Abstract
Background
Food consumption of adolescents is characterized by a highly prevalent intake of ultra-processed food and a low intake of fresh or minimally processed foods. Factors such as income, gender, race/skin color, and their intersection can influence this consumption. The objective of this study was to analyze the relationship between food consumption and intersections of race/skin color and gender among Brazilian adolescents.
Methods
Data from Brazil’s National Student Health Survey of 2019 was used. A total of 10 analytical categories (exposure) were designed, based on the intersection of binary gender (man and woman) and race/skin color (white, Asian, black, brown - pardo in Brazilian Portuguese, and Indigenous) to represent social identities. As outcomes, assessments were made of regular intake (5 or more times a week) of three subgroups of fresh or minimally processed food (beans, fruits, and vegetables) and two subgroups of ultra-processed foods (sweet treats and soft drinks). Crude and adjusted (for age group, goods and services score, region and type of school) Poisson regression models were used to estimate prevalence ratios (PR).
Results
Based on data from 155,415 Brazilian school-aged adolescents, it has been identified that regular intake of fruit and vegetables was higher among white men. Compared to white men (reference category), in three of the five food subgroups analyzed (beans, vegetables, and sweets), black (PR 0.94, 0.79, and 1.48, respectively), brown (PR 0.91, 0.89, and 1.42, respectively) and Indigenous (PR 0.85, 0.82, and 1.38, respectively) women showed a statistically significant disadvantage. Black men reported lower PR of regular intake of vegetables and higher of sweet treats (PR 0.87 and 1.15) while brown, Asian, and Indigenous men presented higher PR of regular intake of beans in comparison to the reference category.
Conclusion
Food consumption of adolescents differed among the analytical categories that represent social identities. Black and Indigenous women showed greater vulnerability compared to white men, who have a privileged social identity because of racial and gender inequalities. This study takes a novel approach by employing inter-categorical analysis of social identities with a view to presenting them as social experiences.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s12889-026-28035-3.
Keywords: Gender inequality, Racial inequality, Ultra-processed foods, Fresh or minimally processed foods, Health survey
Background
Low prevalence of fruit and vegetable intake and high prevalence of soft drink intake are very common in the dietary habits of adolescents. Approximately 34.5% of adolescents consumed fruit and 20.6% consumed vegetables less than once per day, whereas 46.1% consumed soft drinks at least once per week [1], which suggests overall inadequate dietary quality. In 2015, according to data from Brazil’s National Student Health Survey (PeNSE), a quarter of the ninth-graders showed health risk behaviors. Risk factors include inadequate food consumption – characterized by frequent consumption of ultra-processed foods and low regular consumption of fresh or minimally processed foods [2]. The dietary intake of adolescents, when compared to that of adults and the elderly, showed greater energy share of ultra-processed foods [3].
Some characteristics of ultra-processed foods, such as being hyperpalatable, ready to consume, widely available, and heavily advertised [4], may influence their consumption by adolescents. Furthermore, with a focus on food consumption of adolescents, this is associated with the practice of eating meals with parents and guardians [5] and sedentary behavior - for example, while watching television, playing video games, using a computer [6] - as well as with sociodemographic and economic factors [7].
In addition, food consumption may be related to working and housing conditions, routine, time spent to and from school - examples of intermediate social determinants of health that are influenced by structural determinants, such as social class, race/skin color, gender, sexuality, territory, education, occupation, and income, and these determinants tend to impact vulnerable individuals [8, 9]. When these factors intersect and operate together, they can increase inequalities.
Intersectionality can be understood as an analytical tool that examines the dynamics of overlapping axes of power and subordination—such as racism, patriarchy, and classism—and how they operate as structural determinants [10, 11]. Racism and sexism operate and sustain ideologies of domination within a context of dependent patriarchal-racist capitalism, which functions to transform differences into inequalities, imposing poorer living conditions on social groups such as Black and Indigenous women [12].
In the literature on intersectionality and public health, cross-sectional analyses were used in studies on body mass index and waist circumference measurements in the USA [13]; health inequalities in different countries, including urban violence-induced youth mortality in Brazil [14]. In Brazil, in addition to research on violence, evidence has been produced - from the intersection of socio-demographic and economic factors and food and nutritional insecurity [15, 16], documentary analysis of public policies on obesity [17], health determinants [18]; risk factors for cardiovascular diseases [19]; and food intake and educational inequality in adults [20]. Notably, most body of evidence on intersectionality has been produced in the Global North (mainly in the USA, where the term was coined). However, intersectionality should be studied in different contexts, for example, in middle- and low-income countries, such as Brazil, where exposure to the mechanisms of social “oppression” may differ.
Given the ability of the intersectional tool to identify overlapping inequalities by shedding light on the interrelation of the different categories related to discrimination and oppression, and the lack of research addressing the intersection of race/color and gender and food intake by adolescents, the objective of this study is to analyze the relationship between food consumption and intersections of race/skin color and gender among Brazilian adolescents.
Methods
Study design and sampling
This is a cross-sectional study based on data from Brazil’s National Student Health Survey (PeNSE), conducted between April and September 2019 by the Brazilian Institute of Geography and Statistics in partnership with the Ministry of Health, with support from the Ministry of Education.
PeNSE used a complex sampling plan, namely a cluster sampling design in two stages of selection: the first one referred to schools and the second, to classes. The sample was composed of adolescent students from classes that were selected using information from the 2017 School Census, which was conducted by the National Institute of Educational Studies and Research Anisio Teixeira – INEP [21]. More details on the sampling plan can be found in the PeNSE 2019 report [21]. The population of the present study is composed of elementary (7th to 9th graders) and high school (10th to 12th graders) adolescent students attending morning, afternoon, and evening classes in public and private schools. In the present study, individuals with a lack of information on race/skin color were excluded, given the impossibility to construct the exposure variable.
Study variables
The data were collected using the Student and the School Environment questionnaires, available on the IBGE website [22]. The PeNSE 2019 report provides more details on the questionnaires [21]. The variables for this study were determined using the Student’s questionnaire. Among the 14 themes addressed in the questionnaire, two were selected for this study; 10 questions were from the general information module and 5 questions from the food intake module.
The exposure variable - intersection of race/skin color and gender - was designed using the sociodemographic variables race/skin color (white, black, brown - pardo in Brazilian Portuguese, Asian, and Indigenous), and gender (men and women). The race/skin color variable was considered a social marker of racial inequality, according to the publication “Social Inequalities by Color or Race in Brazil” [23], and the sex variable was used as a proxy of gender and a marker of gender inequality in the present study. The answers were combined to create 10 analytical categories - white man, white woman, brown man, brown woman, black man, black woman, Asian man, Asian woman, Indigenous man, and Indigenous woman - which represent social identities.
The outcome variables (regular food consumption) were designed using the questions about the intake of three subgroups of fresh or minimally processed food (beans, vegetables, and fruits) and two subgroups of ultra-processed foods (sweet treats and soft drinks), defined according to the NOVA classification [24]. For all items, the question was “In the last 7 days, how many days did you eat/drink (food/drink)?”. Regular intake considered the consumption greater than or equal to five days.
The following sociodemographic variables were used: region (North, Northeast, Southeast, South, and Midwest), type of school (public and private), age group (less than 13 years, 13 to 15 years, 16 or 17 years, and 18 years of age or more), and goods and services score. The basic sociodemographic variables for the design of the score were obtained from the following questions: “Do you have a mobile phone?”; “Do you have a computer or a laptop in your home?”; “Do you have Internet access at home?”; “Does anyone living in your home have a car?”; “How many full bathrooms, with toilet and shower, are there in your home?”; “Is there a domestic worker performing paid work in your home three or more days a week?”. The goods and services score was designed using the method used by Levy et al. [25]. Household goods and services were assigned a weight equivalent to the inverse of the frequency of ownership; the results of this calculation were added and subsequently categorized into tertiles, which was used in descriptive analyses and adjustments. The first tertile referred to the lowest values of the sum of the distribution, forming the poorest tertile, while the richest tertile (the third tertile) was built using the highest values of the distribution. The score was also categorized into median values for sensitivity analyses.
Data analysis
Descriptive analyses used the confidence intervals (95% CI) of the sociodemographic variables to check the distribution of the study characteristics in the population. Food consumption was described as a proportion of regular intake of fresh or minimally processed foods and ultra-processed foods. A significant difference was assumed based on the comparison between the 95% CI values and the absence of overlap between the intervals, considering the significance level of 5%. Additionally, sensitivity analysis stratified by the median of the goods and service score to assess the distribution of the prevalence of regular food consumption was conducted.
Poisson regression was used to estimate the prevalence ratios (PR) of the association between the analytical categories, which were created based on the intersection of race/skin color and gender and the regular food intake of fresh or minimally processed and ultra-processed foods. The category corresponding to white men was used as a reference, based on the privilege of gender and race of the social identity represented by this category [12, 26, 27].
The variables considered for adjusting the models were: goods and services score, region, age group, and type of school. The first model (model 1) was adjusted for individual factors – age group and goods and services score, and the second model (model 2) was adjusted for region and type of school. As models 1 and 2 may include adjustment for possible mediators, the crude model was considered as the main one.
The analyses were performed with the software STATA SE version 14 (Statacorp College Station USA), using the survey module, which considers the complex sampling design of PeNSE and its weighting factors.
Results
The present study evaluated data from 155,415 Brazilian adolescent students. The most prevalent categories among them were brown women (23.6%). The North, Northeast, and Midwest regions showed a higher prevalence of adolescents identified as brown women (33.9%, 28.3% and 26.7%, respectively), compared to the other categories, while the South region showed a higher prevalence of white men and women (31.0% and 30.9%, respectively). The highest prevalence of black men and women was found in the Northeast region, when compared to the other regions (8.9% and 7.7%, respectively). Brown women (24.9%) were the most prevalent in public schools, while white women and white men were the most prevalent in private schools (28.3% and 27.7%, respectively). As for goods and services score, brown women were the most prevalent (24.1%) in the first tertile, which was the poorest, while white men (26.3%) predominated in the third tertile, which was the richest. There was a low prevalence of black women (3.5%) in the third tertile (Table 1).
Table 1.
Brazilian schoolchildren’s sociodemographic characteristics according to the intersection race/skin color and gender. Brazil, 2019
| Sociodemographic characteristics | Analytical categories | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| White Man |
White Woman | Brown Man | Brown Woman |
Black Man | Black Woman | Asian Man | Asian Woman | Indigenous Man |
Indigenous Woman | |
| n = 29,991 | n = 30,155 | n = 32,278 | n = 36,037 | n = 9,098 | n = 7,596 | n = 2,447 | n = 3,064 | n = 2,438 | n = 2,311 | |
| % (95% CI) | % (95% CI) | % (95% CI) | % (95% CI) | % (95% CI) | % (95% CI) | % (95% CI) | % (95% CI) | % (95% CI) | % (95% CI) | |
| Brazil |
17.9 (17.3,18.5) |
17.7 (17.3,18.2) |
20.5 (20.0,21.0) |
23.6 (23.0,24.2) |
7.2 (6.9,7.6) |
6.1 (5.8,6.4) |
1.9 (1.7,2.0) |
1.9 (1.7,2.1) |
1.7 (1.5,1.8) |
1.6 (1.4,1.7) |
| Regions | ||||||||||
| North |
10.0 (9.2,10.9) |
10.7 (9.8,11.6) |
27.2 (25.8,28.6) |
33.9 (32.5,35.3) |
5.4 (4.9,6.0) |
5.1 (4.3,5.9) |
1.8 (1.4,2.3) |
1.8 (1.5,2.2) |
2.4 (1.8,3.1) |
1.8 (1.5,2.2) |
| Northeast |
11.9 (11.4, 12.4) |
11.0 (10.5,11.6) |
23.6 (22.7,24.5) |
28.3 (27.5,29.1) |
8.9 (8.4,9.5) |
7.7 (7.1,8.4) |
2.3 (2.0,2.6) |
2.5 (2.2,2.8) |
1.9 (1.6,2.1) |
1.9 (1.7,2.2) |
| Southeast |
20.2 (19.0, 21.5) |
20.4 (19,4,21,4) |
19.2 (18,2,20,1) |
20.3 (19,2,21,5) |
7.4 (6,7,8,1) |
6.3 (5,8,6,9) |
1.7 (1,4,1,9) |
1.6 (1,3,1,9) |
1.5 (1.3,1.7) |
1.4 (1.1,1.7) |
| South |
31.0 (29.1,32.9) |
30.9 (29.4,32.4) |
11.7 (10.7,12.8) |
13.4 (12.2,14.8) |
4.8 (4.1,5.6) |
3.4 (2.7,4.2) |
1.5 (1.1,1.9) |
1.3 (1.0,1.7) |
1.1 (0.9,1.5) |
0.9 (0.7,1.2) |
| Midwest |
15.7 (14.8,16.6) |
15.3 (14.5,16.3) |
22.7 (21.7,23.8) |
26.7 (25.6,27.8) |
6.7 (6.1,7.4) |
5.1 (4.6,5.7) |
2.1 (1.8,2.4) |
2.3 (2.0,2.7) |
1.7 (1.4,2.1) |
1.7 (1.3,2.1) |
| Age group | ||||||||||
| Less than 13 years |
18.0 (16.7,19.4) |
19.9 (18.4,21.4) |
19.7 (18.3,21.1) |
25.4 (23.4,27.5) |
4.7 (4.0,5.6) |
4.7 (3.9,5.5) |
2.0 (1.5,2.8) |
1.9 (1.5,2.3) |
1.9 (1.4,2.4) |
1.9 (1.5,2.3) |
| 13 to 15 years |
18.7 (17.9,19.5) |
17.6 (16.9,18.3) |
20.0 (19.4,20.7) |
23.5 (22.7,24.2) |
7.0 (6.6,7.5) |
6.0 (5.5,6.4) |
1.8 (1.6,2.0) |
2.0 (1.8,2.2) |
1.7 (1.5,1.9) |
1.7 (1.5,2.0) |
| 16 to 17 years |
17.0 (16.0,18.0) |
18.3 (17.4,19.2) |
20.9 (20.1,21.8) |
22.9 (21.8,24.0) |
8.1 (7.5,8.8) |
6.7 (6.1,7.3) |
1.8 (1.5,2.1) |
1.8 (1.5,2.1) |
1.4 (1.2,1.6) |
1.1 (0.9,1.4) |
| 18 or over |
15.3 (13.8,16.9) |
12.3 (10.6,14.2) |
24.1 (22.2,26.2) |
23.9 (22.0,25.9) |
10.0 (8.8,11.3) |
7.4 (6.5,8.4) |
2.0 (1.5,2.6) |
1.8 (1.4,2.3) |
1.8 (1.4,2.5) |
1.3 (0.9,1.9) |
| Type of school | ||||||||||
| Public |
16.2 (15.6,16.9) |
16.0 (15.4,16.5) |
21.4 (20.8,22.0) |
24.9 (24.2,25.5) |
7.8 (7.4,8.2) |
6.6 (6.2,7.0) |
1.9 (1.8,2.1) |
1.9 (1.7,2.1) |
1.7 (1.6,1.9) |
1.6 (1.5,1.8) |
| Private |
27.7 (27.0,28.4) |
28.3 (27.6,29.1) |
15.4 (14.9,16.0) |
15.8 (15.3,16.3) |
3.8 (3.5,4.1) |
3.1 (2.9,3.4) |
1.5 (1.3,1.6) |
2.0 (1.8,2.2) |
1.3 (1.2,1.4) |
1.1 (1.0,1.3) |
| Goods and services score* | ||||||||||
| First tertile |
17.0 (16.3,17.7) |
17.8 (17.2,18.3) |
19.8 (19.3,20.4) |
24.1 (23.4,25.0) |
7.6 (7.1,8.0) |
6.6 (6.2,7.0) |
1.8 (1.6,2.1) |
1.9 (1.7,2.1) |
1.7 (1.5,1.9) |
1.7 (1.5,1.9) |
| Second tertile |
17.3 (16.5,18.1) |
16.4 (15.7,17.2) |
22.3 (21.4,23.2) |
24.5 (23.6,25.4) |
6.8 (6.3,7.3) |
5.9 (5.3,6.4) |
1.9 (1.7,2.2) |
1.9 (1.6,2.2) |
1.7 (1.4,1.9) |
1.4 (1.2,1.6) |
| Third tertile |
26.3 (24.9,27.7) |
22.7 (21.4,24.1) |
18.4 (17.3,19.7) |
16.7 (15.6,17.9) |
6.0 (5.2,6.9) |
3.5 (2.8,4.3) |
1.7 (1.4,2.1) |
2.0 (1.6,2.5) |
1.6 (1.2,2.1) |
1.1 (0.8,1.4) |
95% CI: 95% confidence interval. * The first third refers to the poorest tertile, while the third refers to the richest tertile
The prevalence of regular intake of fresh or minimally processed foods and of ultra-processed foods according to sociodemographic characteristics can be found in the Additional file 1.
Beans showed the highest regular intake, with a prevalence ranging between 50% and 70% among the analytical categories, while regular fruit and vegetable intake was reported by less than a third of the participants. Regular intake of beans was more prevalent among men, especially black (68.4%, 95% CI 66.2, 70.6), Asian (66.7%, 95% CI 62.8, 70.4) and brown men (66.5%, 95% CI 65.1, 67.9), and lower for white women (51.2%, 95% CI 49.7, 52.6). Indigenous women showed a significantly lower prevalence of regular intake of beans (52.8%, 95% CI 48.4, 57.2) compared to white men (61.0%, 95% CI 59.6, 62.3). There was a lower prevalence of regular fruit and vegetable intake by black women (25.1%, 95% CI 23.4, 27.0; 23.5%, 95% CI 21.6, 25.4, respectively), when compared to white men (29.6%, 95% CI 28.5, 30.8; 32.9%, 95% CI 31.8, 34.1, respectively). Although the confidence interval is wide, Indigenous women showed a significantly lower prevalence of regular vegetable intake (24.9%, 95% CI 21.4, 28.8) compared to white men (32.9%, 95% CI 31.8, 34.1) and white women (32.3%, 95% CI 30.9, 33.7) (Table 2).
Table 2.
Distribution of regular food consumption by the intersection of race/skin color and gender. Brazil, 2019
| Analytical categories | Regular food consumption | ||||
|---|---|---|---|---|---|
| Fresh or minimally processed foods | Ultra-processed foods | ||||
| Beans | Fruits | Vegetables | Soft drinks | Sweet treats | |
| % (95% CI) | % (95% CI) | % (95% CI) | % (95% CI) | % (95% CI) | |
| White Man | 61.0 (59.6,62.3) | 29.6 (28.5,30.8) | 32.9 (31.8,34.1) | 19.1 (17.9,20.3) | 27.5 (26.3,28.7) |
| White Woman | 51.2 (49.7,52.6) | 28.2 (27.1,29.4) | 32.3 (30.9,33.7) | 18.0 (17.0,19.0) | 39.4 (37.9,40.9) |
| Brown Man | 66.5 (65.1,67.9) | 27.1 (26.0,28.2) | 29.8 (28.7,30.9) | 16.3 (15.2,17.4) | 25.7 (24.6,26.9) |
| Brown Woman | 55.8 (54.5,57.1) | 27.0 (25.9,28.1) | 27.1 (26.0,28.2) | 15.0 (14.2,15.8) | 36.1 (35.0,37.3) |
| Black Man | 68.4 (66.2,70.6) | 27.4 (25.4,29.5) | 26.2 (24.5,28.1) | 19.4 (17.7,21.2) | 29.9 (28.1,31.7) |
| Black Woman | 59.2 (56.6,61.8) | 25.1 (23.4,27.0) | 23.5 (21.6,25.4) | 17.1 (15.2,19.2) | 38.1 (35.6,40.7) |
| Asian Man | 66.7 (62.8,70.4) | 27.6 (24.2,31.2) | 28.6 (25.1,32.4) | 18.6 (15.3,22.5) | 25.5 (21.9,29.6) |
| Asian Woman | 56.1 (52.3,59.7) | 27.2 (24.3,30.4) | 29.2 (25.9,32.8) | 15.9 (13.0,19.3) | 37.8 (34.6,41.1) |
| Indigenous Man | 65.7 (61.4,69.8) | 29.1 (25.1,33.4) | 27.6 (24.3,31.1) | 17.9 (14.8,21.5) | 24.7 (20.9,28.8) |
| Indigenous Woman | 52.8 (48.4,57.2) | 25.5 (21.9,29.6) | 24.9 (21.4,28.8) | 16.1 (12.2,20.8) | 35.0 (30.9,39.4) |
95% CI: 95% confidence interval
Sweet treats had a higher prevalence of regular intake, in general. The highest prevalence was found for women, especially white (39.4%, 95% CI 37.9, 40.9), black (38.1%, 95% CI 35.6–40.7), and Asian women (37.8%, 95% CI 34.6, 41.1). Although the non-regular intake of soft drinks was more frequent, the prevalence of regular consumption ranged between 15% and 20%, but with no significant difference between the analytical categories (Table 2).
Considering the stratification by the goods and services score, some previously significant differences were no longer observed when individuals within the same gender and race groups were compared to white men, according to score level (below or above the median of the goods and services score, representing lower and higher affluence, respectively). For instance, the prevalence of regular fruit consumption among more affluent black women did not differ from that observed among more affluent white men. It was different from black women overall or among those with lower affluence, for which a significant difference was verified. Nevertheless, despite these changes in statistical significance, the differences in prevalence between white men and the other groups generally followed a consistent pattern, regardless of affluence level (Additional file 2 and Table 2).
Table 3 shows the association between regular intake of fresh or minimally processed foods and the intersection of race/skin color and gender of adolescents. The analytical categories black men (PR 1.12, 95% CI 1.08, 1.17), brown (PR 1.09, 95% CI 1.06, 1.12), Asian (PR 1.09, 95% CI 1.03, 1.15), and Indigenous men (PR 1.07, 95% CI 1.01, 1.15) were associated with higher regular intake of beans in the adjusted models. By contrast, in the adjusted analyses, the analytical categories white women (PR 0.84, 95% CI 0.81, 0.87), Indigenous (PR 0.86, 95% CI 0.78, 0.93), brown (PR 0.91, 95% CI 0.89, 0.94), and Asian women (PR 0.91, 95% CI 0.85, 0.98) were associated with lower regular intake of beans, when compared to white men, in the model 1. In model 2, the results remain consistent, except for a black woman, this analytical category was also associated with lower regular intake of beans (PR 0.94, 95% CI 0.90, 0.98).
Table 3.
Association between regular consumption of fresh or minimally processed foods and analytical categories. Brazil, 2019
| Analytical categories | Regular consumption of fresh or minimally processed food | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Beans | Fruits | Vegetables | |||||||
| Crude Model RP (95% CI) |
Model 1 RP (95% CI) |
Model 2 RP (95% CI) |
Crude Model RP (95% CI) |
Model 1 RP (95% CI) |
Model 2 RP (95% CI) |
Crude Model RP (95% CI) |
Model 1 RP (95% CI) |
Model 2 RP (95% CI) |
|
| White Man | REF | REF | REF | REF | REF | REF | REF | REF | REF |
| White Woman |
0.84 (0.81,0.87) |
0.84 (0.81,0.87) |
0.84 (0.82,0.87) |
0.95 (0.91,1.00) |
0.96 (0.91,1.01) |
0.96 (0.91,1.00) |
0.98 (0.93,1.04) |
0.98 (0.93,1.04) |
0.98 (0.93,1.04) |
| Brown Man |
1.09 (1.06,1.12) |
1.09 (1.06,1.12) |
1.08 (1.05,1.11) |
0.91 (0.87,0.96) |
0.93 (0.88,0.98) |
0.98 (0.93,1.04) |
0.90 (0.86,0.95) |
0.92 (0.87,0.97) |
0.97 (0.92,1.02) |
| Brown Woman |
0.92 (0.89,0.94) |
0.91 (0.88,0.94) |
0.91 (0.89,0.94) |
0.91 (0.87,0.96) |
0.93 (0.89,0.98) |
0.99 (0.94,1.04) |
0.82 (0.78,0.87) |
0.84 (0.79,0.88) |
0.89 (0.84,0.94) |
| Black Man |
1.12 (1.08,1.17) |
1.12 (1.08,1.17) |
1.08 (1.05,1.12) |
0.93 (0.86,1.00) |
0.97 (0.89,1.04) |
1.01 (0.93,1.09) |
0.80 (0.74,0.86) |
0.82 (0.76,0.88) |
0.87 (0.81,0.94) |
| Black Woman |
0.97 (0.93,1.02) |
0.97 (0.93,1.02) |
0.94 (0.90,0.98) |
0.85 (0.78,0.92) |
0.89 (0.81,0.96) |
0.93 (0.85,1.01) |
0.71 (0.65,0.78) |
0.74 (0.68,0.80) |
0.79 (0.72,0.86) |
| Asian Man |
1.09 (1.03,1.16) |
1.09 (1.03,1.15) |
1.08 (1.02,1.14) |
0.93 (0.82,1.06) |
0.95 (0.83,1.08) |
0.99 (0.87,1.12) |
0.87 (0.76,0.99) |
0.88 (0.77,1.01) |
0.93 (0.82,1.06) |
| Asian Woman |
0.92 (0.86,0.98) |
0.91 (0.85,0.98) |
0.91 (0.86,0.97) |
0.92 (0.82,1.03) |
0.93 (0.83,1.04) |
0.97 (0.87,1.09) |
0.89 (0.79,1.00) |
0.90 (0.80,1.01) |
0.95 (0.85,1.07) |
| Indigenous Man |
1.08 (1.01,1.16) |
1.07 (1.00,1.15) |
1.08 (1.02,1.15) |
0.98 (0.84,1.14) |
0.99 (0.85,1.14) |
1.04 (0.90,1.20) |
0.84 (0.73,0.95) |
0.85 (0.74,0.97) |
0.90 (0.79,1.02) |
| Indigenous Woman |
0.87 (0.79,0.94) |
0.86 (0.78,0.93) |
0.85 (0.78,0.93) |
0.86 (0.74,1.00) |
0.86 (0.74,1.00) |
0.91 (0.78,1.06) |
0.76 (0.65,0.88) |
0.77 (0.66,0.90) |
0.82 (0.70,0.96) |
REF: reference; 95% CI: 95% confidence interval; PR: prevalence ratios
Model 1 _ adjusted for individual factors (goods and services score and age group)
Model 2 _ adjusted for individual and contextual factors (score of goods and services, age group, type of school and region)
The results for regular vegetable intake indicate that, compared to white men, all the analytical categories were related to the lower regular intake of vegetables in model 1, except for the categories white women and Asian women and men, which do not show this association. In model 2, the association remained, with lower prevalence of regular vegetable intake for brown women (PR 0.89, 95% CI 0.84, 0.94), black men (PR 0.87, 95% CI 0.81, 0.94), black women (PR 0.79, 95% CI 0.72, 0.86), and Indigenous women (PR 0.82, 95% CI 0.70, 0.96) (Table 3).
Table 4 shows the association between regular intake of ultra-processed foods and the intersection of race/skin color and gender of Brazilian adolescents. With effect attenuation in model 1, the results showed that brown men and brown women were analytical categories associated with a lower regular intake of soft drinks (PR 0.87, 95% CI 0.80, 0.94 and PR 0.81, 95% CI 0.75, 0.87), and in model 2 only the brown women category was associated with a lower prevalence of regular consumption (PR 0.87, 95% CI 0.80, 0.94).
Table 4.
Association between regular consumption of ultra-processed foods and analytical categories. Brazil, 2019
| Analytical categories | Regular consumption of ultra-processed foods | |||||
|---|---|---|---|---|---|---|
| Soft drinks | Sweet treats | |||||
| Crude Model RP (95% CI) |
Model 1 RP (95% CI) |
Model 2 RP (95% CI) |
Crude Model RP (95% CI) |
Model 1 RP (95% CI) |
Model 2 RP (95% CI) |
|
| White Man | REF | REF | REF | REF | REF | REF |
| White Woman |
0.94 (0.88,1.01) |
0.95 (0.88,1.02) |
0.95 (0.88,1.02) |
1.43 (1.35,1.52) |
1.44 (1.36,1.53) |
1.44 (1.36,1.53) |
| Brown Man |
0.85 (0.79,0.92) |
0.87 (0.80,0.94) |
0.92 (0.85,1.00) |
0.94 (0.88,1.00) |
0.95 (0.89,1.01) |
1.00 (0.94,1.07) |
| Brown Woman |
0.79 (0.73,0.85) |
0.81 (0.75,0.87) |
0.87 (0.80,0.94) |
1.31 (1.25,1.38) |
1.33 (1.27,1.40) |
1.42 (1.35,1.49) |
| Black Man |
1.02 (0.92,1.12) |
1.03 (0.94,1.14) |
1.08 (0.98,1.19) |
1.09 (1.02,1.17) |
1.10 (1.02,1.18) |
1.15 (1.07,1.24) |
| Black Woman |
0.90 (0.79,1.02) |
0.93 (0.82,1.06) |
0.97 (0.86,1.11) |
1.39 (1.27,1.51) |
1.41 (1.29,1.54) |
1.48 (1.36,1.62) |
| Asian Man |
0.98 (0.80,1.19) |
1.00 (0.81,1.22) |
1.06 (0.86,1.30) |
0.93 (0.80,1.08) |
0.94 (0.81,1.09) |
0.99 (0.85,1.15) |
| Asian Woman |
0.83 (0.68,1.02) |
0.85 (0.69,1.04) |
0.92 (0.76,1.12) |
1.38 (1.25,1.51) |
1.39 (1.26,1.53) |
1.47 (1.34,1.61) |
| Indigenous Man |
0.94 (0.78,1.12) |
0.96 (0.80,1.14) |
1.02 (0.86,1.22) |
0.90 (0.76,1.06) |
0.91 (0.77,1.08) |
0.96 (0.81,1.14) |
| Indigenous Woman |
0.84 (0.64,1.11) |
0.86 (0.65,1.13) |
0.92 (0.70,1.21) |
1.28 (1.12,1.45) |
1.30 (1.14,1.48) |
1.38 (1.22,1.57) |
REF: reference; 95% CI: 95% confidence interval; PR: prevalence ratios
Model 1 _ adjusted for individual factors (goods and services score and age group)
Model 2 _ adjusted for individual and contextual factors (score of goods and services, age group, type of school and region)
Being a white (PR 1.44, 95% CI 1.36, 1.53), black (PR 1.41, 95% CI 1.29, 1.54), Asian (PR 1.39, 95% CI 1.26, 1.53), brown (PR 1.33, 95% CI 1.27, 1.40) and Indigenous woman (PR 1.30, IC95% CI 1.14, 1.48), and a black man (PR 1.10, IC95% CI 1.02, 1.18) was associated with a higher prevalence of regular intake of sweet treats, compared to white men. The adjusted analyses of model 2 showed similar results to those in model 1. The analysis of model 2 showed that black women had a prevalence 1.48 times higher of regular intake of sweet treats (PR 1.48, 95% CI 1.36, 1.62) when compared to white men (Table 4).
Discussion
Based on representative data of Brazilian adolescent students, the results of the present study indicate differences in the regular intake of fresh or minimally processed foods and ultra-processed foods between the analytical categories derived from the intersection of race/skin color and gender. Therefore, these findings reinforce the importance of this analysis approach.
The comparison between representatives of social identities and association with food consumption indicated that of the five subgroups of fresh or minimally processed and ultra-processed foods, black, Indigenous, and brown women were at a disadvantage in three of them, compared to white men, owing to the lower frequency of regular intake of beans and vegetables and higher intake of sweet treats. White and Asian women experienced disadvantage in two subgroups, because of the lower prevalence of regular intake of beans, but higher prevalence for sweets. Black men showed disadvantage in two owing to the less frequent regular intake of vegetables, but more frequent intake of sweet treats. Brown, Asian, and Indigenous men, compared to white men, showed a higher prevalence of regular bean consumption. Notably, brown women were at an advantage for regular intake of soft drinks in comparison to the other analytical categories.
Studies with adolescents examining how the overlap of gender and race, and consequently the potential sexism and racism, relates to eating habits were not identified. In an attempt to engage with the existing literature, we included studies conducted with adults, despite differences of food consumption among age groups.
For consumption among white, brown, and black individuals, the findings of the present study differ from those of a study [20] based on adults’ data from the Surveillance System of Risk and Protective Factors for Chronic Diseases by Telephone Survey of 2019 (VIGITEL). The survey reported lower intake of fresh or minimally processed foods by black/brown men, white men, and black/brown women; the intake of ultra-processed foods was higher among men, especially among black/brown men, who had a higher prevalence of consumption than the other analytical categories of the study [20]. This difference between the findings may be due to some factors such as: the use of different methods for data collection (telephone survey vs. self-completion instrument), the outcome variable (intake on the previous day vs. regular intake), the design of analytical categories combining black and brown individuals, and the age of individuals (adults vs. adolescents). Age is a variable interrelated with the dynamics of intersectionality, operating together with other factors such as gender and race/skin color [10]. Gender inequalities and/or racial inequalities may correlate differently throughout life and interfere with food intake in different ways.
There were differences in the prevalence of food consumption between white men and women. White women were at a disadvantage in two of the five subgroups (beans and sweet treats) of the study food items. These differences may be due to gender issues, because in a study on health indicators among Brazilian adolescents, it was associated with dieting [28]. A qualitative study, conducted in Canada, formulated hypotheses for gender differences in food consumption of adolescents; for example, interaction with food and food choices to fit in or adapt to a group; body image pressure and pressure to be thin; expectation to prepare one’s own meals; socialization by girls as a priority over eating at lunchtime; girls’ use of food to interact with peers vs. boys’ prioritization of personal identity and of their own choices; body ideals related to strength and height; knowledge of foods as something appreciated by parents, and practice of sports as a priority over time spent on meals [29]. These factors cannot be extrapolated to populations in Brazil, because in addition to the use of a qualitative approach, the application of intersectionality analysis considers the population in a specific locality and context [30]. However, they may point out to research carried out in Brazil to investigate gender relations and food consumption, and may be a possible mechanism for finding greater intake of sweet treats among female adolescents in comparison to male adolescents.
The intersection of power axes, such as sexism and racism, has the potential to impact individuals of the same gender or individuals of the same race/skin color in different ways [10]. This observation may be associated with the differences in the prevalence of regular consumption observed in the study among individuals of the same race/skin color or the same gender. There should be further studies about overlapping gender issues and racial inequalities among adolescents to clarify how these factors would manifest themselves in this population group.
The prevalence of regular intake of fruits and vegetables by brown men and women had intermediate values in comparison to the intake of categories characterized by white and black women and men. Among white and black adolescents, there may be a gradient in food consumption, which confirms the results for the separate analysis of black individuals (black and brown).
In general, white men showed a higher prevalence of regular intake of fresh or minimally processed foods, while the analytical categories representing women and black men showed higher intake of sweet treats. This scenario shows that food consumption of the analytical category representative of the social identity ‘white men’ is more closely based on the advice and recommendations of the Dietary Guidelines for the Brazilian Population in relation to the intake of fresh or minimally processed foods over ultra-processed ones [31]. When comparing the other analytical categories, black and Indigenous women had relevant results for three subgroups of foods analyzed in the present study: lower regular intake of beans and vegetables and higher prevalence of regular intake of sweet treats, when compared to white men, regardless of factors such as goods and services score, age group, type of school, and region.
The existence of an unfavorable food consumption scenario compared to the intake of white men points to violations of a fundamental human right, namely adequate and healthy eating [32], which is related to the dimensions of gender, race, and ethnicity, and must be physically accessible and affordable. Moreover, food consumption must be balanced in terms of quantity and quality [31]. The violation of the right to adequate and healthy food reflects a privileged social position of an identity that is a majority in the spaces of power, because of structural processes of separation and hierarchical power, through which individuals see themselves as different from others – leading to health inequalities over time [33, 34].
Therefore, this scenario of food consumption of adolescents should not be attributed only to their social identities, but to the acceptance of privileges, marginalization, and health conditions, which are unevenly distributed among social identities. Intersectionality aims to give visibility to changeable separations between identity and privilege, institutionalization of racism and discrimination, marginalization and exclusion [14], a fact which justifies the use of the analytical method in the present study.
With a focus on financial access, socioeconomic issues, such as family income, are pointed out as important factors for food purchase [35]. Furthermore, regarding per capita household income, for example, the White population had an average income that was twice as high as that of the Black and Brown populations [23]. It is important to note that White individuals show a lower prevalence of people without formal education (3%) and a higher prevalence of individuals with completed higher education (21.4%) compared to Black and Brown individuals (6.2% and 9%, respectively) [36]. However, there remains an advantage within the class structure due to racial oppression. Thus, even when individuals have access to the same resources—such as class origin and level of education—the outcomes tend to be unfavorable for non-White populations [12]. This was confirmed in the sensitivity analyses stratified by the median of the goods and services score. Although some previously significant differences were no longer observed after stratification, the distribution of prevalences tended to follow a consistent pattern regardless of score level. The loss of statistical significance is likely attributable to reduced sample sizes and wider confidence intervals in certain subgroups resulting from the additional stratification.
When observing data from the perspective of socioeconomic differences based on educational and economic issues, one may fail to understand the sociocultural consequences of slavery in Brazil and scientific racism in the 19th century, factors that resulted in the acceptance of unequal rights, based on the natural superiority and inferiority of social identities [37]. Racism is marked by the exclusion of social rights based on belonging to a group [34]; this vulnerability is an expression of racial inequality, as a structural social determinant, thus affecting food intake outcomes. Moreover, income levels affect gender differently and lower incomes are associated with negative health outcomes in women [28].
As for Indigenous women, it should be noted that the identity known as “Indigenous” was introduced in the Americas during colonialism, just like the “black” identity, to legitimize the domination imposed by Europeans [26]. This attribution of identities disregards the diversity of Indigenous peoples present in the country. The amplitude of confidence intervals can reflect the diversity of these peoples, as well as the reduced number of respondents who self-reported as Indigenous in the survey.
Few articles have addressed the debate on the intersectional categories Indigenous women and men and Asian women and men. In Brazilian surveys, individuals began to be categorized as Asian in the National Household Sample Survey (PNAD) in 1987; moreover, Indigenous people were only categorized separately from brown people in the 1992 edition of PNAD [38], which reflects the lack of a nationwide analysis on the subject. An integrative review of the literature showed that three studies focused on food and nutrition while addressing race/skin color; among these studies, only one included the five categories determined by IBGE [39], while the other two [6, 40] analyzed Asian individuals as associated with Indigenous ones [38], despite the profound differences between these groups.
In addition to the issue of gender and race, differences in fruit and vegetable intake and the small difference in soft drink intake between the analytical categories may be due to access to food, while the price is also an important dimension. There is a historical trend of decreasing the prices of ultra-processed foods and increasing the prices of fresh or minimally processed foods. The price of fresh foods increased continuously between 1995 and 2017 and ultra-processed foods showed a downward trend; these price trends remained from 2018 to 2022, with an estimated price reversal in 2022, owing to the impact of the COVID-19 pandemic. In both studies, soft drinks were among the food subgroups with the lowest price [41, 42]. This may be linked to the non-statistical difference in the prevalences of the analytical categories. Moreover, this price reversal can further harm vulnerable groups, black women, because in addition to issues related to gender and racism, financial access to fresh or minimally processed foods would also be impaired.
There should be further debate on the need for a tax on ultra-processed foods and subsidies for fresh or minimally processed foods, as recommended by the World Health Organization (WHO) [43], given the high intake of ultra-processed foods, their prevalence among adolescents, and the scenario of inequalities in food consumption among the analytical categories. Levying higher taxes on ultra-processed foods can discourage consumption [44], resulting in beneficial effects that can be enhanced by using the collected amounts in public policies and campaigns aimed at each particular gender, thereby reducing racial inequalities in food consumption by decreasing educational disparities and family socioeconomic issues, increasing physical access to food and access to health information and education, with a view to mitigating food-related gender and racial inequalities. Furthermore, advances in the National School Feeding Program (Programa Nacional de Alimentação Escolar – PNAE) [45], a universal Brazilian policy that aligned its recommendations with the Brazilian Dietary Guidelines [31] and the NOVA classification [4, 24], have the potential to contribute to surpassing gender and race inequalities in food consumption among Brazilian adolescents.
One limitation of the present study is the fact that the PeNSE sampling plan is not designed to apply representativeness to race/skin color, thus some social identities may be underrepresented. Additionally, the study does not allow for causal inference, given that it is based on descriptive data. This survey refers to students enrolled and attending classes in elementary and high schools in Brazil; therefore, it is not representative of all Brazilian adolescents. This study did not consider maternal schooling because there was a high rate of non-responses. However, other structural determinants were used, namely the variables race/skin color and gender as social markers of gender and racial inequality, and goods and services score; they could effectively point out social inequalities and their implications on food consumption [8, 9] in the present study.
This is a nationwide study on adolescent students, and notably the first one on food consumption of adolescents to use intersectionality as an analytical method. The use of the inter-categorical analysis of intersectionality is a strong point of the study, given its adaptation of social epidemiology, and the fact that it does not present social identities as risk factors, but rather as representatives of social experiences [46]. The use of this analysis pointed to inequalities in food consumption that would not be identified without categorizing both race and gender. The use of food markers aligned to the NOVA classification [4, 24] and the Brazilian Dietary Guidelines [31] is also a strength of the study, since the results can contribute to advances in the national policy agenda.
Conclusion
Food consumption of Brazilian adolescents differs among the analytical categories of intersectionality race/skin color and gender, with emphasis on disparities in the regular consumption of fresh or minimally processed foods (beans and vegetables) and ultra-processed foods (sweet treats). Moreover, black, and Indigenous women are the analytical categories at the greatest disadvantage, in comparison to white men, as they can be affected by both sexism and racism.
Supplementary information
Acknowledgements
None.
Abbreviations
- IBGE
Brazilian Institute of Geography and Statistics
- PeNSE
Brazil’s National Student Health Survey
- ELSA
Longitudinal Study of Adult Health
- PNAD
National Household Sample Survey
- INEP
National Institute of Educational Studies and Research Anisio Teixeira
- CONEP
National Research Ethics Committee
- PR
Prevalence Ratios
- VIGITEL
Surveillance System of Risk and Protective Factors for Chronic Diseases by Telephone Survey
- WHO
World Health Organization
Author contributions
DC and TR designed the research. TR carried out the data analysis and wrote the final version of the manuscript. DC and FS contributed to the intellectual conception, interpretation and review of the manuscript. All the authors read, critically reviewed and approved the final manuscript.
Funding
This work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Financing Code 001, the Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ process number E-26/201.328/2022) and National Council for Scientific and Technological Development (CNPq processes numbers 311475/2021-3 and 405991/2024-0). All research funders had no role in the conception, analysis or writing of this article. The Article Processing Charge (APC) for the publication was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) (process n. 88887.708598/2022-00).
Data availability
The dataset analyzed in the study is publicly available on the IBGE website, [https://www.ibge.gov.br/estatisticas/sociais/educacao/9134-pesquisa-nacional-de-saude-do-escolar.html?edicao=31442&t=downloads].
Declarations
Ethics approval and consent to participate
The PeNSE carried out in 2019 was approved by the National Research Ethics Committee (Comissão Nacional de Ética em Pesquisa - CONEP) under protocol number 3.249.268, granted on April 8, 2019 [17]. The participants consented to their participation by signing the electronic Informed Consent Form, which was displayed on the first page of the survey. Voluntary participation was emphasized, and participants were assured that they had the right to refuse to take part in the research or to stop the questionnaire at any time. The confidentiality of student information was maintained, and schools were not identified. The consent of parents or guardians was not collected, as according to the research report the Statute of the Child and Adolescent (Law No. 8,069 of July 13, 1990) provides for the autonomy of adolescents to engage in initiatives, such as answering a questionnaire that poses no risk to their health and aims to support the development of public policies for this stage of life course [17].
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Beal T, Morris SS, Tumilowicz A. Global patterns of adolescent fruit, vegetable, carbonated soft drink, and fast-food consumption: a meta-analysis of global school-based student health surveys. Food Nutr Bull. 2019;40(4):444–59. [DOI] [PubMed] [Google Scholar]
- 2.Matias TS, Silva KS, Silva JAD, Mello GTD, Salmon J. Clustering of diet, physical activity and sedentary behavior among Brazilian adolescents in the national school - based health survey (PeNSE 2015). BMC Public Health. 2018;18(1):1283. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Louzada MLDC, Cruz GLD, Silva KAAN, Grassi AGF, Andrade GC, Rauber F, et al. Consumo de alimentos ultraprocessados no Brasil: distribuição e evolução temporal 2008–2018. Rev Saúde Pública. 2023;57(1):12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Monteiro CA, Cannon G, Moubarac JC, Levy RB, Louzada MLC, Jaime PC. The UN Decade of Nutrition, the NOVA food classification and the trouble with ultra-processing. Public Health Nutr. 2018;21(1):5–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Martins BG, Ricardo CZ, Machado PP, Rauber F, Azeredo CM, Levy RB. Fazer refeições com os pais está associado à maior qualidade da alimentação de adolescentes brasileiros. Cad Saúde Pública. 2019;35(7):e00153918. [DOI] [PubMed] [Google Scholar]
- 6.Costa CDS, Flores TR, Wendt A, Neves RG, Assunção MCF, Santos IS. Comportamento sedentário e consumo de alimentos ultraprocessados entre adolescentes brasileiros: Pesquisa Nacional de Saúde do Escolar (PeNSE), 2015. Cad Saúde Pública. 2018;34(3):e00021017. [DOI] [PubMed]
- 7.Maia EG, Silva LESD, Santos MAS, Barufaldi LA, Silva SUD, Claro RM. Padrões alimentares, características sociodemográficas e comportamentais entre adolescentes brasileiros. Rev Bras Epidemiol. 2018;21(suppl 1):e180009. [DOI] [PubMed] [Google Scholar]
- 8.Solar O, Irwin A. A conceptual framework for action on the social determinants of health. Social Determinants of Health Discussion Paper 2 (Policy and Practice). Geneva: World Health Organization; 2010. Available in: https://www.who.int/publications/i/item/9789241500852.
- 9.Moreira RM. A alimentação como direito: um olhar interseccional. In:Silva A, Santos B, editors.Interseccionalidade em alimentação: desafios e conexões entre raça, classe, gênero e comida. Brasília (DF): Fundação Oswaldo Cruz. Observatório Brasileiro de Hábitos Alimentares. 2021:14–20.
- 10.Collins PH, Bilge S, Interseccionalidade. 1ª ed. São Paulo: Boitempo Editorial; 2021.
- 11.Crenshaw K. Documento para o encontro de especialistas em aspectos da discriminação racial relativos ao gênero. Rev Estud Fem. 2002;10(1):171–88. [Google Scholar]
- 12.Gonzalez L. Por um feminismo afro-latino-americano. São Paulo: Schwarcz-Companhia das Letras; 2020. [Google Scholar]
- 13.Hicken MT, Lee H, Hing AK. The weight of racism: Vigilance and racial inequalities in weight-related measures. Soc Sci Med. 2018;199:157–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Shannon G, Morgan R, Zeinali Z, Brady L, Couto MT, Devakumar D, et al. Intersectional insights into racism and health: not just a question of identity. Lancet. 2022;400(10368):2125–36. [DOI] [PubMed] [Google Scholar]
- 15.Santos LA, Ferreira AA, Pérez-Escamilla R, Sabino LL, Oliveira LGD, Salles-Costa R. Interseções de gênero e raça/cor em insegurança alimentar nos domicílios das diferentes regiões do Brasil. Cad Saúde Pública. 2022;38(11):e00130422. [DOI] [PubMed] [Google Scholar]
- 16.Silva SOD, Santos SMCD, Gama CM, Coutinho GR, Santos MEPD, Silva NDJ. A cor e o sexo da fome: análise da insegurança alimentar sob o olhar da interseccionalidade. Cad Saúde Pública. 2022;38(7):e00255621. [DOI] [PubMed] [Google Scholar]
- 17.Rodrigues LS, Miranda NG, Cabrini D. Obesidade e interseccionalidade: análise crítica de narrativas no âmbito das políticas públicas de saúde no Brasil (2004–2021). Cad Saúde Pública. 2023;39(7):e00240322. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Hogan VK, De Araujo EM, Caldwell KL, Gonzalez-Nahm SN, Black KZ. We black women have to kill a lion everyday: An intersectional analysis of racism and social determinants of health in Brazil. Soc Sci Med. 2018;199:96–105. [DOI] [PubMed] [Google Scholar]
- 19.Barber S, Diez Roux AV, Cardoso L, Santos S, Toste V, James S, et al. At the intersection of place, race, and health in Brazil: Residential segregation and cardio-metabolic risk factors in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Soc Sci Med. 2018;199:67–76. [DOI] [PubMed] [Google Scholar]
- 20.Crepaldi BVC, Okada LM, Claro RM, Louzada ML da C, Rezende LFM, Levy RB, et al. 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. [DOI] [PMC free article] [PubMed]
- 21.Instituto Brasileiro de Geografia e Estatística. Pesquisa nacional de saúde do escolar: 2019. Coordenação de População e Indicadores Sociais. Rio de Janeiro: IBGE; 2021. [Google Scholar]
- 22.Instituto Brasileiro de Geografia e Estatística. Pesquisa Nacional de Saúde do Escolar: conceitos e métodos. Rio de Janeiro: IBGE. 2023. Disponível em: https://www.ibge.gov.br/estatisticas/sociais/educacao/9134-pesquisa-nacional-de-saude-do-escolar.html?edicao=31442&t=conceitos-e-metodos. Accessed 5 Apr 2025.
- 23.Instituto Brasileiro de Geografia e Estatística. Desigualdades sociais por cor ou raça no Brasil: estudos e pesquisas. Informação Demográfica e Socioeconômica. 2022;48.
- 24.Monteiro CA, Cannon G, Levy RB, Moubarac JC, Louzada ML, Rauber F, et al. Ultra-processed foods: what they are and how to identify them. Public Health Nutr. 2019;22(5):936–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Levy RB, Castro IRR, Cardoso de, de O L, Tavares LF, Sardinha LMV, Gomes F da S, et al. Consumo e comportamento alimentar entre adolescentes brasileiros: Pesquisa Nacional de Saúde do Escolar (PeNSE), 2009. Ciênc Saúde Coletiva. 2010;15:3085–97. [DOI] [PubMed]
- 26.Quijano A. Colonialidade do poder, Eurocentrismo e América Latina. In: Lander E, editor. A colonialidade do saber: eurocentrismo e ciências sociais: perspectivas latino-americanas. Buenos Aires: Consejo Latinoamericano de Ciencias Sociales; 2005:117–42.
- 27.Bento C. O pacto da branquitude. 1ª ed. São Paulo: Companhia das Letras; 2022. [Google Scholar]
- 28.Buffarini R, Abdalla S, Weber AM, Costa JC, Menezes AMB, Gonçalves H, et al. The Intersectionality of Gender and Wealth in Adolescent Health and Behavioral Outcomes in Brazil: The 1993 Pelotas Birth Cohort. J Adolesc Health. 2020;66(1):S51–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Deslippe AL, Tugault-Lafleur CN, McGaughey T, Naylor PJ, Le Mare PJ, Mâsse L. Gender plays a role in adolescents’ dietary behaviors as they transition to secondary school. Appetite. 2021;167:105642. [DOI] [PubMed] [Google Scholar]
- 30.Kapilashrami A, Hankivsky O. Intersectionality and why it matters to global health. Lancet. 2018;391(10140):2589–91. [DOI] [PubMed] [Google Scholar]
- 31.Brasil. Guia alimentar para a população brasileira. Ministério da Saúde, Secretaria de Atenção à Saúde, Departamento de Atenção Básica. 2nd ed. Brasília (DF): Ministério da Saúde; 2014. [Google Scholar]
- 32.Brasil. Cria o Sistema Nacional de Segurança Alimentar e Nutricional – SISAN com vistas em assegurar o direito humano à alimentação adequada e dá outras providências. Diário Oficial da União; 2006. Lei nº 11.346, de 15 de setembro de 2006.
- 33.Ribeiro D. Pequeno manual antirracista. São Paulo: Companhia das Letras; 2019. [Google Scholar]
- 34.Devakumar D, Selvarajah S, Abubakar I, Kim SS, McKee M, Sabharwal NS, et al. Racism, xenophobia, discrimination, and the determination of health. Lancet. 2022;400(10368):2097–108. [DOI] [PubMed] [Google Scholar]
- 35.Costa JC, Jesus ACDSD, Jesus JGLD, Madruga MF, Souza TN, Louzada MLDC. Diferenças no consumo alimentar da população brasileira por raça/cor da pele em 2017–2018. Rev Saúde Pública. 2023;57(1):4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Instituto Brasileiro de Geografia e Estatística (IBGE). SIDRA – Sistema IBGE de Recuperação Automática: Tabela 7129. Assunto: Pessoas de 14 anos ou mais de idade (%). Disponível em: https://sidra.ibge.gov.br. Acesso em: 19.
- 37.Carneiro S. Racismo, sexismo e desigualdade no Brasil. Selo Negro Edições; 2021.
- 38.Ghiggino LT, Ribeiro EC, de Vieira S, de Alves EV, de Ferreira L. Iniquidades raciais em estudos populacionais de alimentação e nutrição. Res Soc Dev. 2022;11(12):e397111234610–397111234610. [Google Scholar]
- 39.Araújo C, Toral N, Silva ACF, da, Velásquez-Melendez G, Dias AJR. Estado nutricional dos adolescentes e sua relação com variáveis sociodemográficas: Pesquisa Nacional de Saúde do Escolar (PeNSE), 2009. Ciênc Saúde Coletiva. 2010;15:3077–84. [DOI] [PubMed]
- 40.Camelo LDV, Rodrigues JFDC, Giatti L, Barreto SM. Lazer sedentário e consumo de alimentos entre adolescentes brasileiros: Pesquisa Nacional de Saúde do Escolar (PeNSE), 2009. Cad Saúde Pública. 2012;28(11):2155–62. [DOI] [PubMed]
- 41.Maia EG, dos Passos CM, Levy RB, Martins APB, Mais LA, Claro RM. What to expect from the price of healthy and unhealthy foods over time? The case from Brazil. Public Health Nutr. 2020;23(4):579–88. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Andrade GC, Caldeira TCM, Mais LA, Bortoletto Martins AP, Claro RM. Food price trends during the COVID-19 pandemic in Brazil. PLoS ONE. 2024;19(5):e0303777. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.World Health Organization (WHO). Fiscal policies for diet and prevention of noncommunicable diseases: technical meeting report. 5–6 May 2015, Geneva, Switzerland. Geneva: World Health Organization; 2016. https://iris.who.int/handle/10665/250131. [Google Scholar]
- 44.Basto-Abreu A, Torres-Alvarez R, Barrientos-Gutierrez T, Pereda P, Duran AC, et al. Estimated reduction in obesity prevalence and costs of a 20% and 30% ad valorem excise tax to sugar-sweetened beverages in Brazil: a modelling study. PLoS Med. 2024;21(7):e1004399. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Brasil, Resolução. CD/FNDE nº 3, de 4 de fevereiro de 2025. Available in: https://www.gov.br/fnde/pt-br/acesso-a-informacao/legislacao/resolucoes/2025/resolucao-cd_fnde-no-3-de-4-de-fevereiro-de-2025-resolucao-cd_fnde-no-3-de-4-de-fevereiro-de-2025-dou-imprensa-nacional.pdf/view.
- 46.Green MA, Evans CR, Subramanian SV. Can intersectionality theory enrich population health research? Soc Sci Med. 2017;178:214–6. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The dataset analyzed in the study is publicly available on the IBGE website, [https://www.ibge.gov.br/estatisticas/sociais/educacao/9134-pesquisa-nacional-de-saude-do-escolar.html?edicao=31442&t=downloads].