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Revista Brasileira de Epidemiologia

Print version ISSN 1415-790XOn-line version ISSN 1980-5497

Rev. bras. epidemiol. vol.23  Rio de Janeiro  2020  Epub Feb 21, 2020

https://doi.org/10.1590/1980-549720200003 

ORIGINAL ARTICLE

Demographic, socioeconomic and lifestyle factors associated with sugar-sweetened beverage intake: a population-based study

Fatores demográficos, socioeconômicos e de estilo de vida associados ao consumo de bebidas açucaradas: um estudo de base populacional

Amanda Silva FontesI 
http://orcid.org/0000-0001-6816-2645

Ana Carolina PallottiniI 
http://orcid.org/0000-0002-6672-8227

Diva Aliete dos Santos VieiraII 
http://orcid.org/0000-0003-4080-3425

Mariane de Mello FontanelliI 
http://orcid.org/0000-0002-3480-6948

Dirce Maria MarchioniI 
http://orcid.org/0000-0002-6810-5779

Chester Luiz Galvão CesarIII 
http://orcid.org/0000-0002-6999-1506

Maria Cecilia Goi Porto AlvesIV 
http://orcid.org/0000-0003-4357-9473

Moisés GoldbaumV 
http://orcid.org/0000-0002-8049-7824

Regina Mara FisbergI 
http://orcid.org/0000-0002-4490-9035

IDepartment of Nutrition, School of Public Health, Universidade de São Paulo - São Paulo (SP), Brazil.

IICenter of Biological and Health Sciences, Department of Nutrition, Universidade Federal de Sergipe - Aracaju (SE), Brazil.

IIIDepartment of Epidemiology, School of Public Health, Universidade de São Paulo - São Paulo (SP), Brazil.

IVInstitute of Health, Department of Health of the State of São Paulo - São Paulo (SP), Brazil.

VDepartment of Preventive Medicine, Medicine School, Universidade de São Paulo - São Paulo (SP), Brazil.


ABSTRACT:

Introduction:

The intake of sugar-sweetened beverages (SSB) varies according to the characteristics of the population.

Objective:

To investigate the SSB intake and demographic, socioeconomic and lifestyle factors associated with its consumption in adolescents, adults, and older adults in São Paulo.

Methods:

Data were drawn from the Health Survey of São Paulo, a cross-sectional population-based study including 1,662 individuals aged 12 years or more. SSB were classified into six groups: sugar-sweetened sodas, sweetened coffee and tea, sweetened milk and dairy products, sweetened fruit juice, sweetened fruit drink, and total SSB. The association of each group with demographic, socioeconomic and lifestyle variables was assessed using linear regression models.

Results:

The mean SSB intake was 668.4 mL in adolescents, 502.6 mL in adults, and 358.2 mL in elderly adults. Sodas and sweetened coffee and tea represented had the greatest contribution to energy intake. SSB consumption was lower among female sex and higher among overweight adolescents, among sufficiently active adults, and among lower household per capita income older adults. Consumption of SSB was high, particularly among adolescents. Public policies are required in order to decrease the consumption of these beverages.

Conclusion:

Age group, sex, household per capita income, and body mass index status were associated with SSB intake.

Keywords: Nutrition surveys; Beverages; Life style; Socioeconomic factors

RESUMO:

Introdução:

A ingestão de bebidas açucaradas varia de acordo com as características da população.

Objetivos:

Investigar o consumo de bebidas açucaradas e os fatores demográficos, socioeconômicos e de estilo de vida associados ao seu consumo em adolescentes, adultos e idosos residentes em São Paulo.

Métodos:

Foram utilizados dados do Inquérito de Saúde de São Paulo, estudo transversal de base populacional, incluindo 1.662 indivíduos com 12 anos ou mais. As bebidas açucaradas foram classificadas em seis grupos: refrigerantes, cafés e chás adoçados, leite e produtos lácteos adoçados, sucos de fruta natural adoçados, sucos de fruta artificial adoçados e bebidas açucaradas totais. A associação de cada grupo com variáveis demográficas, socioeconômicas e de estilo de vida foi determinada por meio de modelos de regressão linear.

Resultados:

A ingestão média de bebidas açucaradas foi 668,4 mL em adolescentes, 502,6 mL em adultos e 358,2 mL em idosos. Refrigerantes e cafés e chás adoçados foram os grupos com a maior contribuição para a ingestão energética. O consumo de bebidas açucaradas foi menor entre as mulheres e maior entre os adolescentes com excesso de peso, entre adultos suficientemente ativos e entre os idosos de menor renda familiar per capita. O consumo de bebidas açucaradas foi elevado, particularmente entre adolescentes. Políticas públicas são necessárias a fim de reduzir o consumo dessas bebidas.

Conclusão:

Faixa etária, sexo, renda familiar per capita e índice de massa corporal foram associadas ao consumo de bebidas açucaradas.

Palavras-chave: Inquéritos nutricionais; Bebidas; Estilo de vida; Fatores socioeconômicos

INTRODUCTION

Intake of sugar-sweetened beverages (SSB) has been associated with the development of excess body weight and cardiometabolic diseases1. A study that evaluated global burdens of diseases associated with SSB consumption in 2010 found that 8.5 million disability-adjusted life years and 184,000 deaths worldwide were attributable to SSB intake. The majority of these burdens occurred in low- and middle-income countries, with a substantial morbidity and mortality rates in Latin America and the Caribbean2.

Studies have shown that the intake of SSB varies according to the characteristics of the population3,4,5. Global SSB consumption in adults was estimated to be 137.2 mL/day in a study performed with data from 187 countries, with the highest consumption among men and younger adults. The study also demonstrated that the middle-income countries had higher SSB consumption when compared to high- or lower-income countries. The average SSB consumption in upper-middle income countries was 189.3 mL/day6.

Most of the energy from SSB is from added/free sugar and there is a close alignment between SSB and sugar nutritional recommendations7. Data from a cross-sectional population-based study performed in eight Latin American countries indicated that approximately 70% of all individuals had added sugar intake above recommended levels, oscillating from 49.1% in Ecuador to 79.6% in Costa Rica. This number increased to 90% when more restrictive recommendations were applied, from 90.3% in Brazil to 97.9% in Peru8. The same study showed that added sugar intake was higher in women, decrease with advancing age, and, in general, was no different between socioeconomic levels8.

Beverage intake corresponded to 17% of total daily energy in Brazilians aged 10 years or more in 2008-20099. However, a downward trend in the intake of soft drinks and artificial juices in adults living in Brazilian capitals and Federal District from 2009 to 2016 was reported by the Surveillance System of Risk and Protection Factors for Chronic Diseases by Telephone Survey (VIGITEL). The reduction was especially seen in those aged between 18 and 44 years, with more than 12 years of study, with a partner, and residents of more developed regions10. Despite these significant findings, it is important to be aware of the limitations of the questionnaire used to collect dietary data in this survey, as well as the fact that the beverages investigated does not include all SSB, as addressed by the authors10.

Clustering of health behaviors have been described in the literature and may explain part of the association between SSB intake and the development of cardiometabolic diseases11. Studies indicate that a higher consumption of SSB is associated with reduced physical activity, current smoker status, increased consumption of alcoholic beverages, and lower adherence to the Mediterranean dietary pattern12,13, suggesting SSB intake may be a marker of an unhealthy lifestyle.

From this perspective, it is important to be aware of the quantity and types of SSB consumed, as well as of the characteristics associated with this consumption. The aims of the present study are to investigate SSB consumption and evaluate demographic, socioeconomic, and lifestyle factors associated with SSB consumption in adolescents, adults, and older adults living in São Paulo, Brazil.

METHODOLOGY

STUDY SAMPLE

Data for this study were obtained from the 2008 Health Survey of São Paulo (2008 HSP). This is a cross-sectional population-based study including a probabilistic sample of individuals living in permanent households, in the urban area of São Paulo.

A two-stage cluster sampling of census tracts and households was performed. Census tracts constituted the primary sampling units and households, the secondary. In the first stage, 70 census tracts were randomly selected among the 267 census tracts of the urban area of São Paulo included in the 2005 Continuous National Household Sample Survey 2005. For the second stage, 16,607 private households were randomly selected, within census tracts. Eight domains were fixed according to age and sex: children < 1 year; children 1-11 years; male adolescents 12-19 years; female adolescents 12-19 years; male adults 20-59 years; female adults 20-59 years; older male adults ≥ 60 years; and older female adults ≥ 60 years.

For this study, only data from adolescents, adults, and older adults were used. The 2008 HSP sample was composed of 2,691 individuals aged 12 years or over (605 adolescents, 1,162 adults and 924 older adults). For information on food consumption data, a subsample of 900 participants was estimated (300 individuals per age group). This sample size was calculated to estimate proportions of 0.5 with a sample error of 0.07 at a 5% significance level and design effect of 1.5. To compensate for possible losses, 25% more individuals were drawn. The first 24-hour dietary recall (24HR) was collected in 1,662 individuals aged 12 years or over (560 adolescents, 585 adults, and 517 older adults). The second 24HR was collected in 832 subjects (232 adolescents, 302 adults, and 298 older adults). This study used information from 1,662 individuals with demographic, socioeconomic and lifestyle information and first measure of the 24HR, and 1,582 with complete data were included in the regression models.

The Ethics Committee of the São Paulo Municipal Health Department and the Public Health School of University of São Paulo approved the study (CAAE No. 26800414.1.0000.5421). All participants were enrolled in the study after providing free and written informed consent.

DATA COLLECTING AND PROCESSING

Between 2008 and 2009, a home visit was performed to collect demographic, socioeconomic, and lifestyle data using a structured questionnaire. This study will use the following demographic, socioeconomic, and lifestyle parameters: sex, age, self-reported skin color, level of education of the household head, household per capita income, smoking status, alcohol consumption, physical activity level, weight, and height.

For the classification of the age group (adolescents, adults and older adults), the definitions of the World Health Organization and the Brazilian Child and Adolescent Statute were considered. Self-reported skin color was classified as “white and yellow” and “black and brown”. The level of education of the household head was measured in years of study and classified as ≤ nine years and ≥ ten years of study. Household per capita income was calculated using the sum of the income of all family members, divided by the number of family members, and it was classified as ≤ 1 minimum wage and > 1 minimum wage (R$ 415.00 or US$ 260.00 in 2008). Smoking status was classified as never, current, and former smoker. Individuals were classified as consumer or no consumer of alcoholic beverages.

Physical activity was collected by the long version of the International Physical Activity Questionnaire validated for the Brazilian population14. Leisure time physical activity was classified as insufficiently active or sufficiently active (physical activity practiced for at least 30 minutes daily, 5 days per week, at a moderate intensity or at least 20 minutes daily, 3 days per week, at a vigorous intensity).

Body mass index (BMI) was calculated on the basis of self-reported weight and height and classified according to the cut-off for adolescents, adults and older adults15,16,17. This information was previously validated in the studied population18.

DIETARY INTAKE DATA

Food consumption data were collected using two 24HR. The median time between the two 24HR measures was 184 days. Data were collected on non-consecutive days, representing every day of the week and all seasons of the year. The first 24HR data were collected during the first home visit, and the second 24HR was conducted by telephone, using the Automated Multiple Pass Method19. The Nutrition Data System for Research (NDSR) software, 2007 version, was used for data collection. The main source of NDSR data is the food composition table developed by the United States Department of Agriculture, therefore, foods typed in every inquiry have nutritional value compared to the values available in national tables20,21,22.

The equations used to calculate the estimated energy requirements (EER) on an individual basis were obtained from the Institute of Medicine of the National Academies23. The EER is the average dietary energy intake required to preserve energy balance according to age, sex, weight and physical activity level23. In an individual with stable body weight, the dietary energy intake is compatible with the EER, therefore it is possible to estimate and account for misreporting, a common error of self-reported dietary intake24. The misreporting percentage of each individual energy needs was determined by: energy intake - EER / EER × 10025.

SUGAR-SWEETENED BEVERAGES

SSB were defined as any beverages containing a caloric sweetener, either intrinsic to the product formulation or added before consumption26. If the respondent reported adding sugar to the consumed beverage, this quantity was also taken into account. When there was no information available regarding the quantity of sugar added before beverage consumption in the 24HR, a further 5 g of sugar for each 100 mL of beverage were considered.

All SSB cited in the 24HR were classified into six groups:

  • sugar-sweetened sodas (soft drinks with added sugar);

  • sweetened coffee and tea (e.g. coffee, coffee and milk, cappuccino and tea with sugar added);

  • sweetened milk and dairy products (e.g. milk, milk with chocolate, yogurt with sugar added);

  • sweetened fruit juice (natural fruit juices with sugar added before consumption);

  • sweetened fruit drink (e.g. dry-mix fruit drinks, nectar and drinks beverages, sports drinks with added sugar);

  • total SSB (sum of all categories).

SSB consumed by < 5% of the population or not nutritionally similar to any group were excluded from the analysis; these included sugar sweetened alcoholic beverages, beverages with artificial sweeteners, and soy-based beverages.

The usual SSB intake was estimated using the Multiple Source method, which combines the probability and the amount of food consumed to estimate the usual intake for each individual27. To ensure the accuracy of the estimates, it is required to have two short-come measurements of dietary intake for at least 40% of the population studied28. For this study, the replication rate was 55.69%.

STATISTICAL ANALYSES

The characteristics of the subjects were presented as percentages and 95% confidence interval. Intake of SSB was described according to the daily amount consumed and the proportion of total daily energy intake. The association between each SSB group and total SSB intakes, respectively, and demographic, socioeconomic, and lifestyle variables was calculated using linear regression models for each age group. All the models had the following factors as independent variables: sex, physical activity, smoking status, household per capita income, BMI, self-reported skin color, and alcohol consumption. All the models were adjusted for misreporting and were accepted after residual analysis. The inclusion of the variables was based on references, and on the univariate linear regression models3,4,5. Variables not mentioned in the literature, but with the p < 0.20 were considered for inclusion on the linear multiple regression models. The inclusion process was performed from the lowest to the highest p-value. All analyses considered the complexity of the sample design by using the survey module of the STATA® (Statistics/Data Analysis, version 13.0, Texas, 115 USA).

RESULTS

The characteristics of the participants of the study are presented in Table 1. The subjects were predominantly female, self-reported white and yellow skin colored, non-smokers, and alcohol consumers. The majority of participants head of the family had 10 or more years of education and household per capita income higher than 1 minimum wage. Excess body weight was observed in 43.1% of subjects and 85.6% were insufficiently active.

Table 1. Characteristics of the participants in the Health Survey of São Paulo 2008 according to age group. São Paulo, 2008. 

Characteristics Adolescents Adults Older adults Total
% 95%CI % 95%CI % 95%CI % 95%CI
Sex
Male 50.1 45.3 - 54.9 46.9 43.3 - 50.6 40.5 36.6 - 44.5 46.5 43.9 - 49.2
Female 49.9 45.1 - 54.7 53.1 49.5 - 56.7 59.5 55.5 - 63.4 53.5 50.8 - 56.1
Skin color
White and yellow 56.8 49.3 - 63.9 58.3 52.7 - 63.7 70.9 65.2 - 76.0 59.8 54.7 - 64.8
Black and brown 43.2 36.1 - 50.7 41.7 36.3 - 47.3 29.1 24.0 - 34.8 40.2 35.2 - 45.3
Smoking status
Non-smoker 93.1 90.7 - 94.9 59.5 54.4 - 64.3 56.6 51.8 - 61.3 64.2 60.4 - 67.7
Former smoker 2.6 1.5 - 4.2 15.5 12.3 - 19.5 33.2 29.0 - 37.8 16.0 13.5 - 19.0
Current smoker 4.3 2.9 - 6.4 25.0 20.5 - 30.0 10.2 7.4 - 13.7 19.8 16.6 - 23.4
Alcohol consumption
Non-consumer 66.5 61.3 - 71.4 41.8 38.2 - 45.4 62.7 57.8 - 67.4 48.4 45.3 - 51.5
Consumer 33.5 28.6 - 38.7 58.2 54.6 - 61.8 37.3 32.6 - 42.2 51.6 48.5 - 54.7
Education of the head of the family
≤ 9 years 45.7 39.3 - 52.3 43.4 36.1 - 51.1 65.1 58.7 - 71.0 46.8 41.0 - 52.7
≥ 10 years 54.3 47.7 - 60.7 56.6 48.9 - 63.9 34.9 29.0 - 41.3 53.2 47.3 - 59.0
Household per capita income
≤ 1 minimum wage 60.9 53.6 - 67.7 39.5 33.4 - 46.0 44.7 37.3 - 52.3 43.5 38.3 - 48.8
> 1 minimum wage 39.1 32.3 - 46.4 60.5 54.0 - 66.6 55.3 47.7 - 62.8 56.5 51.3 - 61.7
Body mass index
Without EBW 70.7 65.8 - 75.2 52.6 48.1 - 57.0 63.6 59.7 - 67.4 56.9 53.3 - 60.3
With EBW 29.3 24.8 - 34.2 47.4 43.0 - 51.9 36.4 32.6 - 40.3 43.1 39.7 - 46.7
Physical activity
Insufficiently active 75.1 69.9 - 79.7 87.1 83.4 - 90.0 89.6 86.5 - 92.1 85.6 82.9 - 87.9
Sufficiently active 24.9 20.3 - 30.1 12,9 10.0 - 16.6 10.4 7.9 - 13.5 14.4 12.1 - 17.1

CI: confidence interval; EBW: excess body weight.

Total consumption of SSB was greater among adolescents, and the most consumed beverages were sugar-sweetened sodas (Table 2). Adolescents also presented higher intake of sweetened milk and dairy products and sweetened fruit drinks. Total daily energy intake from SSB varied from 10.4 to 14.5% in older adults and adolescents, respectively.

Table 2. Sugar-sweetened beverage intake according to amount (mL/day) and proportion (%) of total daily energy intake among adolescents, adults and older adults. São Paulo, 2008. 

Beverages group

  • mL/d

  • Mean

  • (standard deviation)

  • % total daily energy

  • Mean

  • (standard deviation)

Total Adolescents Adults Older adults Total Adolescents Adults Older adults
Sugar-sweetened sodas 163.6 (172.8) 263.9 (216.3) 157.0 (164.7) 87.3 (92.8) 3.8 (3.7) 5.3 (4.1) 3.7 (3.7) 2.6 (2.6)
Sweetened coffee and tea 164.4 (151.0) 115.6 (133.1) 174.3 (152.4) 166.8 (153.0) 3.6 (3.6) 2.2 (2.7) 3.7 (3.5) 4.5 (4.5)
Sweetened milk and dairy products 56.2 (99.9) 101.0 (129.0) 51.1 (96.0) 32.9 (61.9) 2.6 (4.3) 4.3 (5.4) 2.4 (4.2) 1.9 (3.4)
Sweetened fruit juice 29.8 (69.1) 43.9 (104.6) 28.8 (63.0) 19.4 (43.13) 0.61 (1.3) 0.7 (1.5) 0.6 (1.2) 0.5 (1.2)
Sweetened fruit drinks 87.3 (116.8) 123.4 (138.0) 87.3 (116.2) 47.6 (75.0) 1.1 (1.6) 1.5 (2.0) 1.1 (1.6) 0.7 (1.1)
Total sugar-sweetened beverages 507.7 (262.1) 668.4 (280.2) 502.6 (250.0) 358.2 (198.2) 11.9 (6.3) 14.5 (6.3) 11.7 (6.2) 10.4 (6.4)

Table 3 shows the association between each SSB group and demographic, socioeconomic, and lifestyle variables in adolescents, adults and older adults. In adolescents, female sex participants presented lower mean intake of sugar-sweetened soda and total SSB intake when compared with male sex. Adolescents who self-reported black and brown skin color presented higher mean intake of sweetened coffee and tea. Household per capita family income of participants in this age group was negatively associated with the intake of sweetened coffee and tea and sweetened fruit drink, and positively associated with the intake of sweetened fruit juice. Adolescents with excess body weight presented higher intake of sugar-sweetened soda and total SSB when compared with subjects at the same age group without excess body weight.

Table 3. Demographic, socioeconomic, and lifestyle variables associated with sugar-sweetened beverage intake in adolescents, adults and older adults. São Paulo, 2008. 

Variables Sugar-sweetened sodas Sweetened coffee and tea Sweetened milk and dairy products Sweetened fruit juice Sweetened fruit drink Total sugar-sweetened beverages
β p β p β p β p β p β p
Adolescents
Sex (ref. male)
Female -75.9 0.001 -21.6 0.100 -11.4 0.436 12.0 0.169 -22.9 0.097 -141.1 0.000
Skin color (ref. white and yellow)
Black and brown -7.4 0.726 35.1 0.011 -21.9 0.096 5.7 0.534 17.6 0.173 35.8 0.075
Smoking status (ref. non-smoker)
Former smoker 144.3 0.056 -40.5 0.084 57.8 0.201 11.4 0.690 -48.1 0.095 161.1 0.086
Current smoker 6.4 0.878 38.3 0.117 -10.6 0.628 5.62 0.833 -13.7 0.548 16.2 0.717
Alcohol consumption (ref. non-consumer)
Consumer -13.4 0.468 11.6 0.325 -12.8 0.274 4.4 0.630 8.9 0.480 -12.4 0.640
Household per capita income (US$ per month) 0.4 0.792 -1.3 0.019 -0.0 0.945 3.2 0.027 -1.9 0.000 1.2 0.343
Body Mass Index (ref. without excess body weight)
With excess body weight 62.7 0.006 -11.7 0.461 22.3 0.155 17.8 0.148 -6.9 0.743 103.2 0.001
Physical activity (ref. Insufficiently active)
Sufficiently active 40.8 0.067 -12.0 0.378 21.8 0.161 -14.1 0.072 5.1 0.692 41.5 0.098
Adults
Sex (ref. male)
Female -33.3 0.031 5.2 0.673 -9.7 0.354 1.8 0.770 -14.7 0.153 -61.2 0.014
Skin color (ref. white and yellow)
Black and brown 29.1 0.086 0.95 0.945 -18.2 0.050 1.4 0.843 15.8 0.130 30.4 0.096
Smoking status (ref. non-smoker)
Former smoker -12.2 0.507 13.1 0.287 -25.3 0.025 1.5 0.836 -16.8 0.185 -34.3 0.259
Current smoker 15.2 0.412 26.3 0.125 -25.1 0.037 -5.9 0.280 -19.6 0.238 -9.0 0.757
Alcohol consumption (ref. non-consumer)
Consumer -5.3 0.060 -9.6 0.518 9.4 0.365 -0.8 0.890 9.0 0.428 14.7 0.599
Household per capita income (US$ per month) -0.6 0.413 -1.6 0.102 0.6 0.265 -0.3 0.092 0.1 0.905 -1.7 0.171
Body Mass Index (ref. without excess body weight)
With excess body weight 33.4 0.060 -19.8 0.227 -10.4 0.283 6.3 0.308 -2.5 0.790 4.0 0.841
Physical activity (ref. Insufficiently active)
Sufficiently active 2.5 0.926 -7.0 0.741 48.4 0.020 5.4 0.586 28.8 0.143 94.1 0.027
Older Adults
Sex (ref. male)
Female -17.8 0.101 2.6 0.865 -2.3 0.679 6.2 0.025 -12.3 0.052 -27.8 0.160
Skin color (ref. white and yellow)
Black and brown -6.9 0.469 -8.1 0.518 -2.5 0.733 1.3 0.756 5.8 0.500 -20.4 0.208
Smoking status (ref. non-smoker)
Former smoker 5.6 0.621 -22.8 0.141 -8.2 0.240 0.2 0.949 10.7 0.217 -19.1 0.348
Current smoker -1.4 0.931 -7.2 0.735 -14.8 0.110 -2.1 0.676 -1.0 0.939 -30.6 0.245
Alcohol consumption (ref. non-consumer)
Consumer -4.1 0.641 3.3 0.798 -4.1 0.555 8.8 0.044 -8.0 0.337 7.2 0.687
Household per capita income (US$ per month) -0.3 0.341 -1.0 0.017 -0.2 0.314 -0.4 0.002 -0.4 0.059 -2.4 0.001
Body Mass Index (ref. without excess body weight)
With excess body weight 16.2 0.120 -26.2 0.109 -3.3 0.599 0.9 0.811 17.2 0.036 4.4 0.832
Physical activity (ref. Insufficiently active)
Sufficiently active -0.54 0.974 8.6 0.747 -2.0 0.826 4.8 0.366 2.2 0.817 7.8 0.810

The values of family income per capita were divided per R$ 100 (US$57.8 in 2008). Significant associations are indicated in bold. All the models were adjusted for misreporting.

In adults, female sex participants presented lower mean intake of sugar-sweetened soda, sweetened milk and dairy products and total SSB when compared with male sex. Adults who are former or current smokers presented lower intake of sweetened milk and dairy products when compared to non-smokers. Sufficiently active adults presented higher mean intake of sweetened milk and dairy products and total SSB when compared to insufficiently active ones (Table 3).

In older adults, female sex participants and alcohol consumers presented higher mean intake of sweetened fruit juice when compared to male sex and non-consumers, respectively. Older adults with excess body weight had higher intake of sweetened fruit drink compared to older adults without excesses body weight. Household per capita income was negatively associated with the intake of sweetened coffee and tea, sweetened fruit juices and total SSB intake (Table 3).

DISCUSSION

In the present study, the consumption of SSB was elevated among all age groups, particularly among adolescents. Similar or even smaller portions of SSB have been associated with the development of excess body weight, type 2 diabetes mellitus, and metabolic syndrome in previous studies29,30,31. Sugar-sweetened soda and sweetened coffee and tea represented the beverages with the greatest total energy intake. Our analysis demonstrated that SSB intake was associated with different demographic, socioeconomic, and lifestyle factors, according to life stage and group of SSB.

Adolescents had the highest SSB consumption, representing 14.5% of total daily energy intake. Data from a national survey evaluating beverages consumption showed that the overall contribution of beverages to total daily energy intake decreased with age9. A cross-sectional study involving Mexican adolescents found that SSB consumption contributed to 16.8% of total daily energy intake32. Corroborating these findings, data derived from 187 countries, involving individuals aged ≥ 20 years, indicated that global SSB consumption was higher in younger adults6. However, it is not clear whether this elevated intake among younger subjects is an effect of aging or if recent generations consume more than the older ones2.

Sugar-sweetened soda was one of the most consumed beverages, particularly among adolescents. Nationally, a previous study showed that individuals at younger ages, 10 to 39 years, consumed a higher proportion of energy from soft drinks than older ages9. Many factors may be associated with a higher SSB intake among adolescents: practical to consume, increase in portion size, and low cost33,34,35. Stuckler et al. observed that there is a rapid increase in the consumption of sugar-sweetened soda and processed foods in low- and middle-income countries, compared to high-income countries36. The advertisement of SSB aimed at children and adolescents has increased in these countries, influencing food preferences, purchase requests, and dietary patterns, as young persons are unable to understand the persuasive intent of sale of these products37.

For the most of the SSB, female sex was associated with lower SSB intake. Similar results were also observed in several countries6,29,38. A possible explanation for the different consumption according to sex is that female subjects generally require a lower energy intake than male subjects, due to smaller average body weight and lower resting metabolic rate39. Other reasons include an increased concern with appearance and a greater health consciousness40,41.

The association between household income and SSB presented varied patterns. In older adults and adolescents, household per capita income was negatively associated with the intake sweetened coffee and tea. A previous study with a national representative sample of Brazil also found that individuals with higher income had decreased sweetened coffee intake9. In older adults, the mean intake of sweetened fruit juices and total SSB was negatively associated with household per capita income, while in adolescents’ age group, income was negatively associated with the intake of sweetened fruit drink, and positively associated with the intake of sweetened fruit juice. The association between low socioeconomic status and higher SSB consumption was seen in a cross-sectional study of the population ≥ 2 years old in the United States42. Different factors may explain this association, in general, foods with poor nutritional value cost less per calorie and tend to be selected by groups of lower socioeconomic status43. Moreover, individuals of a higher socioeconomic level have additional nutritional knowledge and information about prevention in nutrition, and favor the choice of healthy foods43.

In the present study, sufficiently active adults had higher intake of sweetened milk and dairy products compared to insufficient active adults, while adult’s former and current smokers presented lower intake of sweetened milk and dairy products when compared to non-smokers. Dairy products have been associated with lower risk of major causes of cardiovascular diseases, and total mortality in a cohort study of individuals aged 35-70 years from 21 countries and are considered a healthy food item in many dietary indexes44,45,46. From this perspective, the intake dairy consumption, even with added sugar, may represent a marker of healthy lifestyle habit.

Excess body weight was associated with higher sugar-sweetened soda intake and total SSB intake among adolescents, and these results were also seen in adolescents from different countries47,48. Studies have indicated a lower satiety effect with carbohydrate intake from liquids when compared to carbohydrate intake from solids, resulting in an increased daily energy intake and, consequently, increased susceptibility to obesity12,49.

Besides obesity, SSB intake is associated with cardiovascular disease and type 2 diabetes1. Systematic review of cohort studies suggested that no intake of SSB would be the optimal intake level for reducing cardiometabolic diseases1.

In addition to health adverse effects, Lieffers et al. observed that excess intakes of SSB had $ 382.8 million direct health care costs and $ 480.4 million indirect costs in Canada. Altogether, the evidences point to the impact of food choices in the economic burden of cardiometabolic diseases and the importance of reducing the consumption of SSB at the population level to reduce health costs as well as to prevent cardiometabolic diseases50.

Some limitations of the present study should be considered. This is a cross-sectional study, and, therefore, we cannot determine causality between the factors evaluated and SSB intake. The 24HR is a source of errors for the estimation of dietary intake, specifically recall errors, omissions, and errors in portion size estimation. However, in order to minimize bias, dietary data were collected by trained interviewers using a standardized method, usual intake was estimated by using two 24HR and the Multiple Source method.

A further limitation was the different definition of SSB in other studies also evaluating SSB intake, as this made comparisons between studies challenging. The time lag of the data collection is another limitation, since the data was collected in 2008. However, it does not diminish the findings of this study considering that few researches in national literature investigated the same research question in a population-based study10. In addition, this investigation used data from a representative sample of one of the world’s largest cities that is an important financial center of South America, and a migrant destination for different parts of Brazil and other countries.

Planning of public health policies aimed at decreasing SSB consumption is essential to reduce the incidence of cardiometabolic diseases. A number of strategies may contribute to greater awareness of reducing SSB consumption among the population, such as taxation of SSB, food marketing regulation, control of SSB sales in schools, and setting product labelling standards to facilitate the prompt identification of unhealthy foods51,52. Our findings may be used to improve targeted public health nutrition policies aimed at decreasing SSB consumption.

CONCLUSION

Our results indicate that age group, sex, household per capita income, and BMI status were characteristics associated with higher intake of SSB.

ACKNOWLEDGMENTS

We would like to thank all ISA-Capital staff and the researchers of the Evaluation of Food Consumption Research Group (GAC - Grupo de Pesquisa de Avaliação do Consumo Alimentar).

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Financial support: São Paulo Municipal Health Department (no grant number), the National Council for Scientific and Technological Development (grant numbers 481176/2008-0 and 473100/2009-6) and the São Paulo Research Foundation (grant number 2009/15831-0 and 2012/22113-9).

Received: October 18, 2018; Revised: November 30, 2018; Accepted: November 30, 2018

Corresponding author: Regina Mara Fisberg. Avenida Dr. Arnaldo, 715, Cerqueira Cesar, CEP: 01246-904, São Paulo, SP, Brazil. E-mail: rfisberg@usp.br

Conflict of interests: nothing to declare

Authors’ contribution: ASF contributed with analysis, interpretation of data and wrote the manuscript. ACP, DASV and MMF contributed with analysis, interpretation of data and critically revising it for important intellectual content. CLGC, MCGPA, MG e DMM were involved in the conception and design of the study and in critically revising the manuscript for important intellectual content. RMF was involved in the conception and design of the study, data interpretation, and critically revising the manuscript for important intellectual content. All authors read and approved the submitted version.

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