Abstract

Introduction

Adolescence is a period of intense change. Habits and knowledge acquired by teens have an important influence on many aspects of adulthood, affecting diet, health, preferences, and psychosocial development, among others ¹1. World Health Organization. Nutrition in adolescence: issues and challenges for the health sector: issues in adolescent health and development. Geneva: World Health Organization; 2005.. Adolescents’ diet has been characterized by high consumption of ultra-processed foods (high in fats, sugars, and sodium) ²2. Monteiro CA, Moubarac JC, Cannon G, Ng SW, Popkin B. Ultra-processed products are becoming dominant in the global food system. Obes Rev 2013; 14 Suppl 2:21-8. and insufficient consumption of natural foods such as fruits and vegetables ³3. Azeredo CM, Rezende LF, Canella DS, Claro RM, Castro IR, Luiz OC, et al. Dietary intake of Brazilian adolescents. Public Health Nutr 2015; 18:1215-24.^,44. Souza AM, Barufaldi LA, Abreu GA, Giannini DT, Oliveira CL, Santos MM, et al. ERICA: ingestão de macro e micronutrientes em adolescentes brasileiros. Rev Saúde Pública 2016; 50 Suppl 1:5s..

Inadequate diet in childhood and adolescence is an important risk factor for obesity and other chronic noncommunicable diseases (NCDs) such as cardiovascular diseases, diabetes, and cancer ⁵5. World Health Organization. Diet, nutrition and the prevention of chronic diseases. Geneva: World Health Organization; 2003.^,66. Dauchet L, Amouyel P, Hercberg S, Dallongeville J. Fruit and vegetable consumption and risk of coronary heart disease: a meta-analysis of cohort studies. J Nutr 2006; 136:2588-93.. The identification of adolescents’ dietary habits is thus highly relevant for public health, in order to promote healthy eating in this group and thus reduce the risk of obesity and other NCDs.

However, research has shown that obesity and NCDs do not result from the consumption of single food groups, but from inadequate dietary patterns. Thus, the association between diet and health in adolescents should not consider only the presence or absence of certain nutrients, but of the foods consumed as a whole ⁷7. Nobre LN, Lamounier JA, Franceschini SC. Preschool children dietary patterns and associated factors. J Pedriatr (Rio J) 2012; 88:129-36.. In this context, robust analytical approaches have been used to identify dietary patterns ⁸8. Tavares LF, Castro IR, Levy RB, Cardoso LO, Claro RM. Dietary patterns of Brazilian adolescents: results of the Brazilian National School-Based Health Survey (PeNSE). Cad Saúde Pública 2014; 30:2679-90.^,99. Mu M, Xu LF, Hu D, Wu J, Bai MJ. Dietary patterns and overweight/obesity: a review article. Iran J Public Health 2017; 46:869-76., allowing a broader representation of how foods are consumed and assessing the relationship between these dietary patterns and sociodemographic and behavioral characteristics and risk factors for NCDs.

Another important question when analyzing a country of continental dimensions like Brazil is the differences in dietary habits according to geographic region. Studies that assessed the consumption of food markers have already identified such differences, for example in the consumption of beans, sodas, and vegetables ⁴4. Souza AM, Barufaldi LA, Abreu GA, Giannini DT, Oliveira CL, Santos MM, et al. ERICA: ingestão de macro e micronutrientes em adolescentes brasileiros. Rev Saúde Pública 2016; 50 Suppl 1:5s.^,1010. Velásquez-Meléndez G, Mendes LL, Pessoa MC, Sardinha LM, Yokota RT, Bernal RT, et al. Tendências da frequência do consumo de feijão por meio de inquérito telefônico nas capitais brasileiras, 2006 a 2009. Ciênc Saúde Colet 2012; 17:3363-70..

In the last five years, it has been witnessed a trend in the use of methodologies for assessing food consumption in Brazilian children and adolescents based on the identification of dietary patterns ¹¹11. Biazzi Leal D, Altenburg de Assis MA, Hinnig PF, Schmitt J, Soares Lobo A, Bellisle F, et al. Changes in dietary patterns from childhood to adolescence and associated body adiposity status. Nutrients 2017; 9:E1098.^,1212. Oliveira Santos R, Vieira DADS, Miranda AAM, Fisberg RM, Marchioni DM, Baltar VT. The traditional lunch pattern is inversely correlated with body mass index in a population-based study in Brazil. BMC Public Health 2018; 18:33.^,1313. Meller FO, Assunção MC, Schäfer AA, Loret de Mola C, Dahly DL, Vaz JS, et al. Is the number of siblings associated with dietary patterns in adolescents? The 1993 birth cohort of Pelotas (Brazil). PLoS One 2017; 12:e0174087.^,1414. Sena EMS, Muraro AP, Rodrigues PRM, Fiuza RFP, Ferreira MG. Risk behavior patterns for chronic diseases and associated factors among adolescents. Nutr Hosp 2017; 34:914-22.^,1515. Pinho MDM, Adami F, Benedet J, Vasconcelos FAG. Association between screen time and dietary patterns and overweight/obesity among adolescents. Rev Nutr 2017; 30:377-89.^,1616. Pinho L, Silveira MF, Botelho ACC, Caldeira AP. Identification of dietary patterns of adolescents attending public schools. J Pediatr (Rio J) 2014; 90:267-72.^,1717. Mascarenhas JMO, Silva RCR, Assis AMO, Santana MLP, Moraes LTLP, Barreto ML. Identification of food intake patterns and associated factors in teenagers. Rev Nutr 2014; 27:45-54.^,1818. Villa JKD, Santos TSS, Ribeiro AQ, Silva AR, Sant'Ana LFR, Pessoa MC. Padrões alimentares de crianças e determinantes socioeconômicos, comportamentais e maternos. Rev Paul Pediatr 2015; 33:302-9.^,1919. Silva DFO, Lyra CO, Lima SCVC. Padrões alimentares de adolescentes e associação com fatores de risco cardiovascular: uma revisão sistemática. Ciênc Saúde Colet 2016; 21:1181-96.^,2020. Vieira DA, Castro MA, Fisberg M, Fisberg RM. Nutritional quality of dietary patterns of children: are there differences inside and outside school? J Pediatr (Rio J.) 2017; 93:47-57.^,11. World Health Organization. Nutrition in adolescence: issues and challenges for the health sector: issues in adolescent health and development. Geneva: World Health Organization; 2005.^,2121. Cunha DB, Bezerra IN, Pereira RA, Sichieri R. At-home and away-from-home dietary patterns and BMI z-scores in Brazilian adolescents. Appetite 2018; 120:374-80.^,2222. Ribeiro-Silva RC, Fiaccone RL, Conceicao-Machado MEPD, Ruiz AS, Barreto ML, Santana MLP. Body image dissatisfaction and dietary patterns according to nutritional status in adolescents. J Pediatr (Rio J) 2018; 94:155-61.. However, such studies involve localized samples, denoting a gap in studies with nationally representative probabilistic samples of Brazilian adolescents according to geographic region.

Considering the importance of assessing dietary patterns that reflect the adolescents’ overall diet, besides considering regional differences in Brazilians’ eating habits, the current study aimed to identify the dietary patterns of Brazilian adolescents according to geographic region and to verify differences by age, sex, and type of school.

Methods

Study design, sample, and data collection

Data were obtained from the Study of Cardiovascular Risk in Adolescents (ERICA), conducted in 2013-2014. ERICA was a nationwide, multicenter, school-based survey aimed at estimating the prevalence of cardiovascular risk factors and metabolic syndrome in adolescents 12 to 17 years of age, enrolled in public and private schools. The study population was allocated across 32 geographic strata consisting of the 27 Brazilian state capitals and five sets of municipalities with more than 100 thousand inhabitants in each of the country’s five major geographic regions. Schools were selected in each stratum with probabilities proportional to the number of students enrolled in the seventh, eighth, and ninth grades of primary school and the first, second, and third grades of middle school, and inversely proportional to the distance between the school’s municipality and the state capital. In these schools, three combinations of grade and shift (morning and afternoon) were selected. In each combination, a class was selected, and all of its students were invited to participate in the study. In all, 1,251 schools were selected in 124 municipalities, out of a total of 273 municipalities with more than 100,000 inhabitants. The sample was probabilistic and representative of the country, the five regions, and the state capitals ²³23. Bloch KV, Klein CH, Szklo M, Kuschnir MC, Abreu GA, Barufaldi LA, et al. ERICA: prevalences of hypertension and obesity in Brazilian adolescents. Rev Saúde Pública 2016; 50 (Suppl 1):9s.^,2424. Vasconcellos MTL, Silva PLN, Szklo M, Kuschnir MCC, Klein CH, Abreu GA, et al. Sampling design for the Study of Cardiovascular Risks in Adolescents (ERICA). Cad Saúde Pública 2015; 31:921-30.^,2525. Bloch KV, Szklo M, Kuschnir MC, Abreu GA, Barufaldi LA, Klein CH, et al. The Study of Cardiovascular Risk in Adolescents - ERICA: rationale, design and sample characteristics of a national survey examining cardiovascular risk factor profile in Brazilian adolescents. BMC Public Health 2015; 15:94..

Participation in the data collection included all the eligible adolescents that signed the consent form. The study excluded pregnant adolescents and individuals with physical or mental disabilities. Details on the study protocol, design, and sampling have been published elsewhere ²³23. Bloch KV, Klein CH, Szklo M, Kuschnir MC, Abreu GA, Barufaldi LA, et al. ERICA: prevalences of hypertension and obesity in Brazilian adolescents. Rev Saúde Pública 2016; 50 (Suppl 1):9s.^,2424. Vasconcellos MTL, Silva PLN, Szklo M, Kuschnir MCC, Klein CH, Abreu GA, et al. Sampling design for the Study of Cardiovascular Risks in Adolescents (ERICA). Cad Saúde Pública 2015; 31:921-30.^,2525. Bloch KV, Szklo M, Kuschnir MC, Abreu GA, Barufaldi LA, Klein CH, et al. The Study of Cardiovascular Risk in Adolescents - ERICA: rationale, design and sample characteristics of a national survey examining cardiovascular risk factor profile in Brazilian adolescents. BMC Public Health 2015; 15:94..

Sociodemographic data were collected with the adolescent’s questionnaire. This instrument was self-administered with an electronic data collector, a PDA (personal digital assistant) model LG GM750Q (LG Electronics, Seoul, Korea). The questionnaire was used to obtain information on age, sex, type of school administration (public or private), and others. Adolescents reported on frequency and duration of certain physical activities in the previous seven days. The variables sex, type of school (public or private), geographic region (North, Northeast, Central, Southeast, and South), and age (12-14 and 15-17 years) were analyzed categorically, and physical activity (minutes/week) was analyzed as a continuous variable.

Anthropometric measurements were recorded with the adolescents wearing light clothing, barefoot, and in orthostatic position. Weight was measured with a digital scale (model P150m Líder, São Paulo, Brazil) with a capacity of 200kg and accurate to 50g. Height was measured with a calibrated stadiometer (Alturexata, Minas Gerais, Brazil), with a maximum height of 213 cm and calibrated in millimeters. The anthropometric measurements were used to calculate body mass index (BMI = weight/height²), and classified by the cutoff points for sex and age published by the World Health Organization (WHO) ²⁶26. de Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ 2007; 85:660-7.. BMI was categorized as: underweight (including very low weight and low weight), normal weight, and excess weight (including overweight and obesity).

A 24-hour food recall (24hR) was applied to each adolescent using an individual face-to-face interview, performed by previously trained field interviewers. The multiple-pass method ²⁷27. Conway JM, Ingwersen LA, Vinyard BT, Moshfegh AJ. Effectiveness of the US Department of Agriculture 5-step multiple-pass method in assessing food intake in obese and nonobese women. Am J Clin Nutr 2003; 77:1171-8. was applied during the interviews, which organizes the 24hR in five steps to reduce underreporting of food consumption. The 24hR was completed directly on netbooks with a specific software for entering food consumption data, the ERICA-REC24h ²⁸28. Barufaldi LA, Abreu GA, Veiga GV, Sichieri R, Kuschnir MC, Cunha DB, et al. Software to record 24-hour food recall: application in the Study of Cardiovascular Risks in Adolescents. Rev Bras Epidemiol 2016; 19:464-8.. This software contained a list of foods developed from a database on food and beverage purchases from the Brazilian Household Budgets Survey (POF) 2008-2009, conducted by the Brazilian Institute of Geography and Statistics ²⁸28. Barufaldi LA, Abreu GA, Veiga GV, Sichieri R, Kuschnir MC, Cunha DB, et al. Software to record 24-hour food recall: application in the Study of Cardiovascular Risks in Adolescents. Rev Bras Epidemiol 2016; 19:464-8.^,2929. Instituto Brasileiro de Geografia e Estatística. Pesquisa de Orçamentos Familiares 2008-2009: análise do consumo alimentar pessoal no Brasil. Rio de Janeiro: Instituto Brasileiro de Geografia e Estatística; 2011.. The interviewers used photographs in the software to help estimate the size of portions. Foods reported by adolescents that were not in the ERICA-REC24h database were keyed in by the interviewers.

Application of the 24hR produced a databank with 1,128 foods. Due to the large volume of food consumption data, we opted to group these foods according to the similarity of macronutrients, creating food groups with similar nutritional composition. This procedure is often used in the specific literature ³⁰30. Gutierrez-Pliego LE, Camarillo-Romero ES, Montenegro-Morales LP, Garduño-García JJ. Dietary patterns associated with body mass index (BMI) and lifestyle in Mexican adolescents. BMC Public Health 2016; 16:850.^,3131. Borges CA, Marchioni DML, Levy RB, Slater B. Dietary patterns associated with overweight among Brazilian adolescents. Appetite 2018; 123:402-9. and was based on a grouping performed in a previous publication ⁴4. Souza AM, Barufaldi LA, Abreu GA, Giannini DT, Oliveira CL, Santos MM, et al. ERICA: ingestão de macro e micronutrientes em adolescentes brasileiros. Rev Saúde Pública 2016; 50 Suppl 1:5s. which also used the food consumption data from ERICA. The reported foods were categorized in 19 groups (Box 1), and final consumption was estimated in grams for each of the food groups. Energy intake was estimated by the Table of Nutritional Composition of Foods Consumed in Brazil³²32. Instituto Brasileiro de Geografia e Estatística. Pesquisa de orçamentos familiares 2008-2009: tabela de composição nutricional dos alimentos consumidos no Brasil. Rio de Janeiro: Instituto Brasileiro de Geografia e Estatística; 2011..

From the total of 102,327 eligible adolescents, 71,740 (70.1%) presented complete information from the adolescent’s questionnaire, anthropometry, and 24hR. The current study excluded adolescents with total daily energy intake less than 400Kcal or greater than 8,000Kcal ³³33. Zhang J, Wang H, Wang Y, Xue H, Wang Z, Du W, et al. Dietary patterns and their associations with childhood obesity in China. Br J Nutr 2015; 113:1978-84. (388 individuals), leaving a final sample of 71,298 adolescents (69.7% of those eligible). The sociodemographic characteristics of participating and non-participating adolescents were described in a previous publication; briefly, the percentage of male adolescents and older adolescents was slightly higher in nonparticipants. Among nonparticipants with incomplete data, but with anthropometric measurements (8.5% of the nonparticipants), mean BMI was slightly higher than that of participants. The point estimates obtained with the analytical strategy used for a complex sample showed that the study participants are also representative of the nonparticipants ³⁴34. Silva TLN, Klein CH, Souza AM, Barufaldi LA, Abreu GA, Kuschnir MC, et al. Participação no Estudo de Riscos Cardiovaculares em Adolescentes - ERICA. Rev Saúde Pública 2016; 50 Suppl 1:3s..

Statistical analyses

Dietary patterns were identified by factor analysis, using estimation by principal components analysis ³⁵35. Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol 2002; 13:3-9., and the factors obtained were rotated by varimax orthogonal rotation. To test the applicability of factor analysis to food consumption data, the Bartlett and Kaiser-Meyer-Olkin (KMO) sphericity tests were applied ³⁶36. Gleason PM, Boushey CJ, Harris JE, Zoellner J. Publishing nutrition research: a review of multivariate techniques - part 3: data reduction methods. J Acad Nutr Diet 2015; 115:1072-82.. The criteria used for retention of factors were eigenvalues > 1, and the best interpretability of factors was used for the final decision. Food groups with a factor load greater than or equal to +0.3 and less than or equal to -0.3 were considered important components of the dietary pattern. Analyses of dietary patterns were performed separately for each of Brazil’s major geographic regions (North, Northeast, Central, Southeast, and South). Finally, a factor score was calculated for each adolescent in each of the dietary patterns using the predict command.

Analysis of the association between the adolescents’ characteristics (independent characteristics) and the factor scores of each region’s dietary patterns (dependent variables) used linear regression analysis stratified by age group (12-14 and 15-17 years), representing primary school and middle school students, respectively. The models were adjusted a priori for nutritional status (underweight, normal weight, and excess weight), physical activity (minutes/week), and total energy intake (Kcal). Adjustment of the analyses of association by the nutritional status and physical activity variables was performed as a function of their direct influence on individual food consumption levels and by the fact that they were also associated with the target exposures, sex and socioeconomic status (type of school administration). Adjustment by total energy intake is also recommended in epidemiological studies to control for “confounding”, as discussed in Willet et al. ³⁷37. Willett WC, Howe GR, Kushi LH. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr 1997; 65(4 Suppl):1220S-8S.. The descriptive, factor, and linear regression analyses used Stata version 13.1 (https://www.stata.com) and considered the complex sampling design.

Ethical aspects

The study complied with the ethical principles laid out in the Declaration of Helsinki and was approved by the Institutional Review Board of the Institute of Public Health Studies of the Federal University of Rio de Janeiro (IESC/UFRJ), case review n. 01/2009, protocol no. 45/2008. The study was also approved by research ethics committees in each state of Brazil.

Results

Table 1 shows the participants’ characteristics. The majority of the adolescents were 15-17 years of age (54.1%), studied in public schools (78.5%), and had normal weight (72.5%). Excess weight was found in 24.5% of the sample. The median values for physical activity and energy consumption were 300 minutes/week and 2,134Kcal, respectively.

A total of 16 dietary patterns were identified, four of which in the North and three in each one of the other four regions of the country. The first pattern in the North consisted of the food groups meat, rice, and beans. The second consisted of bread, coffee, oils, and fats. The third consisted of sugary beverages, snacks, pasta, sweets and desserts, and cake and cookies. The fourth dietary pattern in the North of Brazil consisted of roots/tubercles, fruits and vegetables, chicken, fish, seafood, and eggs. The dietary patterns in the North explained 29.5% of the variability in food consumption data and were named as follows: “traditional”, “bread-and-coffee”, “unhealthy”, and “traditional-North”, respectively (Table 2).

The first pattern in the Northeast consisted of bread, coffee, oil and fat, processed meat, and corn. The second consisted of sugary beverages, snacks, pasta, cake and cookies, and sweets and desserts. The third dietary pattern consisted of rice, beans, meat, and roots/tubercles. The dietary patterns in the Northeast explained 23% of the variability in the food consumption data and were named as: “bread-and-coffee”, “unhealthy”, and “traditional”, respectively (Table 2).

The first pattern in the Southeast consisted of rice, beans, and meat and showed a negative factor load for pasta. The second consisted of bread, oil and fat, coffee, and pasta. The third dietary pattern consisted of sugary beverages, snacks, sweets and desserts, milk and cheese, and cake and cookies. The dietary patterns in the Southeast explained 24.3% of the variability in the food consumption data and were named as: “traditional”, “bread-and-coffee”, and “unhealthy”, respectively (Table 2).

The first pattern in the South consisted of rice, beans, and meat. The second consisted of bread, oil and fat, processed meat, milk, cheese, and coffee. The third dietary pattern consisted of sugary beverages, snacks, cake and cookies, and sweets and desserts. The dietary patterns in the South explained 24.6% of the variability in food consumption data and were named as: “traditional”, “bread-and-coffee”, and “unhealthy”, respectively (Table 2).

The first pattern in the Central consisted of rice, beans, and meat. The second consisted of bread, oil and fat, milk and cheese, processed meat, chicken, and eggs. The third dietary pattern consisted of sugary beverages, snacks, sweets and desserts, and cake and cookies. The dietary patterns in the Central explained 23.2% of the variability in the food consumption data and were named as: “traditional, “bread-and-coffee”, and “unhealthy”, respectively (Table 2).

No major differences were observed in dietary patterns by geographic region, and in most of the regions the patterns were traditional, bread-and-coffee, and unhealthy, with the exception of the Northeast, where the order was different, namely, the first pattern was bread-and-coffee and the last was traditional. A fourth pattern was identified in the North, consisting of traditional regional foods.

Tables 3 and 4 presents the results of the associations between the factor scores for dietary patterns and the variables sex and type of school, according to the age groups 12-14 and 15-17 years, respectively. In the North, independently of age group, male adolescents showed greater adherence to the traditional and bread-and-coffee pattern and less adherence to the unhealthy pattern. No significant associations were found between sex and type of school and the traditional-North pattern (Tables 3 and 4).

In the Northeast, the results were similar for the two age groups. Boys showed greater adherence to the bread-and-coffee and traditional patterns and lower adherence to the unhealthy pattern. Private school students showed less adherence to the bread-and-coffee and traditional patterns greater adherence to the unhealthy pattern (Tables 3 and 4).

In the Southeast, adolescents from both age groups and boys showed greater adherence to the traditional and bread-and-coffee patterns and less adherence to the unhealthy pattern. Meanwhile, private school students, also in both age groups, showed lower adherence to the traditional and bread-and-coffee patterns and greater adherence to the unhealthy pattern. In the South, boys showed greater adherence to the traditional and bread-and-coffee patterns, independently of age group. In relation to the unhealthy pattern, lower adherence was seen in the younger adolescent boys (12-14 years) and greater adherence in private school students, independently of age (Tables 3 and 4).

In the Central, boys in both age groups showed greater adherence to the traditional pattern, and younger boys showed lower adherence to the unhealthy pattern. Private school students showed lower adherence to the traditional pattern and greater adherence to the unhealthy pattern. Private school students showed greater adherence to the bread-and-coffee pattern, but only among adolescents 15 to 17 years of age (Tables 3 and 4).

In general, the results showed that in all five regions of Brazil, when compared to their female peers, male adolescents were more likely to adhere to the dietary pattern characterized by the consumption mainly of rice, beans, and meat, while adhering less to the pattern characterized by the consumption of sugary beverages, snacks, cake and cookies, and sweets and desserts. Meanwhile, private school students adhered more to the pattern characterized by the consumption of unhealthy foods and less to the pattern characterized by rice, beans, and meat (Tables 3 and 4).

Discussion

The current study identified adolescents’ dietary patterns in each of Brazil’s five major geographic regions and found some differences in the number and order of patterns and in the foods comprising the patterns. In most of the regions, the patterns’ order was: traditional, bread-and-coffee, and unhealthy, with the exception of Northeast Brazil, where the order changed, namely, the first pattern was bread-and-coffee and the last was traditional. A fourth pattern was identified in the North of Brazil, characterized by traditional regional foods, and called the traditional-North pattern. The results also showed that male adolescents adhered more to the traditional pattern and less to the unhealthy pattern, when compared to female adolescents. Private school students showed the opposite, with greater adherence to the unhealthy pattern and lower adherence to the traditional pattern.

Generally speaking, in relation to the patterns identified in this study, the traditional pattern was characteristic of a healthier diet, the bread-and-coffee pattern as intermediate, and the unhealthy pattern characterized by inadequate food choices. Thus, considering the patterns’ order, the Northeast showed the least healthy food consumption, since the traditional pattern was first in the other four regions but last in the Northeast. Importantly, however, with the exception of the North, no pattern was identified that was characterized by the consumption of fruits and vegetables.

A study with data from the National School Health Survey (PeNSE) of 2009, with 9^th grade students from Brazil’s state capitals and the Federal District (Brasilia), identified three dietary patterns: healthy, unhealthy, and mixed. Comparisons with the study’s results are limited, since the patterns were identified with a different methodology (cluster analysis) and based on grouping of the foods in markers of healthy and unhealthy diet. However, the study found differences in the patterns’ prevalence rates both within and between regions. Higher proportions of the healthy pattern were observed in adolescents in the state capitals of the Southeast, South, and Central of Brazil ⁸8. Tavares LF, Castro IR, Levy RB, Cardoso LO, Claro RM. Dietary patterns of Brazilian adolescents: results of the Brazilian National School-Based Health Survey (PeNSE). Cad Saúde Pública 2014; 30:2679-90..

Identification of the traditional pattern as the most frequent in four of the five regions is consistent with the results of another study performed with data from ERICA, which assessed the foods most consumed by Brazilian adolescents. The study identified rice and beans among the most frequently consumed foods ⁴4. Souza AM, Barufaldi LA, Abreu GA, Giannini DT, Oliveira CL, Santos MM, et al. ERICA: ingestão de macro e micronutrientes em adolescentes brasileiros. Rev Saúde Pública 2016; 50 Suppl 1:5s.. The dietary patterns identified in our study are also similar to those reported by Cunha et al. ²¹21. Cunha DB, Bezerra IN, Pereira RA, Sichieri R. At-home and away-from-home dietary patterns and BMI z-scores in Brazilian adolescents. Appetite 2018; 120:374-80., who also used factor analysis to identify dietary patterns in Brazilian adolescents and found a pattern characterized by traditional Brazilian foods, another marked by the consumption of bread-and-coffee, and a third pattern characterized by unhealthy foods.

We identified a fourth dietary pattern in the North of Brazil, called the traditional-North pattern, characterized by typical regional foods such as root/tubercles, fruits and vegetables, and fish and seafood. Food consumption by Brazilian adolescents has been marked increasingly by greater consumption of ultra-processed foods and a drop in the consumption of fruits and vegetables ²2. Monteiro CA, Moubarac JC, Cannon G, Ng SW, Popkin B. Ultra-processed products are becoming dominant in the global food system. Obes Rev 2013; 14 Suppl 2:21-8.^,33. Azeredo CM, Rezende LF, Canella DS, Claro RM, Castro IR, Luiz OC, et al. Dietary intake of Brazilian adolescents. Public Health Nutr 2015; 18:1215-24.^,44. Souza AM, Barufaldi LA, Abreu GA, Giannini DT, Oliveira CL, Santos MM, et al. ERICA: ingestão de macro e micronutrientes em adolescentes brasileiros. Rev Saúde Pública 2016; 50 Suppl 1:5s.; however, adolescents in the North of Brazil are apparently those that still tend to maintain their typical regional diet. Souza et al. ³⁸38. Souza AM, Pereira RA, Yokoo EM, Levy RB, Sichieri R. Alimentos mais consumidos no Brasil: Inquérito Nacional de Alimentação 2008-2009. Rev Saúde Pública 2013; 47 Suppl 1:190s-9s. also identified a high prevalence of consumption of cassava flour, fresh fish, and fish-based dishes in the North of Brazil.

One characteristic of the North of Brazil, also part of the Amazon Region, is the availability of various highly nutritious fruits. The Amazon has as many fruit species as all of the rest of the Americas ³⁹39. Clement CR, Junqueira AB, Araujo MC, Yuyama LKO, Yuyama K. Frutas - à espera de mercados. In: Capozzoli U, organizador. Scientific American Brasil. São Paulo: Duetto Editorial; 2008. p. 36-43. (Coleção Amazônia - Tesouros).. In addition to fruits, the Amazon Region is bathed by dozens of rivers with diverse fish species containing high-quality protein and high in polyunsaturated fats ⁴⁰40. Rocha YRd, Aguiar JPL, Marinho HA, Shrimpton R. Aspectos nutritivos de alguns peixes da Amazônia. Acta Amaz 1982; 12:787-94.. Thus, in addition to the supply of fruits, Northern Brazil is characterized primarily by high consumption of fish, whole flours, and starches, exceeding by several times the average Brazilian consumption. This has even been described as the staple diet of the rural Amazonian population ⁴¹41. Batista VS, Inhamuns AJ, Freitas CEC, Freire-Brasil D. Characterization of the fishery in river communities in the low-Solimões/high-Amazon region. Fish Manag Ecol 1998; 5:419-35.^,4242. Murrieta R, Dufour DL. Fish and farinha: protein and energy consumption in Amazonian rural communities on Ituqui Island, Brasil. Ecol Food Nutr 2004; 43:231-55.^,4343. Murrieta RSS, Bakri MS, Adams C, Oliveira PSS, Strumpf R. Consumo alimentar e ecologia de populações ribeirinhas em dois ecossistemas amazônicos: um estudo comparativo. Rev Nutr (Campinas) 2008; 21:123s-33s.. However, the current study was performed in urban areas of municipalities in the North.

The Northeast of Brazil was the only region with the “corn” food group (corn, cornmeal, polenta, and other corn-based dishes) in the bread-and-coffee dietary pattern. This food group’s presence can be explained by the consumption of typical regional dishes containing corn as a staple ingredient. Coelho & Gubert ⁴⁴44. Coelho SEAC, Gubert MB. Insegurança alimentar e sua associação com consumo de alimentos regionais brasileiros. Rev Nutr 2015; 28:555-67. identified high consumption of corn couscous among adolescents in Northeast Brazil. The region also has a historically large corn crop, which may be reflected in the population’s food consumption ⁴⁵45. Carvalho HWL, Cardoso MJ, Leal MLS, Santos MX, Tabosa JN, Souza EM. Adaptabilidade e estabilidade de cultivares de milho no Nordeste brasileiro. Pesq Agropec Bras 2005; 40:471-7.. The high consumption of tapioca, pirão (thick cassava gravy), and cassava flour in this region ⁴⁴44. Coelho SEAC, Gubert MB. Insegurança alimentar e sua associação com consumo de alimentos regionais brasileiros. Rev Nutr 2015; 28:555-67. may also explain the presence of the roots/tubercles group in the traditional pattern in the Northeast. The South and Central regions showed the milk and cheese group in the bread-and-coffee pattern, and this food group was present in the unhealthy pattern in the Southeast. The consumption of this food group may be explained by the extensive production and processing of milk and dairy products, characteristic of these regions of Brazil ⁴⁶46. Instituto Brasileiro de Geografia e Estatística. Indicadores IBGE: estatística da produção pecuária. Rio de Janeiro: Instituto Brasileiro de Geografia e Estatística; 2006..

The dietary patterns identified in Brazilian adolescents have certain characteristics that attest to specificities in regional dietary habits, aspects that were already identified in the mid-1940s by Josué de Castro in The Geography of Hunger⁴⁷47. Castro J. Geografia da fome: o dilema brasileiro, pão ou aço. Rio de Janeiro: Civilização Brasileira; 2001., and that appear to have been preserved to this day, despite the profound changes in the profile of food availability and food consumption in the Brazilian population in the last seventy years ⁴⁸48. Vasconcelos FAG. Josué de Castro e a Geografia da Fome no Brasil. Cad Saúde Pública 2008; 24:2710-7.. They also preserve characteristics of the indicators of household food availability observed in the POF 2008-2009 ⁴⁹49. Instituto Brasileiro de Geografia e Estatística. Pesquisa de Orçamentos Familiares 2008-2009: avaliação nutricional da disponibilidade domiciliar de alimentos no Brasil. Rio de Janeiro: Instituto Brasileiro de Geografia e Estatística; 2010.^,5050. Instituto Brasileiro de Geografia e Estatística. Pesquisa de Orçamentos Familiares 2008-2009: aquisição alimentar domiciliar per capita - Brasil e grandes regiões. Rio de Janeiro: Instituto Brasileiro de Geografia e Estatística; 2010., which generated several publications pointing to specificities in regional Brazilian food habits.

According to the current study’s results, male adolescents appear to have a healthier dietary pattern, since they show greater adherence to the traditional pattern and lower adherence to the unhealthy pattern, compared to female adolescents. Similar results were found in a study of adolescent students in public schools in Bahia, where the “healthy” pattern was inversely associated with female adolescents ¹⁷17. Mascarenhas JMO, Silva RCR, Assis AMO, Santana MLP, Moraes LTLP, Barreto ML. Identification of food intake patterns and associated factors in teenagers. Rev Nutr 2014; 27:45-54..

Since no other study was found that related dietary pattern to type of school, we compared our results to those of studies that assessed the association between the adolescents’ dietary pattern and socioeconomic characteristics, since type of school (public versus private) can be considered a proxy for socioeconomic status in Brazil ⁵¹51. Peixoto MCL, Aranha AVS. Universidade pública e inclusão social: experiência e imaginação. Belo Horizonte: Editora da UFMG; 2008.. In the current study, adolescents enrolled in private schools showed greater adherence to the pattern characterized by consumption of unhealthy foods and lower adherence to the traditional pattern, while in México the Western dietary pattern was positively associated with the adolescents’ level of schooling and housing quality index ⁵²52. Lozada AL, Flores M, Rodríguez S, Barquera S. Patrones dietarios en adolescentes mexicanas. una comparación de dos métodos. Encuesta Nacional de Nutrición, 1999. Salud Pública Méx 2007; 49:263-73.. On the other hand, the same pattern was inversely associated with income in Australia ⁵³53. Ambrosini GL, Oddy WH, Robinson M, O'Sullivan TA, Hands BP, Klerk NH, et al. Adolescent dietary patterns are associated with lifestyle and family psycho-social factors. Public Health Nutr 2009; 12:1807-15., socioeconomic status in Germany ⁵⁴54. Richter A, Heidemann C, Schulze MB, Roosen J, Thiele S, Mensink GB. Dietary patterns of adolescents in Germany - associations with nutrient intake and other health related lifestyle characteristics. BMC Pediatr 2012; 12:35., and income and maternal schooling in Salvador, Bahia, Brazil ⁵⁵55. Silva RCR, Assis AMO, Szarfarc SC, Pinto EJ, Costa LCC, Rodrigues LC. Iniquidades socioeconômicas na conformação dos padrões alimentares de crianças e adolescentes. Rev Nutr 2012; 25:451-61.. In relation to the healthy pattern, studies have found a positive association between this dietary pattern and socioeconomic status ⁵⁴54. Richter A, Heidemann C, Schulze MB, Roosen J, Thiele S, Mensink GB. Dietary patterns of adolescents in Germany - associations with nutrient intake and other health related lifestyle characteristics. BMC Pediatr 2012; 12:35.^,5656. Cutler GJ, Flood A, Hannan P, Neumark-Sztainer D. Major patterns of dietary intake in adolescents and their stability over time. J Nutr 2009; 139:323-8.. This finding can also be explained by the presence of canteens and snack bars that sell unhealthy foods inside schools, while public schools are supported by the National School Feeding Program (PNAE), the goal of which is guarantee healthy, natural diet ⁵⁷57. Ministério da Educação. Resolução nº 26, de 17 de junho de 2013. Dispõe sobre o atendimento da alimentação escolar aos alunos da educação básica no âmbito do Programa Nacional de Alimentação Escolar - PNAE. Diário Oficial da União 2013; 18 jun..

The results of this study are subject to some limitations. The cross-sectional design precludes inference of causality in the associations. The percentage of losses in the study can be considered high, but Silva et al. ³⁴34. Silva TLN, Klein CH, Souza AM, Barufaldi LA, Abreu GA, Kuschnir MC, et al. Participação no Estudo de Riscos Cardiovaculares em Adolescentes - ERICA. Rev Saúde Pública 2016; 50 Suppl 1:3s. analyzed the differences between participating and non-participating adolescents and concluded that the analytical procedures used in ERICA ensured that the participants were representative of the non-participants (since the losses were random), and thus the non-response rate had little impact on the resulting estimates’ precision. In addition, the food consumption data obtained by just one 24hR may not represent the target population’s usual consumption. However, ERICA was the first nationwide school-based survey in Brazil to apply this food consumption instrument to a nationally and regionally representative sample of adolescents. Another limitation was the use of type of school (public versus private) as a proxy for socioeconomic status to describe dietary patterns. This choice was due to the fact that variables related more directly and individually to socioeconomic status (and thus to food consumption), such as maternal schooling (23.8% missing data) and head-of-family’s schooling (36.5% missing data) to classify social class showed such high non-response rates, since adolescents are often unable to answer these questions. Meanwhile, the type of school administration (public versus private) is not only strongly related to maternal schooling and income/socioeconomic status (data not shown), but also differs substantially in relation to the food available during school hours, since private schools in Brazil rarely offer school lunch programs (data not shown).

Considering ERICA’s sample size and representativeness, the current study makes important contributions to adolescents’ food consumption in each of Brazil’s five geographic regions and highlights the need for educational measures aimed at reducing the consumption of processed and ultra-processed foods, targeted specifically to Brazilian adolescents.

Acknowledgments

The authors wish to thank the field interviewers, all the participating schools, and especially the students in the survey that provided the data for the current study, as well as Brazilian Innovation and Research Funding (FINEP 01090421) and Brazilian National Research Council (CNPq 565037/2010-2, 405009/2012-7, and 457050/2013-6) for the research funding.

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