The last decades were marked by a rising prevalence in obesity in high-, low- and middle-income countries(Reference Ng, Fleming and Robinson1). Changes in food patterns were observed in the same period and represent one of the main factors related to obesity rising. Basic traditional foods, such as bean, milk, fruit and vegetables, that present low energy density and high nutrient density were replaced by ready-to-eat or ready-to-heat products, which, in general, present high processing level as well as high contents of sugar, salt and fat(Reference Popkin2–Reference Popkin4).
NOVA classification allocates food products into four groups according to the industrial processing degree: unprocessed or minimally processed foods, processed culinary ingredients, processed foods and ultra-processed foods(Reference Monteiro, Cannon and Moubarac5,6) . This last group includes ready-to-eat or ready-to-heat products, which are highly convenient as they require little or no cooking. Usually, the consumption of these foods tends to replace that of unprocessed or minimally processed foods, leading to a nutritionally unbalanced diet(Reference Monteiro, Cannon and Moubarac5). Moreover, substances for industrial use only, such as dyes and flavours, added for the purpose of increasing durability make these foods highly palatable, helping to increase their consumption(Reference Monteiro, Cannon and Moubarac5,6) . Although the use of additives is legally authorised, the effects on health of its cumulative lifetime consumption and of the interaction among additives are still unknown(Reference Fiolet, Srour and Sellem7).
The annual trend in the sales of ultra-processed foods has been increasing, reflecting the rise in the consumption of such foods(8,Reference Stuckler, McKee and Ebrahim9) . In Brazil, a national study among adolescents and adults showed that about 30 % of the total daily energy intake in 2008–2009 came from ultra-processed foods(Reference Louzada, Baraldi and Steele10). In studies carried out in high-income countries, daily energies from ultra-processed foods ranged from 48·0 to 58·5 % from 2004 to 2014(Reference Baraldi, Steele and Canella11–Reference Rauber, da Costa Louzada and Steele13).
In cross-sectional studies, the consumption of these products seems to be higher in younger populations(Reference Louzada, Baraldi and Steele10–Reference Moubarac, Batal and Louzada12). However, studies about the factors related to ultra-processed food consumption in these age groups are scarce. Thus, we aimed to describe ultra-processed food consumption according to demographic and socioeconomic characteristics using data from the 2004, 1993 and 1982 Pelotas Birth Cohorts at 11, 22 and 30 years, respectively, collected between 2012 and 2015.
The 2004, 1993 and 1982 Pelotas Birth Cohorts have been following up all live-births throughout life from those years. For the three cohorts, the eligible newborns were children of mothers living in the urban area of Pelotas according to the territorial boundaries on 1 January 1982, as part of the city was emancipated in May 1982. Enrolment strategies were the same in the three cohorts, with the inclusion of births that occurred in all maternity hospitals in the city. In 2004, 1993 and 1982, a total of 4231, 5249 and 5914 mothers, respectively, accepted to participate and were interviewed shortly after delivery (refusal rate of <1 % in all three cohorts), and their children were subsequently followed up over the years. Methodological details of the three cohorts are described in other publications(Reference Santos, Barros and Matijasevich14–Reference Horta, Gigante and Goncalves16).
In relation to the original sample, the follow-up rates were 86·6 % in the 2004 cohort (11 years), 76·3 % in the 1993 cohort (22 years) and 68·1 % in the 1982 cohort (30 years). Both during hospital interviews (perinatal study) and follow-ups, trained interviewers applied standardised and pre-coded questionnaires, similar among the cohorts. The instruments contained questions on health-related issues. The data of follow-ups used in this article were collected in a clinic specifically set up for the cohorts(Reference Santos, Barros and Matijasevich14–Reference Horta, Gigante and Goncalves16).
Participants’ food consumption was evaluated through the application of semiquantitative FFQs, which investigate eating habits of the 12 months prior to the interview. Consumption frequency of each food item was measured from the following alternatives: never or <1x per month, 1–3x per month, 1x per week, 2–4x per week, 5–6x per week, 1x per day, 2–4x per day and ≥5x per day. The three cohorts’ FFQ contained eighty-eight items(Reference Schneider, dos Santos Motta and Muniz17), and the average portion was based on domestic measures according to the Table for Assessment of Food Intake in Household Measures(Reference Pinheiro, Lacerda and Benzecry18). The average portion was presented to the participant verbally and with the help of images, and the participant answered if its consumption was usually smaller, equal to or larger than the average portion. A few number of foods in the FFQ used in the 1982 cohort were different from those of the other two cohorts. The methods used for building the FFQ are described in another publication(Reference Schneider, dos Santos Motta and Muniz17). Food items investigated in the FFQ are arranged in online supplementary material (Supplementary Table).
For statistical analyses, we converted consumption frequencies reported in each FFQ food item to annual consumption. Answer options represented a consumption of, respectively, 0, 12, 52, 104, 260, 365·25, 730·5 and 1826·25 times a year. To obtain daily consumption, we divided annual consumption by 365·25. Then, we calculated the quantity in grams for each food item, using the daily frequency of consumption and the average portion. To take into account portion sizes, we divided the gram values by two if the reported portion was less than the average, or we multiplied by 1·5 if it was larger than the average portion. Macronutrient content (carbohydrate, protein and fat) was defined for each food, based on the consumed value in grams and the Brazilian Food Composition Table (TACO)(19) or USDA Nutrient Database for Standard Reference(20), when not available in TACO. Energetic value in kJ for each item was obtained multiplying carbohydrates and proteins by 16·7 kJ and lipids by 37·7 kJ (corresponding to 4 and 9 kcal, respectively). Finally, the total daily energetic intake was calculated grouping kilocalories from carbohydrates, proteins and lipids from all food items. The FFQ items were distributed in the four groups proposed by the NOVA classification(Reference Monteiro, Cannon and Moubarac5) (see online supplementary material, Supplementary Table) and then the proportion of energies from each food group was calculated, relative to the total energy intake.
Covariables included maternal characteristics evaluated in perinatal study: age (≤24, 25–34, ≥35 years), schooling (0–4, 5–8, 9–11, ≥12 completed years of study), self-reported skin colour (white or black for the 1982 Cohort, and white, brown/yellow/indigenous or black for other cohorts) and total monthly family income (quintiles). Variables of cohort members were sex (male or female) in the perinatal study, and skin colour (white, brown/yellow/indigenous or black), schooling (first–third, fourth–fifth or sixth–seventh scholar years for the 2004 Cohort; and 0–4, 5–8, 9–11 or ≥12 completed years of study for other cohorts) and current total monthly family income (quintiles) at 11-, 22- and 30-year follow-ups. Also, as potential confounders in the follow-ups, we included diet to lose weight in the last 12 months (yes or no) and energy intake/energy expenditure ratio(Reference Leech, Worsley and Timperio21).
Energy intake was total daily intake, and energy expenditure was calculated from the equations recommended by the Institute of Medicine (IOM)(22), taking into consideration participants’ sex, nutritional status and level of physical activity. Nutritional status was classified by BMI. At 11-year follow-up, cut-off points for low or normal weight and for overweight were, respectively, <+1 and ≥+1 z-score(Reference Onis, Onyango and Borghi23); and at 22- and 30-year follow-ups, respectively, <25 and ≥25 kg/m2(24). In all the three cohorts, the level of physical activity was determined using accelerometers (GENEActiv; ActivInsights; and Actigraph® GT3X) by the participants for about 6 d with a 24-h protocol, and the raw data were analysed with R-package GGIR(Reference da Silva, van Hees and Ramires25). We used moderate to vigorous-intensity physical activity data (cut-off points of 100 mg, an acceleration threshold corresponding to walking; and 10 min bout)(Reference da Silva, van Hees and Ramires25). Quartile cut-offs were set to classify participants as very low active, low active, active and very active, according to the levels defined by the IOM(22).
Data were collected and entered directly in Pendragon and REDCap (Research Electronic Data Capture)(Reference Harris, Taylor and Thielke26). The statistical package Stata (version 12.1) was used to run the analyses. We calculated the mean percentages of energy contribution from the food groups in each cohort, adjusted for perinatal and current characteristics, for the total sample and after stratification by sex. Thereafter, crude and adjusted average energy contribution from ultra-processed foods was described according to perinatal and current socioeconomic variables, presenting mean values and 95 % confidence intervals (CIs). Energy intake/energy expenditure ratio was included into the adjustment model that described the proportions of energy contribution from each processing group to reduce misclassification bias from under- or overreporting inherent in the use of FFQ(Reference Leech, Worsley and Timperio21), as well as information about diet to lose weight in the last 12 months. We ran adjusted analyses by linear regression accounting for demographic and socioeconomic variables. We constructed a hierarchical conceptual model(Reference Victora, Huttly and Fuchs27) using backward selection procedure by levels and maintaining the variables that presented P-values <0·05 in the level in the adjusted model. First and second levels included perinatal variables (maternal skin colour and age, and maternal schooling and family income, respectively). The third level included participants’ sex and skin colour; and the fourth level, participants’ schooling and current family income (at follow-ups). Energy intake/energy expenditure ratio and diet to lose weight in the last 12 months were not included in the model since they are at a lower hierarchical level and, therefore, are not considered confounders for the association between socioeconomic variables and the outcome. We tested the associations using aggregated and non-aggregated data. In aggregated data, we included time (cohort) and individual as two levels to allow for variables that were not measured. However, we did not find differences between the two ways, then we analysed each cohort separately. Variables presenting P-values <0·05 were considered significantly associated to the outcome, and tests of linear trend were carried out for ordinal variables.
The Ethics Committee of the Faculty of Medicine of the Federal University of Pelotas approved the follow-ups of the 2004, 1993 and 1982 Cohorts (889 753; 1 250 366; and 16/12, respectively). Participants of the 1993 and 1982 Cohorts signed a consent term. In the 2004 Cohort, the mother or legal guardian signed the consent term; and the adolescent, the assent term.
At 11, 22 and 30 years, respectively, 3514, 3648 and 3646 participants had information related to FFQ and were included in the current analysis. In the three cohorts, at the time of delivery, most of the mothers were ≤24 years, had 5–8 years of schooling and self-declared white skin colour (Table 1). Some differences were observed among those who provided food information at 11, 22 and 30 years and those lost in follow-up. At 11 and 30 years, losses were higher among the extreme categories of maternal schooling (0–4 and ≥12 years of study) and family income (first and fifth quintiles). At age 11, there were more losses among children of younger mothers (≤24 years); and at 22 and 30 years, among male participants (Table 1).
Variables marked in bold represent significant statistical differences between followed-up and lost participants at 11, 22 and 30 years (P < 0·05).
* Participants with information regarding the FFQ.
† Total deaths from birth to 11 (98 deaths), 22 (193 deaths) and 30 (325 deaths) years were discounted from the total n of lost participants.
Table 2 shows that, considering the 11-year participants, about 72 % of them were attending the fourth or fifth scholar years. Among the 22-year participants, 41 % reported 9–11 years of schooling; and for the 30-year participants, 44 % reported ≥12 years of schooling. In all the three cohorts, most of participants self-reported white skin colour (Table 2).
The average energy contribution from ultra-processed foods, adjusted for perinatal and current characteristics, was higher in the younger cohort (at age 11) (33·8 %; 95 % CI 33·6, 34·0) than in the 1993 Cohort (29·6 %; 95 % CI 29·4, 29·8) and in the older cohort (at age 30) (25·1 %; 95 % CI 24·7, 25·4). On the other hand, the proportion of energies from unprocessed or minimally processed foods was lower in the younger cohort: 52·2 % (95 % CI 51·9, 52·4), 56·0 % (95 % CI 55·7, 56·2) and 58·8 (95 % CI 58·6, 59·1) at 11, 22 and 30 years, respectively (Fig. 1).
Table 3 shows the average proportions of energy contribution from ultra-processed foods, according to perinatal and current variables. In the 2004 and 1993 Cohorts, those whose mothers self-declared as black had higher mean daily energy contribution from ultra-processed foods, compared to those whose mothers declared themselves as white, as well as those self-declared as brown, yellow or indigenous in the 1993 Cohort. In the two younger cohorts, the average daily contribution from ultra-processed foods was higher among children of younger mothers who had lower schooling and belonged to poorer families. For the 1982 Cohort, the opposite was observed, with the highest averages of energy contribution coming from ultra-processed foods observed among those whose mothers had higher schooling and belonged to wealthiest families. Higher contributions from ultra-processed foods were observed in the 1993 Cohort among those who self-declared as black, brown, yellow or indigenous, compared to those who declared themselves as white; and in the 1993 and 1982 Cohorts, among the women. The lower level of education in the 2004 and 1993 Cohorts and higher education in the 1982 Cohort were associated to higher averages of energy contribution from ultra-processed foods. In the 2004 and 1993 Cohorts, the poorest – and in the 1982 Cohort, the richest – presented higher average energy contributions from ultra-processed foods (Table 3).
Mean differences evaluated by linear regression. Values in bold represent statistically significant associations.
* P-value for linear trend.
In the adjusted analysis, in the 2004 and 1993 Cohorts, those whose mothers self-reported as black presented a higher energy contribution from ultra-processed foods, as well as those self-declared as brown, yellow or indigenous in the 1993 Cohort, compared to white skin colour. The same was observed in the 1982 Cohort participants who self-declared as black (Table 4). The effect of maternal perinatal schooling on energy contribution from ultra-processed foods to daily energy intake was maintained: in the 2004 and 1993 Cohorts, higher average values were observed among those of less educated mothers; and in the 1982 Cohort, among those of more educated mothers. The same direction was observed for family income in the 1982 Cohort, with participants from richer families showing higher average energy contributions from ultra-processed foods. Also, compared to their counterparts, higher average energy contributions from ultra-processed foods were observed among women in the 1993 (30·7; 95 % CI 30·2, 31·3) and 1982 (25·8; 95 % CI 25·3, 26·3) Cohorts; among individuals with lower education in the 1993 Cohort; and in those with 9–11 completed years of study in the 1982 Cohort (Table 4).
Analyses by multiple linear regression. Values in bold represent statistically significant associations.
* P-value for linear trend.
In summary, the current study showed that energy contribution from ultra-processed foods was higher among participants from the 2004 Cohort (at age 11), while the contribution of fresh or processed foods was higher in the 1982 Cohort (at age 30). Black maternal skin colour in the 2004 and 1993 Cohorts was associated to a higher energy contribution from ultra-processed foods, as well as among the 1982 Cohort members. Maternal schooling was associated to ultra-processed food consumption in the three cohorts (higher consumption among participants from less-schooled mothers in the 2004 and 1993 Cohorts, with an inverse dose–response; and higher consumption among participants from more-schooled mothers in the 1982 Cohort, with a direct dose–response). Perinatal family income was associated to the highest energy contribution from ultra-processed foods only in the 1982 Cohort. Also, female sex in the 1993 and 1982 Cohorts, lower participant schooling in the 1993 Cohort, and higher participant schooling in the 1982 Cohort were associated to a higher energy contribution from ultra-processed foods.
Energy intake from ultra-processed foods in the three cohorts ranged from 25 to 33 %. Three studies with a nationwide representative sample – one carried out in Brazil in 2008–2009, two others with data from the Mexican (in 2012) and Chilean (in 2010) population – found similar values(Reference Louzada, Baraldi and Steele10,Reference Marrón-Ponce, Sánchez-Pimienta and da Costa Louzada28,Reference Cediel, Reyes and da Costa Louzada29) . Lower consumption of ultra-processed foods according to increasing age has been observed among Mexicans, Chileans and Canadians(Reference Moubarac, Batal and Louzada12,Reference Marrón-Ponce, Sánchez-Pimienta and da Costa Louzada28,Reference Cediel, Reyes and da Costa Louzada29) . Children and adolescents may represent populations that are more vulnerable to advertisements on ultra-processed products, which may influence their eating behaviours, perceptions and preferences(Reference Henriques, Dias and Burlandy30,Reference Kelly, Halford and Boyland31) .
In the 2004 and 1993 Cohorts, those whose mothers self-declared black skin colour, as well as the participants of the 1982 Cohort, presented a higher energy contribution from ultra-processed foods. It is possible that these results are due to less access to information in health services of individuals with non-white skin colour due to racial discrimination or lower schooling(Reference Chor and Lima32,Reference Phelan and Link33) .
Also in the 2004 and 1993 Cohorts, the relation of the energy contribution from ultra-processed foods with maternal schooling – and with participant schooling in the 1993 Cohort – were inverse; and in the 1982 Cohort, the relation with maternal and participant schooling was direct. It was expected that, in all the three cohorts, higher schooling would promote greater access to information and, consequently, healthier lifestyles. Most studies that evaluated the influence of schooling on ultra-processed food consumption found a direct relationship(Reference Louzada, Baraldi and Steele10,Reference Moubarac, Batal and Louzada12,Reference Marrón-Ponce, Sánchez-Pimienta and da Costa Louzada28,Reference Simões, Cardoso and Benseñor34) . Family income at birth was associated to energy contribution from ultra-processed foods only in the 1982 Cohort, with higher contributions observed among the richest. Other studies carried out in middle-income countries showed higher energy intake from these foods according to increasing income, possibly due to the greater purchasing power of the richest strata(Reference Louzada, Baraldi and Steele10,Reference Marrón-Ponce, Sánchez-Pimienta and da Costa Louzada28,Reference Cediel, Reyes and da Costa Louzada29,Reference Simões, Cardoso and Benseñor34) . Higher consumption of ultra-processed foods observed in the 1982 Cohort among participants of higher schooling and higher income may be due to eating habits acquired in childhood or adolescence. It is possible that the intensification of globalisation and industrialisation during the 1980s, which has promoted increased sophistication in food processing, marketing and distribution techniques, has also promoted greater access to foods of a higher processing level by those with higher purchasing power – in this case, mothers with higher income and schooling on the birth occasion of the 1982 Cohort members(Reference Popkin2,Reference Popkin4) . Together, during the same period, limited concern to healthy diets and body weight by health services or the media may have promoted the development of preferences based on ultra-processed foods(Reference Barros, Victora and Santos35).
Regarding sex, a higher contribution from ultra-processed foods was observed among women in the 1993 and 1982 Cohorts, but not in the 2004 Cohort. Louzada etal., who evaluated Brazilians ≥10 years of age, found a higher consumption of ultra-processed foods among females, compared to males. Mais etal. found null results when evaluating children from 2 to 9 years of age(Reference Louzada, Baraldi and Steele10,Reference Mais, Warkentin and Vega36) . The results of the current study are not in line with our expectation, considering that women represent the portion of a population using health services the most and, consequently, having greater access to food-related information(Reference Levorato, Mello and Silva37–Reference Guibu, Moraes and Guerra39). No differences were observed between boys and girls in the 2004 Cohort, possibly due to the existence of other determinants strongly related to consumption, such as dietary habits of the family.
It is important to highlight some limitations of this study. The method used to evaluate food consumption (FFQ) tends to overestimate energy intake, because it includes a large quantity of food items, thus generating a greater sum of energies compared, for example, to food recalls(Reference Willett40). The FFQ used in the Pelotas Birth Cohorts was developed specifically to evaluate the studied population, which reflects the contexts of foods and preparations commonly reported by this population(Reference Schneider, dos Santos Motta and Muniz17). However, the processing degree was not evaluated, which made it impossible to obtain additional information about food preparation. For this reason, the food items from the FFQ were classified in a more conservative way, with culinary preparations such as lasagne, for example, considered as minimally processed foods; and breads (whole or white) classified as processed foods (see online supplementary material, Supplementary Table). The few foods that differ between the FFQ used in the three cohorts have a very low percentage of contribution in relation to total energy consumption (<0·75 % on average; see online supplementary material, Supplementary Table). In addition, the use of percentage of contribution of each processing group aimed to minimise overestimation by representing a measure relative to total energy consumption. The classification of foods according to their processing degree (NOVA) is recent, but its concepts are well established in the literature and recognised by international agencies (Pan-American Health Organization and the UN Organization for Food and Agriculture – FAO) as an important instrument to evaluate the nutritional quality of diets(8,41) .
Regarding the losses of follow-up, some differences were observed between those followed and those lost for the three cohorts. In the 11- and 30-year follow-ups, higher losses were observed between the extreme categories of maternal schooling and family income; therefore, some possible bias in the overall estimate regarding energy contribution from ultra-processed foods may have occurred. In the 22- and 30-year follow-ups, a higher proportion of women was evaluated compared to men. Considering this differential loss, the overall proportion of outcome in the 1993 and 1982 Cohorts could be lower than that observed. Also, in the 2004 Cohort, a higher proportion of losses was observed among children of younger mothers (≤24 years); however, maternal age was not associated with outcome in any of the cohorts, considering that CIs are overlapping, and therefore, this differential loss may not interfere in the overall proportion of outcome at 11 years.
Among the study strengths, the calculation of energy contribution of each processing group was adjusted for the variables ‘diet for weight loss’ and ‘energy expenditure/consumption ratio’. Individuals who diet for weight loss tend to present information bias, with underestimated consumption(Reference Briefel, Sempos and McDowell42,43) . Energy consumption/expenditure ratio was included in the adjustment variables as an alternative to the exclusion of outlier values from the FFQ report, aiming to avoid sample losses. Furthermore, the use of data from the three birth cohorts – representative of individuals of the same age groups in medium-sized municipalities who applied the same eligibility criteria in their samples and same fieldwork logistics – represents another strength of this study. Additionally, considering follow-up studies of this size, high response rates were obtained in the three cohorts.
In this study, the contribution of ultra-processed foods to total daily energy consumption was higher among younger cohorts. Maternal schooling was associated to the consumption of these foods in the three cohorts, indicating an inverse dose–response relationship in the 2004 and 1993 Cohorts, and a direct relationship in the 1982 Cohort. These findings indicate that information from nutrition public policies requires greater dissemination among population groups with lower schooling and income, mainly girls and women. Other measures aimed at promoting healthy eating, such as regulation of food advertising focused on children and adolescents and promotion of the Dietary Guidelines for the Brazilian Population(6) by the media, are extremely necessary. Along with that, changes in food taxation, with decreasing costs of unprocessed and minimally processed products and increasing costs of ultra-processed products, represent important strategies to promote the reduction of consumption of these foods in our country.
Acknowledgements: This study was conducted in a post-graduate programme supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brazil (CAPES) – Finance Code 001; and by the Conselho Nacional de Desenvolvimento Científico e Tecnológico – Brazil (CNPq). Financial support: This work was supported by the Wellcome Trust (grant number 086974/Z/08/Z) at the 30-year follow-up for the 1982 Cohort, and by DECIT (Departamento de Ciência e Tecnologia) and the Conselho Nacional de Desenvolvimento Científico e Tecnológico- Brazil CNPq (grant number 400943/2013–1) at the 22- and 11-year follow-ups for the 1993 and 2004 Cohorts, respectively. Conflict of interest: None. Authorship: C.S.C., M.C.F.A. and I.S.S. made substantial contribution to the conception and design, data acquisition, analysis and interpretation; drafted the article and revised it critically for important intellectual content; and approved the final version of manuscript to be published. J.S.V., F.R., I.O.B., A.M., B.L.H., H.G. and F.C.W. made substantial contribution to the analysis and interpretation; revised the article critically for important intellectual content; and approved the final version of manuscript to be published. Ethics of human subject participation: This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving study participants were approved by the Ethics Committee of the Faculty of Medicine of the Federal University of Pelotas (889 753, 1 250 366 and 16/12, respectively, for the 2004, 1993 and 1982 Cohorts). Written informed consent was obtained from all subjects/patients. Participants of the 1993 and 1982 Cohorts signed a consent term. In the 2004 Cohort, the mother or legal guardian signed the consent term; and the adolescent, the assent term.
For supplementary material accompanying this paper visit https://doi.org/10.1017/S1368980019004245