":108804225,"identity":"4582610f-8415-49a6-acba-f3d43febf618","added_by":"auto","created_at":"2026-05-08 15:18:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103128,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of consuming five or more ultra-processed foods on the day before the interview, based on intersectional sociodemographic characteristics. Brazil. VIGITEL, 2023 (N=21,690).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9441587/v1/6fa5cd91083a6a8ea0073e6a.png"},{"id":108530129,"identity":"4db062f8-5eb0-4f32-8506-14b50ea3c7df","added_by":"auto","created_at":"2026-05-05 15:45:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":336103,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of consuming five or more ultra-processed foods the day before the interview, based on the Jeopardy Index (considering skin color and education level) stratified by sex. Brazil, VIGITEL, 2023 (N=21,690).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9441587/v1/da00704c20e5af7da21f9c5f.png"},{"id":108976781,"identity":"fffa49ad-fd76-4ffc-a899-10c69198f87d","added_by":"auto","created_at":"2026-05-11 11:28:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":839198,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9441587/v1/839d0c43-43bb-4593-b5d8-958ca2f8c3ae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The paradox of privilege: intersectional inequalities in ultra-processed food consumption in Brazil","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe consumption of ultra-processed foods (UPFs) has risen notably in recent decades, making it one of the major current public health issues (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). These foods are characterized by industrial formulations that include refined ingredients, additives, and low amounts of fresh foods, often with poor nutritional profiles that feature high levels of free sugars, saturated fats, sodium, and energy density, along with low fiber and micronutrients (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Scientific evidence consistently links the intake of ultra-processed foods with several negative health outcomes, such as cardiovascular diseases, type 2 diabetes, obesity, certain cancers, and higher all-cause mortality (\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7 CR8\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Brazil, the increase in ultra-processed food consumption occurs alongside the nutritional transition and changes in food systems, characterized by shifts in food production, distribution, and consumption patterns (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Data from national surveys, such as the Household Budget Survey (POF 2017\u0026ndash;2018), show that ultra-processed foods already make up a significant part of the Brazilian diet, accounting for about 20% of the calories consumed, with a 1.02 percentage point rise compared to 2008\u0026ndash;2009. This increase was more notable among men, black people, rural residents, individuals with less education, and those in the lowest income quintile, while a decline in intake was seen among people with higher education and income levels (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). These results emphasize the importance of targeting ultra-processed foods in public policies aimed at promoting adequate and healthy eating, particularly for populations facing greater social vulnerability (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the distribution of food consumption in the population is not uniform. Sociodemographic characteristics such as gender, race/skin color, and education play a key role in shaping eating habits, reflecting historical, economic, and cultural processes that create social inequalities in health(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Previous research has found significant differences in the consumption of ultra-processed foods based on these social markers, indicating that dietary patterns are closely linked to individuals' social conditions and life contexts (\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTraditionally, these characteristics have been examined separately. However, this method can oversimplify complex social phenomena by ignoring that different social markers do not act independently; instead, they are interconnected and overlap in creating inequalities, often intensifying their effects. In this way, the concept of intersectionality has become increasingly common in social epidemiology to understand how various dimensions of social identity, such as gender, race/skin color, and socioeconomic status, interact and result in specific patterns of exposure and vulnerability related to health.\u003c/p\u003e \u003cp\u003eDespite the growth of research on the health effects of ultra-processed foods, few nationwide studies examine their consumption from an intersectional perspective, especially using recent and comprehensive population data. Incorporating this approach can enhance understanding of inequalities in food consumption, helping to identify population groups with different patterns of exposure to ultra-processed foods.\u003c/p\u003e \u003cp\u003eIn this context, the present study aimed to analyze the prevalence of ultra-processed food consumption and examine its association with sociodemographic characteristics considered through an intersectional lens in adults living in Brazilian capitals, based on data from VIGITEL 2023.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eCross-sectional study using data from the VIGITEL survey\u0026mdash;Surveillance System of Risk and Protective Factors for Chronic Diseases by Telephone Survey\u0026mdash;covering data from 2023. VIGITEL is an annual survey conducted by the Brazilian Ministry of Health through telephone interviews with individuals aged 18 or older living in the 26 state capitals and the Federal District (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2023, for logistical reasons, a minimum of 800 interviews was established in each location. Due to the significant deterioration of landline telephone services in the country, half of the interviews were conducted via mobile phone. This approach ensured adequate representativeness and precision of estimates, with a final sample of 400 landline and 400 mobile phone interviews in each location, aiming for a 95% confidence level and a maximum margin of error of approximately 4 percentage points for any risk factor measured in the study. More details on the sampling process and methods used in this edition of VIGITEL are available in the official publications (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Ethical approval was obtained from the National Committee for Ethics in Research with Human Beings of the Ministry of Health (65610017.1.0000.0008). VIGITEL databases are publicly available (\u0026lt;\u0026thinsp;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://svs.aids.gov.br/download/Vigitel/%3E\u003c/span\u003e\u003cspan address=\"http://svs.aids.gov.br/download/Vigitel/%3E\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, the outcome was the consumption of ultra-processed foods, assessed through a self-reported questionnaire that included 13 subgroups ingested the day before the interview: \"Now I am going to list some foods and I would like you to tell me if you ate any of them \u003cem\u003eYESTERDAY\u003c/em\u003e (from when you woke up to when you went to sleep).\" The foods included in the questionnaire were soft drinks; fruit juice in boxes, cartons, or cans; powdered soft drinks; chocolate drinks; flavored yogurts; packaged snacks (or chips) or crackers; cookies/sweet crackers, stuffed cookies, or packaged cookies; chocolate, ice cream, gelatin, flan, or other industrialized desserts; sausage, processed meats (e.g., sausage, mortadella, or ham); sliced bread; hot dogs or hamburgers; mayonnaise, ketchup, or mustard; margarine; instant noodles, packaged soup, frozen lasagna, or other ready-to-eat dishes bought from frozen. The answer options were \"yes\" and \"no.\" Based on positive responses to the consumption of these foods, ranging from 0 to 13, a cutoff point was established at 5 or more subgroups (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe self-reported covariates used were sex (female/male), skin color (Caucasian, Black, Brown, Yellow, and Indigenous), and schooling in full years classified as low, middle, and high education (0\u0026ndash;8, 9\u0026ndash;11, and 12 or more). These variables were used to construct a measure of social inequality based on the \"multiple risk\" principle, known as the Jeopardy index. This approach guides research on the theory of intersectionality and social phenomena. The multiple risk framework shows that various aspects of an individual's identity, such as sex, class, or race, which lead to discrimination or oppression, are interdependent and exert a compound or cumulative effect (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The intersectionality of these sociodemographic characteristics was categorized into five levels (0 to 4) and created by combining the categories of the variables mentioned: variables with two categories were coded as \"0 and 1,\" and those with three categories as \"0, 1, and 2.\" Within the framework of intersectionality, a score of 0 indicates the lowest inequality (male, Caucasian, and with a high level of education), while a score of 4 indicates the highest degree of inequality (female, Black/Brown/Yellow people, and Indigenous people with a low level of education).\u003c/p\u003e \u003cp\u003eData analysis was conducted using Stata 15.0 statistical software. Prevalence rates and their 95% confidence intervals (95% CIs) for the outcomes were calculated based on exposures using the chi-square test. Poisson regression was used to estimate prevalence ratios (PRs) and 95% CIs. For all analyses, the survey (svy) command was employed for weighting. Intersectionality analyses of sociodemographic characteristics, both in the total sample and stratified by sex, were performed using the Jeopardy index, which considers the score (the higher the score, the greater the risk), along with creating exposure variables that examine the intersection of social markers such as sex and skin color, using schooling as a stratifying variable.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe sample included 21,690 adults, with 54.0% identifying as female, 60.2% as Black, Brown, Yellow, or Indigenous, and 41.3% having 9 to 11 years of education. Regarding intersectional sociodemographic traits, 8.2% were White men with higher education, and 8.7% were Black, Brown, Yellow, or Indigenous women with lower education. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the prevalence of ultra-processed food consumption. The most common items were margarine (43.4%), sliced bread/hot dog buns/hamburger buns (36.5%), chocolate/ice cream/gelatin/flan (29.0%), and soft drinks (28.2%). Less frequently consumed foods included powdered soft drinks (10.5%), chocolate drinks (10.2%), and instant noodles/packaged soup/lasagna (6.1%). The percentage of individuals who ate five or more of the evaluated ultra-processed foods the day before the interview was 17.7% (95%CI 16.6;18.9).\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\u003ePrevalence and 95% confidence interval of ultra-processed food consumption on the day before the interview. Brazil, VIGITEL, 2023 (N\u0026thinsp;=\u0026thinsp;21,690).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUltra-processed foods\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrevalence (CI95%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMargarine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.4 (41.9\u0026ndash;44.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSliced ​​bread, hot dog buns, or hamburger buns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36.5 (35.0-37.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChocolate, ice cream, gelatin, flan, or other commercially produced desserts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.0 (27.6\u0026ndash;30.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoft drink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.2 (26.8\u0026ndash;29.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSausage, chorizo, mortadella, or ham\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.6 (25.3\u0026ndash;27.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePackaged snack or savory biscuit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.0 (23.7\u0026ndash;26.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSweet biscuit, filled biscuit, or packaged cake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.5 (20.3\u0026ndash;22.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayonnaise, ketchup, or mustard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.4 (17.2\u0026ndash;19.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlavored yogurt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.2 (13.1\u0026ndash;15.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFruit juice in a carton or can\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.3 (12.3\u0026ndash;14.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePowdered soft drink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.5 (9.6\u0026ndash;11.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChocolate drink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.2 (9.2\u0026ndash;11.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstant noodles, packaged soup, lasagna, or other ready-made meals purchased frozen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.1 (5.4\u0026ndash;6.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConsumption of five or more ultra-processed foods\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e17.7 (16.6;18.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eValues ​​with \u003cem\u003esvy\u003c/em\u003e weighting\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen evaluating ultra-processed food consumption based on intersectional sociodemographic factors, it is seen that the most privileged group (men, whites, and those with higher education) consumes a larger share of these foods compared to the most vulnerable group (women, Black/Brown/Yellow/Indigenous people with lower education), except for margarine, packaged snacks or savory biscuits, and powdered soft drinks (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding the consumption of five or more ultra-processed foods on the previous day, the adjusted analysis shows that women had a 36% lower prevalence of ultra-processed food consumption compared to men. Additionally, people with 9 to 11 years of schooling showed a 25% higher prevalence of consumption compared to those with more schooling (12 or more years). According to the Jeopardy index, black, brown, yellow, or indigenous women with low schooling have a prevalence of consuming at least five of these foods that is 42% lower compared to white men with high schooling (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003ePrevalence and 95% confidence interval of consuming five or more ultra-processed foods on the day before the interview, according to sociodemographic characteristics and Jeopardy index. Brazil. VIGITEL, 2023 (N\u0026thinsp;=\u0026thinsp;21,690).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrevalence ratio\u003c/p\u003e \u003cp\u003e(CI95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrevalence ratio\u003c/p\u003e \u003cp\u003e(CI95%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.0 (20.1;24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.1 (12.9;15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64 (0.56;0.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin color\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaucasian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.1 (15.2;19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack/Brown/Yellow/Indigenous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.4 (17.0;19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.05 (0.91;1.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (years)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.6 (11.7;15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81 (0.67;0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.1 (19.3;23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25 (1.08;1.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.7 (14.8;18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJeopardy index\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 (less risk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.9 (17.7;28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.8 (16.3;21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82 (0.62;1.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.7 (16.9;20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82 (0.63;1.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.1 (14.2;18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.70 (0.53;0.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 (more risk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.2 (10.3;16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58 (0.41;0.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eModel: sex, skin color, and education level\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdditionally, when analyzing the occurrence of consuming five or more ultra-processed foods based on sex and skin color in an intersectional way across different education levels, a higher prevalence was observed in men with medium education (9 to 11 years), with 26.7% in black/brown/yellow/indigenous men and 27.3% in whites. Overall, those with less education showed lower consumption frequencies, which was also seen in women with higher education (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the prevalence of consuming five or more ultra-processed foods according to the Jeopardy index (which considers skin color and education), broken down by sex. It was observed that the relationship between consumption and the Jeopardy index differs by gender, with a stronger association seen among men.\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eThis study examined ultra-processed food consumption among adults in Brazilian state capitals, considering sociodemographic factors through an intersectional lens. It was found that about two in ten individuals ate five or more ultra-processed foods the day before the interview; ultra-processed food consumption does not seem to be strongly influenced by social factors in Brazil. Our findings show that the most privileged group (men, whites, and those with higher education) had higher intake of ultra-processed foods compared to the most vulnerable group (women, Black/Brown/Yellow/Indigenous people with low education).\u003c/p\u003e \u003cp\u003eAlthough the prevalence of ultra-processed food consumption observed in this study may seem moderate, it indicates frequent consumption of low-nutritional-value foods, which tends to accumulate over time and has significant implications for the risk of chronic diseases (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Women had a 36% lower prevalence of ultra-processed food consumption compared to men. This shows that the consumption of ultra-processed foods varies by sex and race, supporting previous findings indicating higher consumption among men, especially black/brown men (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA possible explanation for this result relates to gender differences in eating habits and household chores. Women are traditionally more encouraged to develop cooking skills and often assume greater responsibility for meal preparation at home (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Furthermore, studies show that women tend to consume more natural or minimally processed foods, such as fruits and vegetables, compared to men (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In this regard, culinary knowledge and cooking at home have been linked to higher diet quality (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe lower prevalence of high ultra-processed food consumption among women from minority racial groups and with less education might initially be seen as a healthier dietary pattern. However, this finding should be approached with caution. Evidence from national studies suggests that lower consumption of ultra-processed foods may mainly result from economic constraints, lower purchasing power, and limited access to these products, rather than a greater commitment to a healthy diet. In such contexts, reduced consumption of ultra-processed foods can occur alongside other forms of food inequality, such as food insecurity, dietary monotony, and low food diversity, emphasizing the importance of considering the broader social environment in interpretations.\u003c/p\u003e \u003cp\u003eOn the other hand, the lower prevalence of high consumption among women with higher education may be linked to greater access to health and nutrition information, a greater appreciation of healthy eating practices, and a better ability to incorporate nutritional recommendations into daily life. However, it is important to emphasize that such eating practices are connected to more favorable social conditions and greater choices, which facilitate the adoption of behaviors aligned with healthy eating guidelines (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe lack of social factors influencing the consumption of ultra-processed foods identified in this study may be explained by several factors. This finding can be attributed to several factors (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The first reason may be the high availability and easier access to ultra-processed foods across the country, leading to a recent increase in consumption among all population groups (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), including men, rural residents (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), and the most vulnerable groups identified in this study (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results observed in this study also reinforce the idea that the consumption of these foods may be becoming a habit in Brazilian society and that this dietary pattern is associated with food addiction (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). However, it is also important to note that these findings could be linked to other factors, such as the possible lack of access to these foods among people in vulnerable social conditions (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), since analyses indicate that populations living in more disadvantaged neighborhoods have greater access to both healthy and unhealthy food outlets (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Additionally, cultural factors may play a role, as many families still value preparing foods at home, such as rice and beans, which are culturally central to the Brazilian diet (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). It is also known that most of the Brazilian population regularly consumes fruits and vegetables and follows recommendations to limit red meat intake, without regularly replacing meals with snacks (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Conversely, families in more socially vulnerable contexts often maintain higher consumption of basic foods and traditional preparations, such as rice and beans, which continue to form the foundation of the Brazilian diet (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). This dietary pattern may partly explain the lower prevalence of high ultra-processed food consumption observed among certain population groups.\u003c/p\u003e \u003cp\u003eAlong with the known health risks of ultra-processed foods demonstrated in the literature (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), increased consumption of these products is also linked to environmental impacts. The production of ultra-processed foods significantly contributes to greenhouse gas emissions, which in turn lead to deforestation, soil degradation, and biodiversity loss (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Therefore, policies should be tailored to populations that consume the most, considering health, lifestyle, and sociodemographic characteristics.\u003c/p\u003e \u003cp\u003eIn this context, implementing public policies to address the increasing consumption of ultra-processed foods becomes essential. In Brazil, initiatives such as the Food Guide for the Brazilian Population represent significant progress by making the degree of food processing a key factor in guiding food choices (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). However, further efforts are necessary, especially in regulating the food industry and the food environment. Successful public health strategies, like tobacco control policies, demonstrate that regulatory measures targeting the industry can substantially reduce risk behaviors at the population level (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Reducing the influence of the ultra-processed food industry would involve disrupting their business models, reallocating resources to other types of food producers, and establishing strong safeguards against conflicts of interest in policymaking, research, and professional practice. Additionally, it requires framing ultra-processed foods as a critical global health issue, building legal, research, and communication capacities, and promoting a fairer, more encouraging transition to diets low in ultra-processed foods (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present study has some limitations that should be considered when interpreting the results. First, data were collected only in Brazil's capitals, which may not reflect conditions in smaller municipalities or rural areas across the country. Additionally, food consumption was assessed through recall of the day before the interview, which may not reflect individuals' habitual eating patterns, especially given potential variations in food intake between weekdays and weekends, times when people tend to eat different foods. However, this method is widely used in population surveys and allows for the estimation of food consumption in large samples with relative ease (\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Furthermore, the information collected is self-reported, which may be subject to bias. Finally, categorizing the skin color variable grouped different racial groups into the same category for analysis, which may mask important differences between these groups. Nonetheless, we had to operationalize it this way for statistical purposes.\u003c/p\u003e \u003cp\u003eEven with these limitations, the study has notable strengths. Using data from a nationwide population survey, with a large sample size and standardized data collection methods, improves the reliability of the estimates. Additionally, analyzing UPFs consumption through social markers in an intersectional way advances beyond traditional methods that examine social markers separately, enabling a more comprehensive understanding of inequalities in food consumption among Brazilian adults.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, two out of ten people consumed five or more ultra-processed foods the day before the interview. Consumption was higher among men, white individuals, and those with higher education, highlighting significant inequalities. Given the widespread availability of these foods in the food environment, the study's findings reinforce the need for regulatory and intersectoral policies to develop industry-level strategies targeting adults, similar to those implemented for tobacco control.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The data is public and was used for research purposes, respecting ethical principles and the confidentiality of information provided by respondents.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.G.N. performed the statistical analyses. R.G.N, K.M., N.G.R.M. and T.R.F. wrote all sections of the article and worked on the literature review. M.O.S and J.K.P. critically reviewed the entire manuscript. All authors approved this version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003e​All people who participated in the survey and CAPES (Coordination for the Improvement of Higher Education Personnel).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed during the current study are publicly available from the Brazilian Ministry of Health. Data from the VIGITEL (Surveillance System of Risk and Protective Factors for Chronic Diseases by Telephone Survey), including the 2023 edition, as well as microdata, data dictionaries, and technical documentation, are available at:https://www.gov.br/saude/pt-br/composicao/svsa/inqueritos-de-saude/vigitel/vigitelThe VIGITEL dataset is fully anonymized and publicly accessible; therefore, no ethical approval was required for this secondary data analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMonteiro CA, Cannon G, Levy RB, Moubarac JC, Louzada ML, Rauber F, Jaime PC. Ultra-processed foods: what they are and how to identify them. Public Health Nutr. 2019;22(5):936\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonteiro CA, Louzada ML, Steele-Martinez E, Cannon G, Andrade GC, Baker P, Touvier M. (2025). Ultra-processed foods and human health: the main thesis and the evidence. \u003cem\u003eThe Lancet\u003c/em\u003e, \u003cem\u003e406\u003c/em\u003e(10520), 2667\u0026ndash;2684.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartini D, Godos J, Bonaccio M, Vitaglione P, Grosso G. Ultra-processed foods and nutritional dietary profile: a meta-analysis of nationally representative samples. Nutrients. 2021;13(10):3390.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLouzada MLDC, Costa CDS, Souza TN, Cruz GLD, Levy RB, Monteiro CA. Impact of the consumption of ultra-processed foods on children, adolescents and adults\u0026rsquo; health: scope review. Cadernos de Saude Publica. 2022;37:e00323020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCanhada SL, Vigo \u0026Aacute;, Luft VC, Levy RB, Matos A, del Carmen Molina SM, Schmidt M, M. I. 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Dispon\u0026iacute;vel em: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gov.br/saude/pt-br/centrais-de-conteudo/publicacoes/svsa/vigitel/vigitel-brasil-2023-vigilancia-de-fatores-de-risco-e-protecao-para-doencas-cronicas-por-inquerito-telefonico/view\u003c/span\u003e\u003cspan address=\"https://www.gov.br/saude/pt-br/centrais-de-conteudo/publicacoes/svsa/vigitel/vigitel-brasil-2023-vigilancia-de-fatores-de-risco-e-protecao-para-doencas-cronicas-por-inquerito-telefonico/view\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSattamini IF. (2019). \u003cem\u003eInstrumentos de avalia\u0026ccedil;\u0026atilde;o da qualidade de dietas: desenvolvimento, adapta\u0026ccedil;\u0026atilde;o e valida\u0026ccedil;\u0026atilde;o no Brasil\u003c/em\u003e (Doctoral dissertation, Universidade de S\u0026atilde;o Paulo).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCosta, C. D. S., Faria, F. R. D., Gabe, K. T., Sattamini, I. F., Khandpur, N., Leite,F. H. M., \u0026hellip; Monteiro, C. A. (2021). Nova score for the consumption of ultra-processed foods: description and performance evaluation in Brazil. \u003cem\u003eRevista de Sa\u0026uacute;de P\u0026uacute;blica\u003c/em\u003e, \u003cem\u003e55\u003c/em\u003e, 13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKing DK. Multiple jeopardy, multiple consciousness: The context of a Black feminist ideology. Signs: J women Cult Soc. 1988;14(1):42\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrepaldi BVC, Okada LM, Claro RM, Louzada MLDC, Rezende LF, Levy RB, Azeredo CM. Educational inequality in consumption of in natura or minimally processed foods and ultra-processed foods: The intersection between sex and race/skin color in Brazil. Front Nutr. 2022;9:1055532.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilva Oliveira MS, Fernandez Unsain RA, Sato M, Ulian PD, Scagliusi MD, F. B., Cardoso MA. Because I saw my mother cooking: the sociocultural process of learning and teaching domestic culinary skills of the Western Brazilian Amazonian women. Food Foodways. 2022;30(4):310\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantiago LB, Francisco PMSB, Cocetti M, de Assump\u0026ccedil;\u0026atilde;o D. Food consumption among older adults: differences between men and women. Geriatr Gerontol Aging. 2025;19:1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGesteiro E, Garc\u0026iacute;a-Carro A, Aparicio-Ugarriza R, Gonz\u0026aacute;lez-Gross M. Eating out of home: influence on nutrition, health, and policies: a scoping review. Nutrients. 2022;14(6):1265.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMachado PP, Claro RM, Canella DS, Sarti FM, Levy RB. Price and convenience: The influence of supermarkets on consumption of ultra-processed foods and beverages in Brazil. Appetite. 2017;116:381\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonteiro CA, Moubarac JC, Cannon G, Ng SW, Popkin B. Ultra-processed products are becoming dominant in the global food system. Obes Rev. 2013;14:21\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSerafim P, Borges CA, Cabral-Miranda W, Jaime PC. Ultra-processed food availability and sociodemographic associated factors in a Brazilian municipality. Front Nutr. 2022;9:858089.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilveira VNC, Dos Santos AM, Fran\u0026ccedil;a AKTC. Determinants of the consumption of ultra-processed foods in the Brazilian population. Br J Nutr. 2024;132(8):1104\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHallum SH, Hughey SM, Wende ME, Stowe EW, Kaczynski AT. Healthy and unhealthy food environments are linked with neighbourhood socio-economic disadvantage: an innovative geospatial approach to understanding food access inequities. Public Health Nutr. 2020;23(17):3190\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarvalho A, Gerhard F, de Ferreira AA. Food experiences at home: the role of ethnic food in Brazilian family relations. Latin Am Bus Rev. 2021;22(1):53\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantin F, Gabe KT, Levy RB, Jaime PC. Food consumption markers and associated factors in Brazil: distribution and evolution, Brazilian National Health Survey, 2013 and 2019. Cadernos de saude publica. 2022;38:e00118821.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePagliai G, Dinu M, Madarena MP, Bonaccio M, Iacoviello L, Sofi F. Consumption of ultra-processed foods and health status: a systematic review and meta-analysis. Br J Nutr. 2021;125(3):308\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLane, M. M., Davis, J. A., Beattie, S., G\u0026oacute;mez-Donoso, C., Loughman, A., O'Neil, A.,\u0026hellip; Rocks, T. (2021). Ultraprocessed food and chronic noncommunicable diseases: a systematic review and meta‐analysis of 43 observational studies. \u003cem\u003eObesity reviews\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(3), e13146.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAskari M, Heshmati J, Shahinfar H, Tripathi N, Daneshzad E. Ultra-processed food and the risk of overweight and obesity: a systematic review and meta-analysis of observational studies. Int J Obes. 2020;44(10):2080\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeferidi P, Scrinis G, Huybrechts I, Woods J, Vineis P, Millett C. The neglected environmental impacts of ultra-processed foods. Lancet Planet Health. 2020;4(10):e437\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBortolini GA, de Paiva Moura AL, de Lima AMC, Moreira HDOM, Medeiros O, Diefenthaler ICM, de Oliveira ML. Food guides: a strategy to reduce the consumption of ultra-processed foods and prevent obesity. Revista Panam de Salud P\u0026uacute;blica. 2019;43:e59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonteiro CA, Cannon GJ. The role of the transnational ultra-processed food industry in the pandemic of obesity and its associated diseases: problems and solutions. World Nutr. 2019;10(1):89\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaker, P., Slater, S., White, M., Wood, B., Contreras, A., Corval\u0026aacute;n, C., \u0026hellip; Barquera,S. (2025). Towards unified global action on ultra-processed foods: understanding commercial determinants, countering corporate power, and mobilising a public health response.\u003cem\u003eThe Lancet\u003c/em\u003e, \u003cem\u003e406\u003c/em\u003e(10520), 2703\u0026ndash;2726.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCosta CS, Del-Ponte B, Assun\u0026ccedil;\u0026atilde;o MCF, Santos IS. Consumption of ultra-processed foods and body fat during childhood and adolescence: a systematic review. Public Health Nutr. 2018;21(1):148\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSampaio LR, Silva MCM, Roriz AKC, Leite VR. (2012). Inqu\u0026eacute;rito alimentar. \u003cem\u003eSampaio, LR Avalia\u0026ccedil;\u0026atilde;o nutricional. EDUFBA\u003c/em\u003e, 103\u0026ndash;112.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFisberg RM, Marchioni DML, Colucci ACA. Avalia\u0026ccedil;\u0026atilde;o do consumo alimentar e da ingest\u0026atilde;o de nutrientes na pr\u0026aacute;tica cl\u0026iacute;nica. Arquivos Brasileiros de Endocrinologia Metabologia. 2009;53:617\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Eating, Epidemiologic Studies, Food, Processed, Health Inequities, Intersectional Framework","lastPublishedDoi":"10.21203/rs.3.rs-9441587/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9441587/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo analyze the prevalence of ultra-processed food consumption and to investigate its association with sociodemographic characteristics considered from an intersectional perspective among adults living in Brazilian state capitals.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis was a cross-sectional study using data from the 2023 Surveillance System of Risk and Protective Factors for Chronic Diseases, conducted with adults aged 18 years or older. The outcome was defined as the consumption of at least five groups of ultra-processed foods on the day prior to the interview. The exposure variables were sex, skin color, and education, and the intersectionality of these characteristics was constructed using the Jeopardy Index.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe sample comprised 21,690 individuals. The prevalence of consumption of five or more groups of ultra-processed foods was 17.7% (95% CI 16.6\u0026ndash;18.9). Black, brown, yellow, and Indigenous women with low educational levels showed a 42% lower prevalence of consumption of these foods compared to white men with higher education.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eApproximately one-fifth of the population consumed at least five groups of ultra-processed foods on the previous day, with the most privileged group (men, white individuals, and those with higher education) showing the highest prevalence of consumption. Public policies aimed at reducing the consumption of ultra-processed foods are needed, taking into account the intersectionality of social markers, since the occurrence of consumption differs across groups.\u003c/p\u003e","manuscriptTitle":"The paradox of privilege: intersectional inequalities in ultra-processed food consumption in Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-05 15:45:28","doi":"10.21203/rs.3.rs-9441587/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"185982103039698132196036431225689688299","date":"2026-05-14T11:03:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-14T10:27:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264371222882982400581113945011761485719","date":"2026-05-07T10:44:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-27T04:55:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-21T06:19:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-20T15:22:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-17T21:21:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-04-17T20:24:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"94075049-e30b-4be0-b587-1ab8ab588fff","owner":[],"postedDate":"May 5th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"185982103039698132196036431225689688299","date":"2026-05-14T11:03:33+00:00","index":29,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-14T10:27:12+00:00","index":28,"fulltext":""},{"type":"reviewerAgreed","content":"264371222882982400581113945011761485719","date":"2026-05-07T10:44:29+00:00","index":25,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T15:45:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-05 15:45:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9441587","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9441587","identity":"rs-9441587","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}