Abstracts

Introduction

Since the 1970s and the 1980s, human diets have been changing from raw, unprocessed foods and traditional dishes to an increasing intake of ultra-processed food and beverages ¹1. Moubarac JC, Martins APB, Claro RM, Levy RB, Cannon G, Monteiro CA. Consumption of ultra-processed foods and likely impact on human health. Evidence from Canada. Public Health Nutr 2013; 16:2240-8.. Advances in food science and technology and globalized food distribution ²2. Popkin BM, Adair LS, Ng SW. Now and then: the global nutrition transition. The pandemic of obesity in developing countries. Nutr Rev 2012; 70:3-21. have made these foods more accessible and convenient at relatively low prices ³3. Monteiro CA. Nutrition and health: the issue is not food, not nutrients, so much as processing. Public Health Nutr 2009; 12:729-31.^,44. Monteiro CA, Levy RB, Claro RM, Castro RR, Cannon G. A new classification of foods based on the extent and purpose of their processing. Cad Saúde Pública 2010; 26:2039-49..

Industrial processing creates food products with ingredients and food additives to enhance flavor, increase durability and contribute to the consumer’s convenience. However, these food products have more calories, higher glycemic indexes, more trans fats, sugars, and sodium, also being lower in fiber, micronutrients, and phytochemicals ⁵5. Ludwig DS. Technology, diet, and the burden of chronic disease. JAMA 2011; 305:1352-3.^,66. Webster JL, Dunford EK, Neal BC. A systematic survey of the sodium contents of processed foods. Am J Clin Nutr 2010; 91:413-20.^,77. Monteiro CA, Cannon G, Levy RB, Claro RM, Moubarac JC, Martins AP, et al. The food system: ultra-processing. The big issue for nutrition, disease, health, well-being. World Nutr 2012; 3:527-69.. There is growing evidence that the increased consumption of ultra-processed foods is associated with overweight ⁵5. Ludwig DS. Technology, diet, and the burden of chronic disease. JAMA 2011; 305:1352-3.^,88. Monteiro CA, Moubarac JC, Cannon G, Ng SW, Popkin B. Ultra-processed products are becoming dominant in the global food system. Obes Rev 2013; 14:21-8., obesity ⁹9. Asfaw A. Does consumption of processed foods explain disparities in the body weight of individuals? The case of Guatemala. Health Econ 2011; 20:184-95.^,1010. Pan American Health Organization. Ultra-processed food and drink products in Latin America: trends, impact on obesity, policy implications. Washington DC: Pan American Health Organization; 2015. and chronic diseases ⁵5. Ludwig DS. Technology, diet, and the burden of chronic disease. JAMA 2011; 305:1352-3.^,1111. World Health Organization. Diet, nutrition and the prevention of chronic diseases. Geneva: World Health Organization; 2003. (WHO Technical Report Series, 916).^,1212. Larsen TM, Dalskov SM, van Baak M, Jeeb AS, Papadaki A, Pfeifer AF, et al. Diets with high or low protein content and glycemic index for weight-loss maintenance. N Engl J Med 2012; 363:2102-13.^,1313. Monteiro CA, Levy RB, Claro RM, Castro IR, Cannon G. Increasing consumption of ultra-processed foods and likely impact on human health: evidence from Brazil. Public Health Nutr 2011; 14:5-13., including diabetes ¹⁴14. Monteiro CA, Cannon G, Levy RB, Moubarac JC, Jaime PC, Martins APB, et al. NOVA: the star shines bright. World Nutr 2016; 7:28-38..

Despite the consumption of ultra-processed foods being higher in high income countries, it has been growing both in relative and absolute terms ¹⁵15. Ricardo CZ, Claro RM. Cost and energy density of diet in Brazil, 2008-2009. Cad Saúde Pública 2012; 28:2349-61.^,1616. World Health Organization. Global status report on noncommunicable diseases 2014: attaining the nine global noncommunicable diseases targets; a shared responsibility. Geneva: World Health Organization; 2014. in high and middle-income countries, especially in the latter ⁸8. Monteiro CA, Moubarac JC, Cannon G, Ng SW, Popkin B. Ultra-processed products are becoming dominant in the global food system. Obes Rev 2013; 14:21-8.. In Brazil, household purchases of ultra-processed food represented 18.7%, 21%, 26.1%, and 29.6% of all food purchases in four successive surveys (1987-1988, 1995-1996, 2002-2003, and 2008-2009, respectively). In the last two periods, ultra-processed food purchases were higher among higher income classes, but have increased in all income groups, mainly among those of lower income ¹⁷17. Martins APB, Levy RB, Claro RM, Moubarac JC, Monteiro CA. Increased contribution of ultra-processed food products in the Brazilian diet (1987-2009). Rev Saúde Pública 2013; 47:656-65..

There are several studies on the health outcomes of social disparities ¹⁸18. Stronks K, van de Mheen HD, Mackenbach JP. A higher prevalence of health problems in low income groups: does it reflect relative deprivation? J Epidemiol Community Health 1998; 52:548-57.^,1919. Caicedo-Velásquez B, Alvarez-Castaño LS, Marí-Dell'Olmo M, Borrell C. Trend in inequalities in mortality due to external causes among the municipalities of Antioquia (Colombia). Gac Sanit 2016; 30:279-86., indicating its influence on health-related behaviors such as diets; in fact, it has been reported that people with higher socioeconomic status generally have a healthier dietary pattern ²⁰20. Proença RPDC. Food and globalization:some thoughts. Ciênc Cult (São Paulo) 2010; 62:43-7.^,2121. Fonseca AB, Souza TSN, Frozi DS, Pereira RA. Modernidade alimentar e consumo de alimentos: contribuições sócio-antropológicas para a pesquisa em nutrição. Ciênc Saúde Coletiva 2011; 16:3853-62.. However, we only identified one study on the association between diet quality (measured by the degree of food processing) and per capita monthly income, whose results go in opposite directions ¹⁷17. Martins APB, Levy RB, Claro RM, Moubarac JC, Monteiro CA. Increased contribution of ultra-processed food products in the Brazilian diet (1987-2009). Rev Saúde Pública 2013; 47:656-65.; despite that, it is unknown whether it would be possible to observe this variation with other socioeconomic indicators. Therefore, this study aims at exploring socioeconomic inequalities in the consumption of ultra-processed foods in a cohort of Brazilian workers.

We investigated the association between socioeconomic position indicators and consumption of ultra-processed foods among participants of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Additionally, we estimated the contribution of unprocessed or minimally processed foods and processed culinary ingredients, processed foods, and ultra-processed foods to the participants’ mean daily dietary intake.

Methods

Study population

This cross-sectional study used data from the ELSA-Brasil collected between 2008 and 2010. ELSA-Brasil is a multicenter cohort study designed to investigate the incidence and progression of diabetes and cardiovascular diseases and their biological, behavioral, environmental, occupational, and psychosocial determinants in Brazilian adults. The ELSA-Brasil cohort consists of 15,105 actives and retired civil servants aged 35-74 years from six higher education institutions in six cities from the South, Southeast, and Northeast regions of Brazil. Data was collected via face-to-face interviews by trained and certified professionals, using standardized and validated questionnaires ²²22. Aquino EM, Barreto SM, Benseñor IM, Carvalho MS, Chor D, Duncan BB, et al. Brazilian Longitudinal Study of Adult Health (ELSA-Brasil): objectives and design. Am J Epidemiol 2012; 175:315-24.. Detailed information on the ELSA-Brasil cohort profile can be found in Schmidt et al. ²³23. Schmidt MI, Duncan BB, Mill JG, Lotufo PA, Chor D, Barreto SM, et al. Cohort profile: Longitudinal Study of Adult Health (ELSA-Brasil). Int J Epidemiol 2015; 44:68-75.. ELSA-Brasil was approved by the Research Ethics Committees from the six participating institutions and all participants provided written consents to participate in the study (approval n. 189/2006).

ELSA-Brasil participants with missing data for dietary intake (n = 36), whose total calorie intake (kcal/day) was below the 1^st percentile (1,146.3kcal/day, n = 150) and above the 99^th percentile (7,176.9kcal/day, n = 151), due to possible underreporting or overreporting of dietary intake, and participants with missing data for occupational social class (n = 238), per capita household income (n = 50), who reported having undergone bariatric surgery (n = 102) (which could result in consequent reductions in dietary intake) were excluded from the analyses, totaling 727 participants. Thus, the final the study population comprised 14,378 participants.

Study variables

Dietary intake was assessed using a one-year, semi-quantitative 114-item food frequency questionnaire (FFQ). The FFQ used showed satisfactory reliability for all nutrients and reasonable relative validity for energy and macronutrients ²⁴24. Molina MC, Faria CP, Cardoso LO, Drehmer M, Velasquez-Melendez JG, Gomes ALC, et al. Diet assessment in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil): development of a food frequency questionnaire. Rev Nutr 2013; 26:167-76.^,2525. Molina M del C, Benseñor IM, Cardoso LO, Velasquez-Melendez JG, Drehmer M, Pereira TSS, et al. Reprodutibilidade e validade relativa do Questionário de Frequência Alimentar do ELSA-Brasil. Cad Saúde Pública 2013; 29:369-79..

Energy values of foods in the FFQ were estimated based on the formula: amount of servings consumed per occasion x weight/serving size x daily intake frequency x nutritional composition of the food serving. The nutritional composition of food items was determined using the Nutrition Data System for Research (NDSR; http://www.ncc.umn.edu/products/) software of the University of Minnesota and the Brazilian Food Composition Table (TACO) of the University of Campinas (UNICAMP) ²⁴24. Molina MC, Faria CP, Cardoso LO, Drehmer M, Velasquez-Melendez JG, Gomes ALC, et al. Diet assessment in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil): development of a food frequency questionnaire. Rev Nutr 2013; 26:167-76..

Food items on the FFQ were assigned to four groups according to the NOVA food classification proposed by Monteiro et al. ¹⁴14. Monteiro CA, Cannon G, Levy RB, Moubarac JC, Jaime PC, Martins APB, et al. NOVA: the star shines bright. World Nutr 2016; 7:28-38., which is based on the nature, extent, and purpose of industrial food processing: unprocessed or minimally processed foods and processed culinary ingredients, processed foods, and ultra-processed foods. In this study, processed culinary ingredients were grouped with unprocessed or minimally processed foods. The food items classification from ELSA-Brasil’s FFQ is presented in Table 1.

The contribution percentage of each food group (unprocessed or minimally processed foods and processed culinary ingredients, processed foods, and ultra-processed foods) was calculated for total calorie intake. The contribution percentage of ultra-processed foods was treated as a continuous variable and used as a response variable.

In the FFQ administered in this study, bread roll, white bread, pita bread, and toasted bread were being considered a single food item. However, according to the classification proposed by Monteiro et al. ¹⁴14. Monteiro CA, Cannon G, Levy RB, Moubarac JC, Jaime PC, Martins APB, et al. NOVA: the star shines bright. World Nutr 2016; 7:28-38., bread roll is classified as a processed food, whereas the other bread types are classified as ultra-processed foods. Thus, we estimated the caloric contribution of bread rolls separately from that of other bread types, using the bread roll caloric contribution reported by the Brazilian Household Budget Survey 2008-2009 for people aged 35-74 years from the South, Southeast, and Northeast regions of Brazil, and incorporated its calories into the caloric contribution of processed foods. Similarly, we incorporated the calories of other bread types into ultra-processed foods.

Socioeconomic position indicators

Education was based on the answer to the question “What is the highest level of education you have completed?”, participants were assigned to the following education levels: incomplete elementary school, complete elementary school, who attended high school, graduate and postgraduate degrees. Per capita household income was expressed in quintiles of study group distribution. The occupational social class was defined based on the socioeconomic status of the participant’s current job. The socioeconomic status of a job is a measure obtained when comparing the expected and the observed educational and income levels of a job, according to the Brazilian occupational matrix ²⁶26. Ministério do Trabalho e Emprego. Classificação brasileira de ocupações. 3ª Ed. Brasilia: Ministério do Trabalho e Emprego; 2010. from 2008 to 2010. The resulting socioeconomic status measurements were grouped into seven strata to achieve a minimum intra-stratum variance and a maximum variation between the strata. The seven occupational social class groups were subsequently named as it follows: “high-upper”, “high-low”, “middle-upper”, “middle-middle”, “middle-low”, “low-high” and “low-low” ²⁷27. Hermeto A. Apresentação e discussão de alternativas para categorizações ocupacionais no Brasil. Belo Horizonte: Centro de Desenvolvimento e Planejamento Regional, Universidade Federal de Minas Gerais; 2014. (Texto para Discussão).. For the current analysis, the strata were grouped into the following groups: high (upper-high + upper-low), middle (upper-middle + middle-middle), lower-middle, lower-high, and lower-low.

These indicators were chosen because each of them provide a partial view of socioeconomic inequalities and food patterns. For instance, education level can contribute to food choices ²⁸28. Galobardes B, Morabia A, Bernstein MS. Diet and socioeconomic position: does the use of different indicators matter? Int J Epidemiol 2001; 30:334-40.^,2929. Turrell G, Hewit B, Patterson C, Oldenburg B. Measuring socio-economic position in dietary research: is choice of socio-economic indicator important? Public Health Nutr 2003; 6:191-200., either by providing more access to information or by being indirectly associated with more highly qualified occupations and higher wages ²⁸28. Galobardes B, Morabia A, Bernstein MS. Diet and socioeconomic position: does the use of different indicators matter? Int J Epidemiol 2001; 30:334-40.. Income directly measures the material resources available to be used as health resources, such as food choices ³⁰30. Food and Agriculture Organization of the United Nations. Country fact sheet on food and agriculture policy trends: Brazil. Washington DC: Food and Agriculture Organization of the United Nations; 2014.^,3131. Kearney J. Food comsumption trends and drivers. Philos Trans R Soc Lond B Biol Sci 2010; 365:2793-807.. The occupational social status can influence people’s diet, through eating habits shared in the workplace, in social networks, and through cultural aspects regarding their occupation ²⁹29. Turrell G, Hewit B, Patterson C, Oldenburg B. Measuring socio-economic position in dietary research: is choice of socio-economic indicator important? Public Health Nutr 2003; 6:191-200.^,3232. Galobardes B, Lynch J, Smith GD. Measuring socioeconomic position in health research. Br Med Bull 2007; 81/82:21-37..

Adjustment variables

The variables age group (35-44, 45-54, 55-64, 65-74 years) and sex were also included in the analysis. These variables were considered as adjustment factors because the consumption of ultra-processed foods ranges according to sociodemographic levels.

Statistical analysis

The caloric contribution percentage of each food group (unprocessed or minimally processed foods and processed culinary ingredients, processed foods, and ultra-processed foods) and of the most consumed foods from each group were calculated for total calorie intake. The mean (interquartile range) of the contribution percentage of ultra-processed foods was calculated according to each explanatory variable. Differences in mean caloric contribution of ultra-processed foods across explanatory variables were compared using the Kruskal-Wallis test.

Generalized linear models (GLM) with gamma distribution and logarithmic link function were used to estimate the strength of the association between socioeconomic status indicators and consumption of ultra-processed foods. The results are presented as arithmetic mean ratios (AMR) and 95% confidence intervals (95%CI), which are exponential regression coefficients ³³33. Lindsey JK, Jones B. Choosing among generalized linear models applied to medical data. Stat Med 1998; 17:59-68..

We performed separated models for each socioeconomic position indicator: education, per capita household income, and occupational social class. First, we estimated the crude models, which were then adjusted for sex and age. All variables associated with the response variable at p < 0.2 in univariable analysis were included in multiple regression models and retained in the final model when associated with the response variable at p < 0.05. Final models were adjusted using the the Pearson’s statistical test. All analyses were performed using the software Stata 12.0 (StataCorp LP, College Station, USA).

Results

From 14,378 participants, 39.4% were aged between 45 and 54 years, most were women and self-reportedly white, 36.6% had a postgraduate degree, 33.6% were part of high occupational social classes, and 15.2% were in the lower-low occupational social class (Table 2). The mean of per capita household income was BRL 1,742.3 ± 1,431.

The mean for total calorie intake was 2,945 ± 1,084kcal/day, 1,146-7,164kcal/day. Unprocessed or minimally processed foods and processed culinary ingredients contributed to nearly two-thirds (65.7%) of the total calorie intake, followed by ultra-processed foods (22.7%), and processed foods (11.6%). Fruits, rice, red meat and derivatives, and poultry contributed with more than 30% of total caloric intake. Bread roll (9%) was the one that contributed with most calories among processed foods, followed by white cheeses and yellow cheeses. The most common ultra-processed foods in terms of caloric contribution were ultra-processed-bread (3.8%), treats (3.1%), followed by cakes and biscuits, pizza and snacks, soft drinks and processed or artificial juices (Table 3).

Percentage caloric contribution of ultra-processed foods

The mean caloric contribution percentage of ultra-processed foods decreased with age and was higher among female. In addition, the mean caloric contribution percentage of ultra-processed foods gradually decreased as the education level was higher, having the lowest value in participants with incomplete elementary school; it also decreased according to higher levels of per capita household income. Mean caloric contribution percentage was lower in lower-low and lower-high occupational social classes (Table 4).

In the multiple regression analysis (Table 5), compared to those with postgraduate degree, the arithmetic mean of caloric contribution percentage of ultra-processed foods was 4%, 12%, 16% and 20% lower among participants with university degree, who had completed high school and elementary school, and who had not complete elementary school, respectively. The mean caloric contribution percentage of ultra-processed foods was 5%, 10%, 15%, and 20% lower among participants from the lowest four income quintiles (4^th, 3^rd, 2^nd, 1^st), respectively, compared to those from the upper income quintile (5^th). Compared to individuals from high occupational social classes, the mean caloric contribution percentages of ultra-processed foods was 7%, 12%, 12%, and 17% lower among middle, lower-middle, lower-high, and lower-low occupational social classes, respectively. All the associations suggest a dose-response gradient.

Discussion

In this study, unprocessed or minimally processed foods and processed culinary ingredients, such as fruits, rice, meat, and beans contributed with two-thirds of people’s total daily calorie intake, while ultra-processed foods contributed with more than one-fifth. Additionally, participants with worse socioeconomic indicators (education, income, and occupational social class) reported a lower caloric contribution from ultra-processed foods with indication of a dose-response relationship in the associations.

The overall contributions of unprocessed or minimally processed foods and processed culinary ingredients, processed foods, and ultra-processed foods to total calorie intake observed in this study are similar to the findings reported for Brazilians with 10 or more years old living in urban and rural areas from 2008 to 2009. It is worth to highlight that this study collected dietary information on the Brazilian population in two dietary recall interviews on nonconsecutive days ³⁴34. Louzada MLC, Martins APB, Canella DS, Baraldi LG, Levy RB, Moubarac JC, et al. Ultra-processed foods and the nutritional dietary profile in Brazil. Rev Saúde Pública 2015; 49:38., whereas, in the current study, participants completed a single FFQ, which is a more adequate dietary pattern measure ³⁵35. Slater B, Philippi ST, Marchioni DML, Fisberg RM. Validação de Questionários de Freqüência Alimentar - QFA: considerações metodológicas. Rev Bras Epidemiol 2003; 6:200-8.. Ultra-processed foods contributed with more one-fifth of the mean total calories consumed by both ELSA-Brasil participants and the urban and rural population of Brazil, a lower proportion compared to the one observed for the US population over one year of age (~60%) ³⁶36. Martínez Steele E, Baraldi LG, Louzada ML, Moubarac JC, Mozaffarian D, Monteiro CA. Ultra-processed foods and added sugars in the US diet: evidence from a nationally representative cross-sectional study. BMJ Open 2016; 6:e009892., and lower proportion compared to the contribution of ultra-processed foods to the total of purchased food for home consumption in Chile and Canada (> 50%) ⁸8. Monteiro CA, Moubarac JC, Cannon G, Ng SW, Popkin B. Ultra-processed products are becoming dominant in the global food system. Obes Rev 2013; 14:21-8.^,3737. Crovetto MM, Uauy R, Martins AP, Moubarac JC, Monteiro CA. Disponibilidad de productos alimentarios listos para el consumo en los hogares de Chile y su impacto sobre la calidad de la dieta (2006-2007). Rev Méd Chile 2014; 142:850-8.. Variations in age-ranges and survey methods among the compared countries and this cohort might account for part of the largely observed differences, but are unlikely to be the main cause of them. We suppose that the strong Brazilian food culture may influence the higher contribution of unprocessed or minimally processed foods, and processed culinary ingredients, and the lower contribution of processed and ultra-processed foods in the diet of the participants of this study, and also the results from the comparison between the Brazilian population diet and the developed countries diet ³⁸38. Monteiro CA, Cannon G, Moubarac JC, Martins AP, Martins CA, Garzillo J, et al. Dietary guidelines to nourish humanity and the planet in the twenty-first century. A blueprint from Brazil. Public Health Nutr 2015; 18:1-12..

Our results differ from other studies that have shown that worse socioeconomic position indicators such as education, per capita household income, and occupational social class, often influence the increased consumption of specific ultra-processed foods, such as pies, sausages, pastries, ice cream ³⁹39. Miura K, Giskes K, Turrell G. Socio-economic differences in takeaway food consumption among adults. Public Health Nutr 2012; 15:218-26. soft drinks ⁴⁰40. Malta DC, Iser BPM, Claro RM, Moura L, Bernal RTI, Nascimento AF, et al. Prevalência de fatores de risco e proteção para doenças crônicas não transmissíveis em adultos: estudo transversal, Brasil 2012. Epidemiol Serv Saúde 2013; 22: 423-34.^,4141. Park S, Onufrak S, Sherry B, Blanck HM. The relationship between health-related knowledge and sugar sweetened beverage intake among US adults. J Acad Nutr Diet 2014; 114:1059-66., energy drinks, and processed juices ⁴¹41. Park S, Onufrak S, Sherry B, Blanck HM. The relationship between health-related knowledge and sugar sweetened beverage intake among US adults. J Acad Nutr Diet 2014; 114:1059-66.. Low socioeconomic position has been associated with increased consumption of food such as pasta, fried foods, sugar and fat, whereas high socioeconomic position has been associated with increased consumption of whole grains, lean meats, fish, low-fat dairy products, and fresh fruits and vegetables ²⁸28. Galobardes B, Morabia A, Bernstein MS. Diet and socioeconomic position: does the use of different indicators matter? Int J Epidemiol 2001; 30:334-40.^,4242. Darmon N, Drewnowski A. Does social class predict diet quality? Am J Clin Nutr 2008; 87:1107-17.. However, other aspects such as the typical food culture of different societies may also affect this. In countries where fruits and vegetables are a major part of the diet such as Greece, Portugal, and Spain, people with lower socioeconomic position usually consume more fruits and vegetables than people with higher socioeconomic position ⁴³43. Roos G, Johansson L, Kasmel A, Klumbiene J, Prattala R. Disparities in vegetable and fruit consumption: European cases from the north to the south. Public Health Nutr 2001; 4:35-43.. It is important to note that the process of choosing, purchasing, and consuming foods is driven by a complex combination of biological, social, and cultural interactions ³¹31. Kearney J. Food comsumption trends and drivers. Philos Trans R Soc Lond B Biol Sci 2010; 365:2793-807.^,4444. Poulain JP, Proença RPDC. O espaço social alimentar: um instrumento para o estudo dos modelos alimentares. Rev Nutr 2003; 16:245-56.^,4545. Poulain JP, Proença RPDC. Reflexões metodológicas para o estudo das práticas alimentares. Rev Nutr 2003; 16:365-86.^,4646. Friel S, Hattersley L, Ford L, O'Rourke K. Addressing inequities in healthy eating. Health Promot Int 2015; 30 Suppl 2:ii77-88.. The determinants of dietary intake include individual characteristics such as nutritional knowledge and structural factors such as access points of purchase and food prices ⁴⁷47. Brug J, Kremers SP, Lenthe F, Ball K, Crawford D. Environmental determinants of healthy eating: in need of theory and evidence. Proc Nutr Soc 2008; 67:307-16.^,4848. Claro RM, Monteiro CA. Family income, food prices, and household purchases of fruits and vegetables in Brazil. Rev Saúde Pública 2010; 44:1014-20..

Our findings were similar to previous analyses that showed that higher per capita household income was associated with a higher contribution of ultra-processed foods to total food purchased by Brazilian households ¹⁷17. Martins APB, Levy RB, Claro RM, Moubarac JC, Monteiro CA. Increased contribution of ultra-processed food products in the Brazilian diet (1987-2009). Rev Saúde Pública 2013; 47:656-65.. However, there were methodological differences between this study and the study by Martins et al. ¹⁷17. Martins APB, Levy RB, Claro RM, Moubarac JC, Monteiro CA. Increased contribution of ultra-processed food products in the Brazilian diet (1987-2009). Rev Saúde Pública 2013; 47:656-65., for example: (1) estimates of food consumption based on food purchase are not the same as those based on the food frequency questionnaire; (2) the age range of the latter was wider (≥ 18 years) than that of ELSA-Brasil (35-74 years); and (3) the range of the Brazilian households income is much higher than the one used in this study, but our results point in the same direction.

To verify the consistency of the associations between socioeconomic position and consumption of ultra-processed foods, we have explored the association of ultra-processed food with different socioeconomic position indicators in our analysis. We did this because each socioeconomic position indicator has its own specificity, in spite of being highly correlated with each other. For instance, educational level influences the occupation and together they can determine the level of income ⁴⁹49. Barata RB, Ribeiro MCSA, Silva ZP, Antunes JLF. Classe social: conceitos e esquemas operacionais em pesquisa em saúde. Rev Saúde Pública 2013; 47:647-55.. The Spearman correlation (data not shown) between the socioeconomic position indicators used ranged from 56% to 76% (p < 0.001), a medium to high correlation. Simultaneous adjustment for the socioeconomic position indicators could violate the multicollinearity assumption and consequently lead to an underestimation of the estimated coefficients ⁵⁰50. Fávero LP. Data analises. Rio de Janeiro: Elsevier; 2015..

Even though eating patterns in Brazil have been strongly influenced by the food industry in the last twenty years ³¹31. Kearney J. Food comsumption trends and drivers. Philos Trans R Soc Lond B Biol Sci 2010; 365:2793-807.^,5151. Monteiro CA. The big issue is ultra-processing. World Nutr 2010; 1:237-69., ultra-processed foods still have higher added value than less processed foods in the country ⁵²52. Hoffmann R. Elasticidades-renda das despesas e do consumo de alimentos no Brasil em 2002-2003. In: Silveira FG, Servo LMS, Menezes T, Piola SF, organizadores. Gasto e consumo das famílias brasileiras contemporâneas. 2a Ed. Brasília: Instituto de Pesquisa Econômica Aplicada; 2007.^,5353. Claro RM, Maia EG, Costa BVL, Diniz DP. Preço dos alimentos no Brasil: prefira preparações culinárias a alimentos ultraprocessados. Cad Saúde Pública 2016; 32:e00104715.. Unlike the United Kingdom, where the cost of ultra-processed foods is on average 13% lower than the cost of unprocessed or minimally processed foods and processed foods together, ultra-processed foods are about 52% more expensive than both food groups in Brazil ⁵⁴54. Moubarac JC, Claro RM, Baraldi LG, Levy RB, Martins AP, Cannon G, et al. International differences in cost and consumption of ready-to-consume food and drink products: United Kingdom and Brazil, 2008-2009. Glob Public Health 2013; 8:845-56.. In addition, we believe that declining the prices of ultra-processed foods in Brazil would probably promote an increase in consumption among the poorer classes. The purchase of ultra-processed foods over the 2002/2003-2008/2009 period increased more among lower-income families than among higher-income families ¹⁷17. Martins APB, Levy RB, Claro RM, Moubarac JC, Monteiro CA. Increased contribution of ultra-processed food products in the Brazilian diet (1987-2009). Rev Saúde Pública 2013; 47:656-65..

The strengths of this study include the analysis of individual food intake in a cohort of adults from different regions of Brazil using a validated food frequency questionnaire to measure food consumption over a 12-month period ²⁴24. Molina MC, Faria CP, Cardoso LO, Drehmer M, Velasquez-Melendez JG, Gomes ALC, et al. Diet assessment in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil): development of a food frequency questionnaire. Rev Nutr 2013; 26:167-76.^,2525. Molina M del C, Benseñor IM, Cardoso LO, Velasquez-Melendez JG, Drehmer M, Pereira TSS, et al. Reprodutibilidade e validade relativa do Questionário de Frequência Alimentar do ELSA-Brasil. Cad Saúde Pública 2013; 29:369-79.. Reverse causality should not be a limitation to address the associations found, because consumption of ultra-processed foods is unlikely to lead to higher socioeconomic position. One limitation of the study is our reliance on a FFQ containing more than 100 food items, as this may lead to overreporting ⁵⁵55. Willett WC. Future directions in the development of food-frequency questionnaires. Am J Clin Nutr 1994; 59(1 Suppl):171S-4S.. Furthermore, the FFQ is not appropriate to report dietary intake according to the degree of food processing, which limited our ability and accuracy to classify some food items. In addition, the classification of foods according to processing level is new and susceptible to future updates and changes. This study included participants from six Brazilian states with stable jobs and higher education level and income than the Brazilian population.

Conclusions

Unprocessed or minimally processed foods were the ones that contributed the most to participants’ daily calorie intake. Our results showed that ultra-processed food consumption ranged according to socioeconomic status and was higher among individuals with higher socioeconomic position, results that suggest a dose-response relationship for these associations.

Acknowledgments

The authors thank the staff and participants of the ELSA-Brasil study for their important contributions. The Department of Sciency and Technology, Brazilian Ministry of Health and the Brazilian Ministry of Science, Technology and Innovation supported this study (Funding Authority for Studies and Projects/Brazilian National Reserach Council - FINEP/CNPq). We thank them, and also Brazilian Graduate Studies Coordinating Board (Capes) and CNPq for their financial support.

References

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