Temporal trend of overweight and obesity prevalence among Brazilian adults, according to sociodemographic characteristics, 2006-2019

Silva, Luiza Eunice Sá da; Oliveira, Max Moura de; Stopa, Sheila Rizzato; Gouvea, Ellen de Cássia Dutra Pozzetti; Ferreira, Kauara Rodrigues Dias; Santos, Roberta de Oliveira; Valença Neto, Paulo da Fonseca; Macário, Eduardo Marques; Sardinha, Luciana Monteiro Vasconcelos

doi:10.1590/s1679-49742021000100008

Abstract

Objetivo

To analyze the temporal trend of overweight and obesity prevalence rates among adults in the Brazilian state capitals and Federal District between 2006 and 2019.

Methods

This was a time series study using data from the Surveillance System for Risk and Protective Factors for Chronic Diseases by Telephone Survey, 2006-2019 (n=730,309). Prevalence of overweight and obesity for each of the years was analyzed, according to combined sex, age, and schooling. Temporal variation trend was analyzed using Prais-Winsten regression.

Results

Variations in overweight prevalence were observed, mainly among males 18-24 years old with up to 8 years of schooling (3.17%/year) and among women between 18-24 years old with more than 12 or more years of schooling (6.81% /year). Variations in obesity prevalence were found mainly among women 18-24 years old with more than 12 years of schooling (10.79%/year).

Conclusion

There was an increase in overweight and obesity in most of the socio-demographic strata studied, especially among more educated young people.

Keywords:
Body Mass Index; Obesity; Health Surveys; Health Status Disparities

Introduction

Overweight and obesity are among the principal factors affecting the global morbidity burden.¹1. World Health Organization - WHO. Obesity and overweight [Internet]. Genebra: World Health Organization; 2020 [cited 2020 Jan 15]. Available from: https://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight /
https://www.who.int/en/news-room/fact-sh... In 2016, global data showed that 39% of adults were overweight and 13% were obese.¹1. World Health Organization - WHO. Obesity and overweight [Internet]. Genebra: World Health Organization; 2020 [cited 2020 Jan 15]. Available from: https://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight /
https://www.who.int/en/news-room/fact-sh... In the period between 1975 and 2016, body mass index (BMI) trends worldwide highlighted the growing evolution of these conditions among adults.²2. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet [Internet]. 2017 Dec [cited 2020 Oct 5];390(10113):2627-42. Available from: https://doi.org/10.1016/S0140-6736(17)32129-3
https://doi.org/10.1016/S0140-6736(17)32... Initially a problem only in high-income countries, since the 2000s overweigth and obesity have been growing faster in low- and middle-income countries.²2. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet [Internet]. 2017 Dec [cited 2020 Oct 5];390(10113):2627-42. Available from: https://doi.org/10.1016/S0140-6736(17)32129-3
https://doi.org/10.1016/S0140-6736(17)32... ³3. Seidell JC, Halberstadt J. The global burden of obesity and the challenges of prevention. Ann Nutr Metab [Internet]. 2015 [cited 2020 Oct 5];66 (Suppl 2):7-12. Available from: https://doi.org/10.1159/000375143
https://doi.org/10.1159/000375143...

National studies conducted between 2006 and 2017 found an increase in the proportion of overweight and obesity according to sex, age and schooling strata.⁴4. Malta DC, Andrade SC, Claro RM, Bernal RT, Monteiro CA. Trends in prevalence of overweight and obesity in adults in 26 Brazilian state capitals and the Federal District from 2006 to 2012. Rev Bras Epidemiol [Internet]. 2014 [cited 2020 Oct 5];17 Suppl 1:267-76. Available from: https://doi.org/10.1590/1809-4503201400050021
https://doi.org/10.1590/1809-45032014000...

5. Malta DC, Santos MAS, Andrade SSCA, Oliveira TP, Stopa SR, Oliveira MMD, et al. Tendência temporal dos indicadores de excesso de peso em adultos nas capitais brasileiras, 2006-2013. Ciênc Saúde Coletiva [Internet]. 2016 abr [citado 2020 out 5];21(4):1061-9. Disponível em: https://doi.org/10.1590/1413-81232015214.12292015
https://doi.org/10.1590/1413-81232015214...

6. Malta DC, Silva AG, Tonaco LAB, Freitas MIF, Velasquez-Melendez G. Tendência temporal da prevalência de obesidade mórbida na população adulta brasileira entre os anos de 2006 e 2017. Cad Saúde Pública [Internet]. 2019 set [citado 2020 out 5];35(9):e00223518. Disponível em: https://doi.org/10.1590/0102-311x00223518
https://doi.org/10.1590/0102-311x0022351... ^-⁷7. Flores-Ortiz R, Malta DC, Velasquez-Melendez G. Adult body weight trends in 27 urban populations of Brazil from 2006 to 2016: a population-based study. PLoS One [Internet]. 2019 Mar [cited 2020 Oct 5];14(3):e0213254. Available from: https://doi.org/10.1371/journal.pone.0213254
https://doi.org/10.1371/journal.pone.021... Similar obesity prevalence rates can be seen between males and females, as well as inverse association with schooling and increased prevalence as age increases.⁴4. Malta DC, Andrade SC, Claro RM, Bernal RT, Monteiro CA. Trends in prevalence of overweight and obesity in adults in 26 Brazilian state capitals and the Federal District from 2006 to 2012. Rev Bras Epidemiol [Internet]. 2014 [cited 2020 Oct 5];17 Suppl 1:267-76. Available from: https://doi.org/10.1590/1809-4503201400050021
https://doi.org/10.1590/1809-45032014000...

5. Malta DC, Santos MAS, Andrade SSCA, Oliveira TP, Stopa SR, Oliveira MMD, et al. Tendência temporal dos indicadores de excesso de peso em adultos nas capitais brasileiras, 2006-2013. Ciênc Saúde Coletiva [Internet]. 2016 abr [citado 2020 out 5];21(4):1061-9. Disponível em: https://doi.org/10.1590/1413-81232015214.12292015
https://doi.org/10.1590/1413-81232015214...

6. Malta DC, Silva AG, Tonaco LAB, Freitas MIF, Velasquez-Melendez G. Tendência temporal da prevalência de obesidade mórbida na população adulta brasileira entre os anos de 2006 e 2017. Cad Saúde Pública [Internet]. 2019 set [citado 2020 out 5];35(9):e00223518. Disponível em: https://doi.org/10.1590/0102-311x00223518
https://doi.org/10.1590/0102-311x0022351... ^-⁷7. Flores-Ortiz R, Malta DC, Velasquez-Melendez G. Adult body weight trends in 27 urban populations of Brazil from 2006 to 2016: a population-based study. PLoS One [Internet]. 2019 Mar [cited 2020 Oct 5];14(3):e0213254. Available from: https://doi.org/10.1371/journal.pone.0213254
https://doi.org/10.1371/journal.pone.021... However, these characteristics were assessed separately.

Given the need to identify groups at greater risk of developing these conditions and in view of the co-existence of these characteristics, the objective of this study was to analyze the temporal trend of overweight and obesity prevalence rates in the Brazilian state capitals and Federal District between 2006 and 2019.

Methods

This was a time series study using data from the Surveillance System for Risk and Protective Factors for Chronic Diseases by Telephone Survey (VIGITEL), gathered between 2006 and 2019 (n=730,309). Every year VIGITEL carries out over 50,000 landline telephone interviews with adults (≥18 years old) living in private households in the 26 Brazilian state capitals and Federal District, collecting self-reported information about sociodemographic and behavioral characteristics, weight and height, among others.⁸8. Ministério da Saúde (BR). Secretaria de Vigilância em Saúde. Departamento de Análise de Saúde e Vigilância de Doenças Não Transmissíveis. Vigitel Brasil 2019: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico: estimativas sobre frequência e distribuição sociodemográfica de fatores de risco e proteção para doenças crônicas nas capitais dos 26 estados brasileiros e no Distrito Federal em 2019 [Internet]. Brasília: Ministério da Saúde; 2020 [citado 2020 out 5]. 137 p. Disponível em: http://www.crn1.org.br/wp-content/uploads/2020/04/vigitel-brasil-2019-vigilancia-fatores-risco.pdf?x53725
http://www.crn1.org.br/wp-content/upload...

The VIGITEL sampling process takes place in two stages: (i) systematized random selection of 5,000 landline telephones per city, based on telephone records provided by the country’s main telephone companies; and (ii) random selection of an individual in each household who is requested to answer the questionnaire.⁸8. Ministério da Saúde (BR). Secretaria de Vigilância em Saúde. Departamento de Análise de Saúde e Vigilância de Doenças Não Transmissíveis. Vigitel Brasil 2019: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico: estimativas sobre frequência e distribuição sociodemográfica de fatores de risco e proteção para doenças crônicas nas capitais dos 26 estados brasileiros e no Distrito Federal em 2019 [Internet]. Brasília: Ministério da Saúde; 2020 [citado 2020 out 5]. 137 p. Disponível em: http://www.crn1.org.br/wp-content/uploads/2020/04/vigitel-brasil-2019-vigilancia-fatores-risco.pdf?x53725
http://www.crn1.org.br/wp-content/upload...

The data on respondent weight and height were used to calculate BMI and were classified according to two categories: (i) overweight (BMI ≥25kg/m²); and (ii) obesity (BMI ≥30kg/m²).⁸8. Ministério da Saúde (BR). Secretaria de Vigilância em Saúde. Departamento de Análise de Saúde e Vigilância de Doenças Não Transmissíveis. Vigitel Brasil 2019: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico: estimativas sobre frequência e distribuição sociodemográfica de fatores de risco e proteção para doenças crônicas nas capitais dos 26 estados brasileiros e no Distrito Federal em 2019 [Internet]. Brasília: Ministério da Saúde; 2020 [citado 2020 out 5]. 137 p. Disponível em: http://www.crn1.org.br/wp-content/uploads/2020/04/vigitel-brasil-2019-vigilancia-fatores-risco.pdf?x53725
http://www.crn1.org.br/wp-content/upload... Missing data on weight and height were imputed using the hot deck procedure.⁸8. Ministério da Saúde (BR). Secretaria de Vigilância em Saúde. Departamento de Análise de Saúde e Vigilância de Doenças Não Transmissíveis. Vigitel Brasil 2019: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico: estimativas sobre frequência e distribuição sociodemográfica de fatores de risco e proteção para doenças crônicas nas capitais dos 26 estados brasileiros e no Distrito Federal em 2019 [Internet]. Brasília: Ministério da Saúde; 2020 [citado 2020 out 5]. 137 p. Disponível em: http://www.crn1.org.br/wp-content/uploads/2020/04/vigitel-brasil-2019-vigilancia-fatores-risco.pdf?x53725
http://www.crn1.org.br/wp-content/upload... Further information on the methodology can be found in the annual VIGITEL report.⁸8. Ministério da Saúde (BR). Secretaria de Vigilância em Saúde. Departamento de Análise de Saúde e Vigilância de Doenças Não Transmissíveis. Vigitel Brasil 2019: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico: estimativas sobre frequência e distribuição sociodemográfica de fatores de risco e proteção para doenças crônicas nas capitais dos 26 estados brasileiros e no Distrito Federal em 2019 [Internet]. Brasília: Ministério da Saúde; 2020 [citado 2020 out 5]. 137 p. Disponível em: http://www.crn1.org.br/wp-content/uploads/2020/04/vigitel-brasil-2019-vigilancia-fatores-risco.pdf?x53725
http://www.crn1.org.br/wp-content/upload...

The sociodemographic variables of interest to the study were sex (male; female), age group (in years: 18-24; 25-34; 35-44; 45-54; 55-64; 65 or over) and level of schooling (in years of study: 0-8; 9-11; 12 or more), which, when combined, generated 36 categories for stratifying the analyses (Supplementary Table 1).

In order to investigate the temporal variation of the indicators studied, first of all the prevalence rates were calculated for each of the strata, year by year. Following this, Prais-Winsten regression models were used to control autocorrelation of the regression residues between the years analyzed;⁹9. Antunes JLF, Cardoso MRA. Uso da análise de séries temporais em estudos epidemiológicos. Epidemiol Serv Saúde [Internet]. 2015 jul-set [citado 2020 out 5];24(3):565-76. Disponível em: https://doi.org/10.5123/S1679-49742015000300024
https://doi.org/10.5123/S1679-4974201500... significant regression coefficient values (p<0.05) indicated an increase or decrease in prevalence.

The prevalence rates estimated by VIGITEL used post-stratification weighting, which took into consideration the sex, age group and level of schooling strata, with the aim of extrapolating the sociodemographic structure of the adult population studied to the structure of the total adult population in each place studied.⁸8. Ministério da Saúde (BR). Secretaria de Vigilância em Saúde. Departamento de Análise de Saúde e Vigilância de Doenças Não Transmissíveis. Vigitel Brasil 2019: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico: estimativas sobre frequência e distribuição sociodemográfica de fatores de risco e proteção para doenças crônicas nas capitais dos 26 estados brasileiros e no Distrito Federal em 2019 [Internet]. Brasília: Ministério da Saúde; 2020 [citado 2020 out 5]. 137 p. Disponível em: http://www.crn1.org.br/wp-content/uploads/2020/04/vigitel-brasil-2019-vigilancia-fatores-risco.pdf?x53725
http://www.crn1.org.br/wp-content/upload...

The statistical analyses were performed using Stata version 14.1. Secondary data available for public access and use on the Ministry of Health’s website were used. The VIGITEL study was authorized by the Ministry of Health’s National Human Research Ethics Committee: Opinion No. 2.006.31, issued on June 6^th 2017; Certificate of Submission for Ethical Appraisal No. 65610017.1.0000.0008. Free and informed consent was obtained verbally during each telephone interview.

Results

Overweight prevalence increased from 42.6% in 2006 to 55.4% in 2019 (2.05% per annum) (Supplementary Table 2). Among males aged over 25, prevalence rates were in excess of 40.0%; with effect from 35 years of age, higher prevalence rates were found among those with higher levels of schooling. The highest mean increases occurred among younger males (18-24 years) with up to 8 years of schooling (from 21.0% in 2006 to 35.8% in 2019; 3.17% per annum) and with 12 or more years of schooling (from 32.6% in 2006 to 41.5% in 2019; 2.24% per annum). Among males with higher levels of schooling (≥12 years), smaller increases occurred with effect from 45 years of age, compared to younger males (18-24 years) in this group (Table 1; Figure 1).

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Figure 1
Average increment and respective confidence intervals (95%CI) for overweight and obesity among the adult population in the capital cities of the Brazilian States and in the Federal District, by sociodemographic strata, based on data from the Surveillance System for Risk and Protective Factors for Chronic Diseases by Telephone Survey (VIGITEL), 2006-2019

Among females, in most years those aged over 45 had overweight prevalence rates above 50.0%. Higher prevalence rates were found in all age groups in females with less schooling. Females aged 18-24 with 12 or more years of schooling showed the largest increase in the period (from 9.0% in 2006 to 27.5% in 2019; 6.81% per annum), compared to females with the same level of schooling aged 35 or more (Table 1; Figure 1).

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Table 1
Percentage^a (%) of overweight adult population in the capital cities of the Brazilian States and in the Federal District, by sociodemographic strata, based on data from the Surveillance System for Risk and Protective Factors for Chronic Diseases by Telephone Survey (VIGITEL), 2006-2019

Prevalence of obesity increased from 11.8% in 2006 to 20.3% in 2019: an increase of 3.8% per annum (Supplementary Table 2). Among males, lower obesity prevalence rates were found among younger males aged 18-24. When comparing all the strata, no differences were found in the average increases (Table 2; Figure 1).

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Table 2
Percentage^a (%) of obese adult population in the capital cities of the Brazilian States and in the Federal District, by sociodemographic strata, based on data from the Surveillance System for Risk and Protective Factors for Chronic Diseases by Telephone Survey (VIGITEL), 2006-2019

Females had the lowest obesity prevalence rates in the youngest age group, i.e. 18-24 years. A significant increase in obesity was found in all the strata analyzed. When comparing age groups within the highest level of schooling, the largest increases were found among females aged 18-24 (from 2.5% in 2006 to 10.5% in 2019; 10.79% per annum), in relation to females aged over 45 years old (Table 2; Figure 1).

Discussion

An increase in the proportion of overweight and obese adults was found between 2006 and 2019 in the majority of the strata studied. Higher mean increases were found above all among young people with high levels of schooling, both for overweight, and for obesity.

A population’s living and health situations and their relationship with income directly influence their food intake pattern and their physical activity, these being the main risk factors for obesity. A population that is in an economically vulnerable situation with regard to its choice of food, reduced to more affordable food items, generally having high energy density, or furthermore, a population living in regions that are unsafe due to urban violence, unable to voluntarily undertake physical activities, is a population that is related to rising growth in obesity.¹⁰10. Barros MBA, Francisco PMB, Zanchetta LM, César CLG. Tendências das desigualdades sociais e demográficas na prevalência de doenças crônicas no Brasil, PNAD: 2003- 2008. Ciênc Saúde Coletiva [Internet]. 2011 set [citado 2020 out 5];16(9):3755-68. Disponível em: https://doi.org/10.1590/S1413-81232011001000012
https://doi.org/10.1590/S1413-8123201100... ¹¹11. Ferreira VA, Magalhães R. Obesidade entre os pobres no Brasil: a vulnerabilidade feminina. Ciênc Saude Coletiva [Internet]. 2011 [citado 2020 out 5];16(4):2279-87. Disponível em: https://doi.org/10.1590/S1413-81232011000400027
https://doi.org/10.1590/S1413-8123201100... Within this scenario, increased prevalence of overweight and obesity has been attributed, above all in middle-income countries such as Brazil, to behavior changes over the years, such as inadequate food intake, increased sedentary habits and low levels of physical activity.¹²12. Blüher M. Obesity: global epidemiology and pathogenesis. Nat Rev Endocrinol [Internet]. 2019 Feb [cited 2020 Oct 5];15(5):288-98. Available from: https://doi.org/10.1038/s41574-019-0176-8
https://doi.org/10.1038/s41574-019-0176-...

The sociodemographic characteristics studied appear to influence the increase in overweight and obesity, especially among the younger. Greater variations in the female sex may be related to gender issues, with notable impact on their health.¹³13. Instituto Brasileiro de Geografia e Estatística - IBGE. Pesquisa nacional por amostra de domicílios contínua: outras formas de trabalho 2017: PNAD contínua [Internet]. Rio de Janeiro: IBGE; 2018 [citado 2020 out 5]. Disponível em: Disponível em: https://agenciadenoticias.ibge.gov.br/media/com_mediaibge/arquivos/81c9b2749a7b8e5b67f9a7361f839a3d.pdf
https://agenciadenoticias.ibge.gov.br/me... ¹⁴14. Instituto Brasileiro de Direito Urbanístico - IBDU. Direito à cidade: uma visão por gênero [Internet]. São Paulo: IBDU; 2017 [citado 2020 out 5]. 126 p. Disponível em: http://wp.ibdu.org.br/wp-content/uploads/2019/04/DIREITO_CIDADE_GENERO.pdf
http://wp.ibdu.org.br/wp-content/uploads... Although, in general, higher prevalence rates are found among individuals with lower levels of schooling, mean increases of greater magnitude can be seen among those with higher levels of schooling. These findings may be related to factors such as financial independence, occupations in which less energy is burned and little time for taking care of one’s health, particularly among females.¹⁵15. Dinsa GD, Goryakin Y, Fumagalli E, Suhrcke M. Obesity and socioeconomic status in developing countries: a systematic review. Obes Rev [Internet]. 2012 Nov [cited 2020 Oct 5];13(11):1067-79. Available from: https://doi.org/10.1111/j.1467-789x.2012.01017.x
https://doi.org/10.1111/j.1467-789x.2012... ¹⁶16. Reyes MU, Mesenburg MA, Victora CG. Socioeconomic inequalities in the prevalence of underweight, overweight, and obesity among women aged 20-49 in low- and middle-income countries. Int J Obes (Lond) [Internet]. 2020 Mar [cited 2020 Oct 5];44(3):609-16. Available from: https://doi.org/10.1038/s41366-019-0503-0
https://doi.org/10.1038/s41366-019-0503-...

With the objective of curbing the growth in obesity, Brazil committed to United Nations Organization targets aimed at preventing increased obesity among adults, reducing regular consumption of sugar-sweetened drinks and promoting intake of fruit and vegetables by 2019.¹⁷17. Câmara Interministerial de Segurança Alimentar e Nutricional - CAISAN (BR). Plano nacional de segurança alimentar e nutricional (PLANSAN 2016-2019). Brasília: Câmara Interministerial de Segurança Alimentar e Nutricional; 2018 [citado 2020 out 5]. 88 p. Disponível em: http://www.mds.gov.br/webarquivos/arquivo/seguranca_alimentar/caisan/Publicacao/Caisan_Nacional/PLANSAN%202016-2019_revisado_completo.pdf
http://www.mds.gov.br/webarquivos/arquiv... In addition, one of the targets of the National Plan to Address Chronic Noncommunicable Diseases consists of controlling growth in obesity among the adult population.¹⁸18. Ministério da Saúde (BR). Secretaria de Vigilância em Saúde. Departamento de Análise de Situação de Saúde. Plano de ações estratégicas para o enfrentamento das Doenças Crônicas não Transmissíveis (DCNT) no Brasil 2011-2022 [Internet]. Brasília: Ministério da Saúde ; 2011 [citado 2020 out 5]. (Série B. Textos Básicos de Saúde). 160 p. Disponível em: https://portaldeboaspraticas.iff.fiocruz.br/biblioteca/plano-de-acoes-estrategicas-para-o-enfrentamento-das-doencas-cronicas /
https://portaldeboaspraticas.iff.fiocruz... In order to achieve these targets, Brazil needs to develop intersectoral response strategies, such as environmental measures that influence healthier choices, including restricting advertising of unhealthy foods,¹⁹19. Freeman B, Kelly B, Vandevijvere S, Baur L. Young adults: beloved by food and drink marketers and forgotten by public health? Health Promot Int [Internet]. 2016 Dec [cited 2020 Oct 5];31(4):954-61. Available from: https://doi.org/10.1093/heapro/dav081
https://doi.org/10.1093/heapro/dav081... fiscal measures such as subsidies and taxation,²⁰20. Passos CM, Maia EG, Levy RB, Martins APB, Claro RM. Association between the price of ultra-processed foods and obesity in Brazil. Nutr Metabol Card Dis [Internet]. 2020 Apr [cited 2020 Oct 5];30(4):589-98. Available from: https://doi.org/10.1016/j.numecd.2019.12.011
https://doi.org/10.1016/j.numecd.2019.12... ²¹21. World Health Organization - WHO. Global strategy on diet, physical activity and health. Resolution of the World Health Assembly. Fifty-seventh World Health Assembly [Internet]. Genebra: World Health Organization ; 2004 [cited 2020 Oct 5]. Available from: https://www.who.int/dietphysicalactivity/strategy/eb11344/strategy_english_web.pdf
https://www.who.int/dietphysicalactivity... and reformulation of urban spaces, making them more accessible for practicing physical activities, prioritizing public security and revitalizing areas and equipment in squares and parks, as well as encouraging people to walk rather than using transport. These interventions form part of public policies intended to promote greater gender equity and equality.²²22. Ferreira VA, Silva AE, Rodrigues CAA, Nunes NLA, Vigato TC, Magalhães R. Desigualdade, pobreza e obesidade. Ciênc Saúde Coletiva [Internet]. 2010 Jun [cited 2020 Oct 5];15(supl. 1):1423-32. Disponível em: https://doi.org/10.1590/S1413-81232010000700053
https://doi.org/10.1590/S1413-8123201000...

Collecting self-reported data and possible resulting inaccuracies in BMI calculation, when such data are compared with data measured directly, limit the validity of this study. However, self-reported information is widely used in health surveys and is recommended for risk factor investigation. ²³23. Centers for Disease Control and Prevention - CDC. Behavioral risk factor surveillance system - BRFSS [Internet]. Washington, D.C.: CDC; 2014 [cited 2020 Jan 24]. Available from: https://www.cdc.gov/brfss/about/index.htm
https://www.cdc.gov/brfss/about/index.ht... ²⁴24. World Health Organization - WHO. Summary: surveillance of risk factors for non-communicable diseases. The WHO STEP wise approach. Geneva: World Health Organization; 2001. The VIGITEL sample is restricted to individuals who have landlines and live in the Brazilian state capitals and Federal District. This may mean that the BMI information reported by individuals with landlines could be biased as it may possibly be different to information relating to individuals who do not have a landline telephone. Although the method used to collect data may perhaps influence the prevalence estimates for each of the years, under or overestimation of those estimates is deemed to be consistent over the course of the years. Finally, the combination of the sociodemographic characteristics studied may have influenced the accuracy of the estimates for some of the strata.

An increase in the proportion of overweight and obese adults was found in the majority of the strata studied, above all among young people with higher levels of schooling. Addressing the complex challenges that obesity represents for society goes beyond the health sector. Using public policies to boost a set of intersectoral actions that encourage healthy lifestyles is essential for controlling the growth of these conditions in Brazil.

References

¹
World Health Organization - WHO. Obesity and overweight [Internet]. Genebra: World Health Organization; 2020 [cited 2020 Jan 15]. Available from: https://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight /
» https://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight
²
NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet [Internet]. 2017 Dec [cited 2020 Oct 5];390(10113):2627-42. Available from: https://doi.org/10.1016/S0140-6736(17)32129-3
» https://doi.org/10.1016/S0140-6736(17)32129-3
³
Seidell JC, Halberstadt J. The global burden of obesity and the challenges of prevention. Ann Nutr Metab [Internet]. 2015 [cited 2020 Oct 5];66 (Suppl 2):7-12. Available from: https://doi.org/10.1159/000375143
» https://doi.org/10.1159/000375143
⁴
Malta DC, Andrade SC, Claro RM, Bernal RT, Monteiro CA. Trends in prevalence of overweight and obesity in adults in 26 Brazilian state capitals and the Federal District from 2006 to 2012. Rev Bras Epidemiol [Internet]. 2014 [cited 2020 Oct 5];17 Suppl 1:267-76. Available from: https://doi.org/10.1590/1809-4503201400050021
» https://doi.org/10.1590/1809-4503201400050021
⁵
Malta DC, Santos MAS, Andrade SSCA, Oliveira TP, Stopa SR, Oliveira MMD, et al. Tendência temporal dos indicadores de excesso de peso em adultos nas capitais brasileiras, 2006-2013. Ciênc Saúde Coletiva [Internet]. 2016 abr [citado 2020 out 5];21(4):1061-9. Disponível em: https://doi.org/10.1590/1413-81232015214.12292015
» https://doi.org/10.1590/1413-81232015214.12292015
⁶
Malta DC, Silva AG, Tonaco LAB, Freitas MIF, Velasquez-Melendez G. Tendência temporal da prevalência de obesidade mórbida na população adulta brasileira entre os anos de 2006 e 2017. Cad Saúde Pública [Internet]. 2019 set [citado 2020 out 5];35(9):e00223518. Disponível em: https://doi.org/10.1590/0102-311x00223518
» https://doi.org/10.1590/0102-311x00223518
⁷
Flores-Ortiz R, Malta DC, Velasquez-Melendez G. Adult body weight trends in 27 urban populations of Brazil from 2006 to 2016: a population-based study. PLoS One [Internet]. 2019 Mar [cited 2020 Oct 5];14(3):e0213254. Available from: https://doi.org/10.1371/journal.pone.0213254
» https://doi.org/10.1371/journal.pone.0213254
⁸
Ministério da Saúde (BR). Secretaria de Vigilância em Saúde. Departamento de Análise de Saúde e Vigilância de Doenças Não Transmissíveis. Vigitel Brasil 2019: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico: estimativas sobre frequência e distribuição sociodemográfica de fatores de risco e proteção para doenças crônicas nas capitais dos 26 estados brasileiros e no Distrito Federal em 2019 [Internet]. Brasília: Ministério da Saúde; 2020 [citado 2020 out 5]. 137 p. Disponível em: http://www.crn1.org.br/wp-content/uploads/2020/04/vigitel-brasil-2019-vigilancia-fatores-risco.pdf?x53725
» http://www.crn1.org.br/wp-content/uploads/2020/04/vigitel-brasil-2019-vigilancia-fatores-risco.pdf?x53725
⁹
Antunes JLF, Cardoso MRA. Uso da análise de séries temporais em estudos epidemiológicos. Epidemiol Serv Saúde [Internet]. 2015 jul-set [citado 2020 out 5];24(3):565-76. Disponível em: https://doi.org/10.5123/S1679-49742015000300024
» https://doi.org/10.5123/S1679-49742015000300024
¹⁰
Barros MBA, Francisco PMB, Zanchetta LM, César CLG. Tendências das desigualdades sociais e demográficas na prevalência de doenças crônicas no Brasil, PNAD: 2003- 2008. Ciênc Saúde Coletiva [Internet]. 2011 set [citado 2020 out 5];16(9):3755-68. Disponível em: https://doi.org/10.1590/S1413-81232011001000012
» https://doi.org/10.1590/S1413-81232011001000012
¹¹
Ferreira VA, Magalhães R. Obesidade entre os pobres no Brasil: a vulnerabilidade feminina. Ciênc Saude Coletiva [Internet]. 2011 [citado 2020 out 5];16(4):2279-87. Disponível em: https://doi.org/10.1590/S1413-81232011000400027
» https://doi.org/10.1590/S1413-81232011000400027
¹²
Blüher M. Obesity: global epidemiology and pathogenesis. Nat Rev Endocrinol [Internet]. 2019 Feb [cited 2020 Oct 5];15(5):288-98. Available from: https://doi.org/10.1038/s41574-019-0176-8
» https://doi.org/10.1038/s41574-019-0176-8
¹³
Instituto Brasileiro de Geografia e Estatística - IBGE. Pesquisa nacional por amostra de domicílios contínua: outras formas de trabalho 2017: PNAD contínua [Internet]. Rio de Janeiro: IBGE; 2018 [citado 2020 out 5]. Disponível em: Disponível em: https://agenciadenoticias.ibge.gov.br/media/com_mediaibge/arquivos/81c9b2749a7b8e5b67f9a7361f839a3d.pdf
» https://agenciadenoticias.ibge.gov.br/media/com_mediaibge/arquivos/81c9b2749a7b8e5b67f9a7361f839a3d.pdf
¹⁴
Instituto Brasileiro de Direito Urbanístico - IBDU. Direito à cidade: uma visão por gênero [Internet]. São Paulo: IBDU; 2017 [citado 2020 out 5]. 126 p. Disponível em: http://wp.ibdu.org.br/wp-content/uploads/2019/04/DIREITO_CIDADE_GENERO.pdf
» http://wp.ibdu.org.br/wp-content/uploads/2019/04/DIREITO_CIDADE_GENERO.pdf
¹⁵
Dinsa GD, Goryakin Y, Fumagalli E, Suhrcke M. Obesity and socioeconomic status in developing countries: a systematic review. Obes Rev [Internet]. 2012 Nov [cited 2020 Oct 5];13(11):1067-79. Available from: https://doi.org/10.1111/j.1467-789x.2012.01017.x
» https://doi.org/10.1111/j.1467-789x.2012.01017.x
¹⁶
Reyes MU, Mesenburg MA, Victora CG. Socioeconomic inequalities in the prevalence of underweight, overweight, and obesity among women aged 20-49 in low- and middle-income countries. Int J Obes (Lond) [Internet]. 2020 Mar [cited 2020 Oct 5];44(3):609-16. Available from: https://doi.org/10.1038/s41366-019-0503-0
» https://doi.org/10.1038/s41366-019-0503-0
¹⁷
Câmara Interministerial de Segurança Alimentar e Nutricional - CAISAN (BR). Plano nacional de segurança alimentar e nutricional (PLANSAN 2016-2019). Brasília: Câmara Interministerial de Segurança Alimentar e Nutricional; 2018 [citado 2020 out 5]. 88 p. Disponível em: http://www.mds.gov.br/webarquivos/arquivo/seguranca_alimentar/caisan/Publicacao/Caisan_Nacional/PLANSAN%202016-2019_revisado_completo.pdf
» http://www.mds.gov.br/webarquivos/arquivo/seguranca_alimentar/caisan/Publicacao/Caisan_Nacional/PLANSAN%202016-2019_revisado_completo.pdf
¹⁸
Ministério da Saúde (BR). Secretaria de Vigilância em Saúde. Departamento de Análise de Situação de Saúde. Plano de ações estratégicas para o enfrentamento das Doenças Crônicas não Transmissíveis (DCNT) no Brasil 2011-2022 [Internet]. Brasília: Ministério da Saúde ; 2011 [citado 2020 out 5]. (Série B. Textos Básicos de Saúde). 160 p. Disponível em: https://portaldeboaspraticas.iff.fiocruz.br/biblioteca/plano-de-acoes-estrategicas-para-o-enfrentamento-das-doencas-cronicas /
» https://portaldeboaspraticas.iff.fiocruz.br/biblioteca/plano-de-acoes-estrategicas-para-o-enfrentamento-das-doencas-cronicas
¹⁹
Freeman B, Kelly B, Vandevijvere S, Baur L. Young adults: beloved by food and drink marketers and forgotten by public health? Health Promot Int [Internet]. 2016 Dec [cited 2020 Oct 5];31(4):954-61. Available from: https://doi.org/10.1093/heapro/dav081
» https://doi.org/10.1093/heapro/dav081
²⁰
Passos CM, Maia EG, Levy RB, Martins APB, Claro RM. Association between the price of ultra-processed foods and obesity in Brazil. Nutr Metabol Card Dis [Internet]. 2020 Apr [cited 2020 Oct 5];30(4):589-98. Available from: https://doi.org/10.1016/j.numecd.2019.12.011
» https://doi.org/10.1016/j.numecd.2019.12.011
²¹
World Health Organization - WHO. Global strategy on diet, physical activity and health. Resolution of the World Health Assembly. Fifty-seventh World Health Assembly [Internet]. Genebra: World Health Organization ; 2004 [cited 2020 Oct 5]. Available from: https://www.who.int/dietphysicalactivity/strategy/eb11344/strategy_english_web.pdf
» https://www.who.int/dietphysicalactivity/strategy/eb11344/strategy_english_web.pdf
²²
Ferreira VA, Silva AE, Rodrigues CAA, Nunes NLA, Vigato TC, Magalhães R. Desigualdade, pobreza e obesidade. Ciênc Saúde Coletiva [Internet]. 2010 Jun [cited 2020 Oct 5];15(supl. 1):1423-32. Disponível em: https://doi.org/10.1590/S1413-81232010000700053
» https://doi.org/10.1590/S1413-81232010000700053
²³
Centers for Disease Control and Prevention - CDC. Behavioral risk factor surveillance system - BRFSS [Internet]. Washington, D.C.: CDC; 2014 [cited 2020 Jan 24]. Available from: https://www.cdc.gov/brfss/about/index.htm
» https://www.cdc.gov/brfss/about/index.htm
²⁴
World Health Organization - WHO. Summary: surveillance of risk factors for non-communicable diseases. The WHO STEP wise approach. Geneva: World Health Organization; 2001.

Supplementary

Thumbnail

Table 1
Distribution of the interviewed population and overweight and obese adults in the capital cities of the Brazilian States and in the Federal District, by sociodemographic strata, based on data from the Surveillance System for Risk and Protective Factors for Chronic Diseases by Telephone Survey (VIGITEL), 2006-2019

Thumbnail

Table 2
Percentage^a (%) overweight and obesity among the adult population of the capital cities of the Brazilian States and the Federal District, by sociodemographic strata, based on data from the Surveillance System for Risk and Protective Factors for Chronic Diseases by Telephone Survey (VIGITEL), 2006-2019

Publication Dates

Publication in this collection
15 Feb 2021
Date of issue
Jan-Dec 2021

History

Received
18 May 2020
Accepted
08 Sept 2020

Este é um artigo publicado em acesso aberto sob uma licença Creative Commons

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» https://doi.org/10.5123/S1679-49742015000300024

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» https://doi.org/10.1590/S1413-81232011001000012

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» https://doi.org/10.1590/S1413-81232011000400027

[12] ¹²
Blüher M. Obesity: global epidemiology and pathogenesis. Nat Rev Endocrinol [Internet]. 2019 Feb [cited 2020 Oct 5];15(5):288-98. Available from: https://doi.org/10.1038/s41574-019-0176-8
» https://doi.org/10.1038/s41574-019-0176-8

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» http://wp.ibdu.org.br/wp-content/uploads/2019/04/DIREITO_CIDADE_GENERO.pdf

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» https://doi.org/10.1111/j.1467-789x.2012.01017.x

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» https://doi.org/10.1093/heapro/dav081

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» https://doi.org/10.1016/j.numecd.2019.12.011

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» https://doi.org/10.1590/S1413-81232010000700053

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Variables		2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	Average increment	95%CI^b	p-value^c
Age (years)	Schooling (years)	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	Average increment	95%CI^b	p-value^c
Male
18-24	0-8	21.0	30.4	23.0	40.8	32.6	30.5	33.1	28.9	24.6	48.5	35.8	32.6	47.0	35.8	3.17	1.06;5.28	0.007
	9-11	25.8	23.5	26.9	26.0	28.7	30.7	31.0	34.4	37.9	34.3	30.8	33.0	28.6	24.9	0.31	-3.23;3.86	0.850
	≥12	32.6	33.9	30.9	32.3	35.3	30.5	37.8	38.0	36.1	38.4	37.5	44.3	39.3	41.5	2.24	1.60;2.89	<0.001
25-34	0-8	47.6	46.4	42.5	45.9	45.7	50.9	59.2	56.3	57.1	59.1	45.0	56.0	55.2	57.8	1.79	0.28;3.29	0.024
	9-11	47.1	48.8	50.7	47.7	49.8	53.3	50.8	49.4	55.8	56.8	63.0	56.0	58.5	56.4	1.75	1.02;2.49	<0.001
	≥12	49.5	52.1	58.5	57.8	54.7	61.7	59.1	54.5	55.9	54.4	59.0	57.6	55.8	57.2	0.50	-0.38;1.39	0.241
35-44	0-8	53.9	52.8	54.5	51.8	53.4	60.1	58.7	59.4	59.9	60.4	66.9	56.8	62.0	60.7	1.36	0.75;1.98	<0.001
	9-11	59.9	56.1	57.3	61.8	61.4	63.8	59.2	63.9	72.1	69.6	64.9	71.6	67.6	68.4	1.58	0.93;2.24	<0.001
	≥12	57.4	66.7	62.2	60.3	65.1	68.0	71.4	71.4	67.8	65.5	69.9	71.1	72.8	71.9	1.33	0.63;2.03	0.001
45-54	0-8	55.2	58.3	55.8	54.5	60.6	54.8	60.5	67.3	61.8	66.5	65.6	66.1	61.7	62.6	1.30	0.52;2.08	0.003
	9-11	59.1	61.7	67.9	60.5	65.3	64.5	63.6	63.9	67.6	66.7	71.8	64.8	70.0	63.8	0.78	0.38;1.17	0.001
	≥12	65.8	65.5	69.2	66.2	72.0	71.3	72.0	65.3	68.1	69.1	70.7	69.9	73.9	71.7	0.55	0.06;1.05	0.032
55-64	0-8	50.3	55.5	57.9	59.1	60.7	59.7	57.6	60.6	64.2	67.2	60.8	57.5	59.4	61.6	1.08	-0.05;2.22	0.059
	9-11	62.9	59.4	64.6	59.7	65.9	64.6	62.1	59.9	65.4	66.9	71.0	62.5	65.0	62.0	0.42	-0.20;1.05	0.168
	≥12	57.0	64.8	63.2	64.4	67.2	59.7	63.2	70.3	61.5	64.4	64.2	67.8	66.4	69.7	0.66	0.14;1.17	0.017
≥65	0-8	50.8	49.0	49.7	55.9	58.5	49.2	57.8	52.7	56.7	55.8	52.6	56.5	60.2	60.0	1.13	0.47;1.79	0.003
	9-11	44.9	48.2	56.2	53.0	56.5	52.2	55.0	55.0	60.6	60.3	58.1	53.3	60.4	59.7	1.52	0.64;2.41	0.003
	≥12	53.8	62.5	57.8	54.5	57.0	60.8	58.6	63.1	56.8	62.3	63.2	61.5	64.7	64.3	0.96	0.49;1.43	0.001
Female
18-24	0-8	25.8	19.2	35.7	28.0	41.0	36.3	34.8	31.1	41.2	52.0	33.0	34.6	49.8	37.3	3.67	1.44;5.91	0.004
	9-11	14.4	14.9	16.0	22.0	24.9	20.0	22.8	25.1	28.7	26.7	28.4	26.7	27.1	28.6	4.70	2.79;6.61	<0.001
	≥12	9.0	13.1	16.9	15.2	14.4	15.5	22.7	21.3	20.6	22.7	22.8	26.4	29.6	27.5	6.81	5.39;8.24	<0.001
25-34	0-8	34.8	41.1	38.3	42.4	47.0	47.0	48.3	46.8	53.5	59.3	56.0	58.8	64.2	61.1	4.23	3.69;4.76	<0.001
	9-11	26.8	29.5	34.7	34.9	38.7	39.8	41.7	40.8	45.2	48.6	49.8	49.2	56.1	56.2	5.04	4.56;5.52	<0.001
	≥12	20.8	23.0	23.1	24.6	33.0	29.2	35.7	31.3	30.9	32.8	35.4	36.4	40.2	42.1	4.64	3.42;5.85	<0.001
35-44	0-8	48.8	48.0	49.5	50.6	52.3	52.7	55.5	60.1	62.1	66.1	61.2	62.2	60.5	57.0	1.44	-0.22;3.11	0.082
	9-11	41.0	37.7	42.6	45.6	44.4	48.1	50.5	48.9	53.2	57.2	57.8	61.5	61.6	58.8	3.68	3.21;4.16	<0.001
	≥12	32.0	31.5	31.6	34.2	38.3	41.6	45.1	41.9	39.5	42.8	50.2	45.3	48.1	51.5	3.67	2.61;4.74	<0.001
45-54	0-8	55.9	54.2	55.0	57.0	58.4	59.8	61.9	61.2	64.3	63.7	63.0	61.2	66.6	68.0	1.56	1.13;1.98	<0.001
	9-11	50.9	48.0	46.4	53.4	50.6	54.6	56.0	55.1	57.8	58.0	56.2	58.6	63.0	65.3	2.09	1.58;2.61	<0.001
	≥12	42.1	44.3	43.9	41.1	44.6	45.3	55.5	49.3	49.3	49.9	49.4	51.2	51.9	52.1	1.69	0.83;2.56	0.001
55-64	0-8	60.2	58.0	59.8	59.7	60.4	61.9	62.7	65.7	64.3	66.4	64.0	64.8	69.2	64.4	1.10	0.84;1.35	<0.001
	9-11	54.4	58.7	55.5	62.7	56.5	60.7	57.7	63.2	59.9	58.8	59.6	61.9	60.2	64.2	0.61	0.33;0.90	<0.001
	≥12	52.9	48.1	48.8	49.2	54.2	51.5	58.7	54.3	48.5	53.9	56.0	52.4	55.8	57.8	0.89	0.15;1.62	0.022
≥65	0-8	53.5	52.6	54.5	53.9	56.9	57.4	59.0	58.5	58.7	57.3	58.9	63.2	60.9	60.4	1.14	0.79;1.49	<0.001
	9-11	54.2	43.9	57.5	52.5	53.6	53.0	63.8	54.6	58.0	60.6	62.6	60.6	60.8	59.6	1.67	1.08;2.25	<0.001
	≥12	46.6	57.9	54.2	51.3	47.2	52.5	52.6	51.8	55.5	52.1	54.8	52.3	55.2	53.5	0.36	-0.31;1.03	0.267

Variables		2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	Average increment	95%CI^b	p-value^c
Age (years)	Schooling (years)	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	Average increment	95%CI^b	p-value^c
Male
18-24	0-8	5.7	4.6	6.7	11.1	8.3	6.8	9.7	5.8	7.4	13.7	13.2	13.8	8.9	3.8	2.09	-5.44;9.61	0.557
	9-11	3.8	4.3	5.6	6.9	5.5	5.6	6.0	7.6	10.6	7.8	7.3	6.2	5.6	6.9	3.30	-1.44;8.04	0.155
	≥12	5.3	5.0	4.6	7.0	5.5	6.9	11.6	10.2	8.0	7.2	8.3	16.5	8.2	9.0	5.81	1.72;9.91	0.009
25-34	0-8	14.0	13.4	12.4	15.8	11.9	19.3	22.3	20.9	24.6	26.0	17.8	17.5	15.8	21.3	3.20	-1.10;7.51	0.131
	9-11	9.3	14.1	11.7	10.9	13.3	14.1	15.9	14.5	16.2	18.0	20.3	18.2	21.7	19.3	5.22	4.20;6.23	<0.001
	≥12	11.2	10.1	14.7	12.9	10.9	15.7	14.6	16.2	13.4	15.4	17.9	18.0	15.3	19.0	3.53	2.40;4.67	<0.001
35-44	0-8	12.7	17.6	15.7	15.2	13.0	21.3	20.1	21.7	22.8	24.0	22.1	22.4	27.2	20.8	4.35	2.85;5.85	<0.001
	9-11	16.2	14.6	17.4	18.1	18.4	20.4	20.9	22.4	23.9	24.8	22.0	25.6	23.2	26.4	3.89	3.11;4.67	<0.001
	≥12	12.5	22.4	15.6	15.6	24.5	22.4	19.9	22.8	18.9	23.0	21.8	27.1	27.9	22.7	3.34	1.74;4.94	0.001
45-54	0-8	18.1	20.4	19.8	14.0	23.3	16.1	19.3	22.0	20.8	18.1	23.2	25.7	22.6	24.0	2.24	0.91;3.57	0.003
	9-11	14.4	18.8	19.1	18.9	21.8	21.0	20.6	21.9	20.3	22.4	22.9	21.5	23.9	22.4	2.19	1.34;3.04	<0.001
	≥12	13.4	19.3	17.4	22.0	24.1	24.3	23.0	21.5	18.2	20.2	23.9	23.7	22.3	25.4	2.73	0.27;5.19	0.032
55-64	0-8	12.1	16.9	18.1	21.9	14.4	17.3	19.0	23.3	26.5	23.8	19.9	21.8	20.8	26.5	3.73	1.24;6.22	0.007
	9-11	15.2	23.0	16.5	17.8	21.3	19.6	21.7	19.0	21.4	17.4	21.3	24.1	20.3	22.6	1.37	0.37;2.36	0.011
	≥12	14.1	17.8	18.8	20.6	14.3	14.6	21.8	23.3	16.9	24.4	18.8	18.8	19.7	22.8	1.98	-0.01;3.96	0.051
≥65	0-8	10.6	11.5	10.9	13.2	16.2	12.5	12.7	16.0	14.8	15.5	15.3	16.6	18.9	18.5	3.88	2.69;5.07	<0.001
	9-11	11.0	9.0	14.5	11.6	20.9	11.1	13.2	15.1	20.1	14.0	12.8	12.4	19.3	16.7	2.56	-0.21;5.32	0.067
	≥12	11.8	14.2	13.9	15.3	12.9	11.5	11.4	21.0	19.9	17.0	16.5	13.8	15.1	17.7	2.28	-0.96;5.53	0.151
Female
18-24	0-8	6.8	6.4	9.1	7.6	11.7	11.8	8.5	4.9	17.0	12.9	19.9	10.7	13.3	13.1	5.61	1.90;9.32	0.006
	9-11	4.1	3.4	3.3	6.3	5.4	5.0	5.4	5.2	8.0	8.8	8.5	7.3	7.5	9.8	6.93	4.39;9.46	<0.001
	≥12	2.5	2.4	3.2	2.8	2.5	3.3	8.6	3.2	3.9	5.0	6.9	8.7	8.1	10.5	10.79	6.33;15.25	<0.001
25-34	0-8	12.6	15.0	13.4	16.9	17.5	14.2	19.9	20.3	19.8	30.2	24.8	25.6	27.3	26.6	6.16	4.67;7.65	<0.001
	9-11	7.2	9.3	9.3	10.5	11.0	14.0	13.3	14.0	14.6	18.9	18.6	17.6	22.3	22.2	7.73	6.82;8.64	<0.001
	≥12	4.6	6.0	6.9	7.0	10.0	7.5	10.6	10.6	10.5	11.4	11.4	10.8	12.4	15.5	6.50	5.19;7.80	<0.001
35-44	0-8	14.4	16.4	18.7	20.0	20.5	21.2	24.2	24.1	28.6	29.3	31.0	24.2	25.4	25.9	4.06	1.44;6.67	0.005
	9-11	10.0	11.4	13.1	12.9	13.8	17.2	18.9	16.5	22.6	22.9	24.6	22.2	24.4	24.0	6.65	5.41;7.88	<0.001
	≥12	10.2	6.8	7.7	9.0	12.6	15.2	13.1	13.7	11.8	16.7	14.9	14.3	14.9	17.8	5.14	2.80;7.48	<0.001
45-54	0-8	18.8	22.2	21.5	22.3	24.9	26.9	28.6	25.6	25.1	28.1	27.8	27.9	29.7	30.8	3.02	1.97;4.06	<0.001
	9-11	14.4	15.9	15.8	18.4	18.2	20.7	22.3	21.0	22.4	21.7	21.1	23.0	24.8	24.9	3.72	2.67;4.77	<0.001
	≥12	8.6	14.2	11.2	11.4	13.7	17.3	17.2	20.8	14.9	15.8	15.1	15.2	17.8	18.3	3.56	0.80;6.32	0.016
55-64	0-8	23.0	21.9	25.7	23.6	24.8	26.8	29.2	30.3	26.4	25.8	28.6	26.3	33.1	27.1	1.88	0.88;2.89	0.002
	9-11	17.2	21.1	17.7	21.7	23.4	22.6	21.8	22.6	22.6	21.1	22.5	22.7	22.9	24.7	1.55	0.61;2.49	0.004
	≥12	16.0	11.7	18.0	16.1	14.9	15.7	20.5	16.7	11.7	17.8	18.7	17.5	22.1	17.0	1.98	0.21;3.75	0.031
≥65	0-8	20.8	19.8	22.4	21.6	23.4	22.6	22.9	24.1	23.3	22.9	24.0	25.2	25.1	24.0	1.39	0.97;1.81	<0.001
≥65	9-11	14.3	13.7	15.2	18.5	15.2	18.2	24.3	20.5	21.2	23.2	23.6	18.9	20.8	24.6	3.70	1.74;5.65	0.001
≥12	13.7	13.5	12.1	13.4	12.5	15.4	20.9	13.0	15.6	14.9	19.0	19.1	18.7	13.9	2.52	0.50;4.55	0.019