Artigos

INTRODUCTION

Obesity is a non-communicable disease (NCD) that arises from multifactorial causes involving complex interactions, including genetic predisposition, environmental and lifestyle causes, and socioeconomic, demographic, and cultural factors1,2. Pre-obesity and obesity represent an important public health problem that has shown an alarming increase worldwide, leading to serious impacts on health systems1,2. According to the World Obesity Federation3, one in four individuals worldwide will live with obesity by 2035, while more than half of the population, around 4 billion people, will be affected by pre-obesity. The combined economic impact is projected to reach 4.32 trillion dollars annually by 2035, nearly 3% of global Gross Domestic Product3.
Studies indicate a higher prevalence of obesity among women compared to men4,5. Specifically, women of reproductive age have been the target of several policies and actions for the prevention and management of obesity1,2. This phase is critical once is linked to significant impacts, including gestational complications, postpartum weight retention, increased childhood obesity risk2,6, reduced fertility, hormonal changes, and conditions such as Polycystic Ovary Syndrome7.
Moreover, this group has historically been the target of specific public health policies and programs, making it strategic for monitoring and evaluating the impact of these initiatives8. Documents such as the National Policy for Comprehensive Women’s Health Care (PNAISM)8 and the Global Strategy for Women’s, Children’s and Adolescents’ Health9 reinforce the centrality of this group within health agendas, particularly due to the intergenerational implications of their nutritional status. In addition, women of reproductive age face greater exposure to social and gender-based determinants—such as income inequality, unpaid domestic labor burden, food insecurity, and barriers to accessing health services—which shape obesity risk and deepen vulnerabilities8,9. Thus, focusing on this group enables a more sensitive assessment of the effects of implemented policies and the inequalities that shape the epidemiological profile of obesity in the country.
Among these women, increasing age may contribute to obesity because repeated exposure to the hormonal fluctuations inherent to menstrual cycles—combined with the physiological changes associated with pregnancy—promotes cumulative metabolic adjustments that increase the propensity for body fat accumulation10,11. Evidence also shows that lower levels of education are associated with a higher prevalence of obesity12,13,14. This relationship may be explained by greater knowledge of healthy nutrition and obesity-related health risks among individuals with higher educational attainment, which is often linked to higher income13. Higher income, in turn, facilitates access to components of a healthy diet, reduces barriers to adopting healthier eating patterns, and is associated with greater opportunities for physical activity13. Marital status is another sociodemographic factor related to obesity. Marriage has been associated with changes in Body Mass Index (BMI) and with behaviors that influence weight gain. This association may reflect several mechanisms, including shifts in lifestyle, dietary habits, and social support. Upon marrying, individuals often experience changes in their daily routines, which may modify health-related behaviors15.
In Brazil, obesity increased from 11.8% in 2006 to 20.3% in 2019, with projections estimating 29.6% of the population affected by 2030, particularly among women16. This trend contrast with the goals to stop the increase in obesity in the Strategic Action Plan for Tackle Chronic Diseases and Noncommunicable Diseases in Brazil, 2021-2030 (NCD Plan) in Brazil17 and the Sustainable Development goal that aims to reduce premature mortality from NCD by 30%, which requires addressing obesity18.
This study makes a relevant contribution by providing an integrated analysis of temporal trends and projections of pre-obesity and obesity among adult women of reproductive age, using recent and nationally representative data from the Brazilian population. By focusing on a subgroup that remains underexplored in the literature — whose vulnerability is shaped by hormonal, reproductive, and sociodemographic factors8,9 — the study contributes to a more comprehensive understanding of this aspect of women’s health. The findings generated are strategic for informing public policies and health promotion measures that aim to reduce the prevalence of obesity in this group or at least contain its increase. In this context, this study aimed to analyze the temporal trend in the prevalence of pre-obesity and obesity according to sociodemographic characteristics among adult Brazilian women of reproductive age between 2006 and 2023 and project this prevalence to 2030.

METHODS

Study Population
This is a time series study based on data from the Surveillance System for Risk and Protective Factors for Chronic Diseases by Telephone Survey (Vigitel)19, collected between 2006 and 2023. Vigitel is a population-based study conducted annually since 2006 to evaluate the adult population (?18 years old) residing in the 26 Brazilian capitals and Federal District through telephone interviews.

Participants and study sampling
Vigitel uses probabilistic sampling (systematic and stratified) conducted in two stages. In the first stage, residential telephone lines provided by the National Telecommunications Agency (Anatel, in Portuguese) are randomly selected and then subdivided into subsamples for each city. The second stage involves the selection of one of the adults (?18 years old) living in the selected household for the interview19,20. Furthermore, all Vigitel estimates are weighted to be representative of the entire adult population of each city. The post-stratification weight calculation utilizes the Rake method19 to equate the Vigitel sample with the estimated distribution of the total population, considering gender, age group, and level of education. Vigitel did not collect data in 2022 because the bidding process was completed only in July, and the contracted company withdrew shortly afterward. Therefore, no interviews were conducted that year. Additional details of the methodology are exposed in annual Vigitel publications19,20.
For this study, out of the 784,479 adults interviewed by Vigitel from 2006 to 2023, 544,500 were excluded, as they were not adult women of reproductive age. Therefore, data from 239,979 non-pregnant women (aged 18 to 49) were analyzed. Women of reproductive age are those between 10 and 49 years old21; however, since the Vigitel sample includes only adult individuals, women under 18 years old were not included in the sample.

Variables
The study population was characterized according to:
a) Explanatory variables:
• Sociodemographic characteristics: age (18 to 35, 36 to 40, and 41 to 49 years old); schooling (0 to 8, 9 to 11, and 12 or more years of study) and presence of partner/spouse (yes; no).
• Years of the survey: 2006 to 2023; 2006 to 2023; 2017 to 2023; 2030
b) Dependent variables
• Pre-obesity (or overweight) and obesity: classified according to nutritional status: BMI = [weight (kg)/height (m)2], calculated from self-reported weight and height information and classified as pre-obesity (25? BMI <30.0kg /m²)10,22 and obesity (BMI ?30kg/m²)10,22.
Data analysis
Initially, Brazilian women of reproductive age were described according to sociodemographic characteristics in each year. Furthermore, the prevalence of pre-obesity and obesity were estimated for the complete population of women and according to sociodemographic characteristics.
Time series analyses were performed for the complete period of the study (2006-2023) and for the most recent period (2017-2023). The latter period was specifically chosen to assess the effectiveness of health promotion programs and policies implemented by the government in this period17,21,23-25, some of which targeted women, and to reduce obesity prevalence among Brazilian adults17.
Prais-Winsten regression models were used to analyze the temporal trend of pre-obesity and obesity during the specified periods. The prevalence of pre-obesity and obesity (for the total women population and according to sociodemographic characteristics) served as dependent variables, while the years of the survey served as the explanatory variable. The angular coefficient of these models represented the annual average variation, expressed by percentage points of the increase or reduction of the indicator over the period and according to each sociodemographic characteristic and its respective categories, which allowed us to assess whether the prevalence differed significantly between the variable categories over time. Significant temporal variations were considered for values of p ?0.05. Additionally, these models were used to project obesity and pre-obesity prevalence for 2030 (for the total population of women and according to sociodemographic characteristics) based on observed trends from 2006 to 2023.
Data processing and analysis were performed using Stata software, version 16 (https://www.stata.com). Vigitel database is available for public access and use (https://svs.aids.gov.br/daent/cgdnt/vigitel/) and the project was approved by the National Ethics Committee for Research with Human Beings of the Ministry of Health (CAAE: 65610017.1.0000.0008). Informed consent was obtained verbally in the telephone interviews.

RESULTS

Among the 239,979 Brazilian adult women of reproductive age, a reduction was observed among the youngest (-0.11 pp/year) and an increase among women aged 41 to 49 years (0.04 pp/year) for the complete period. Regarding schooling, there was a reduction among women with lower educational levels (-1.39 pp/year) and an increase among those with 12 or more years of education (1.23 pp/year) for the complete period. For the recent period, the reduction in lower educational levels continued (-0.91 pp/year). Concerning the presence of partner/spouse, a change was observed in both the complete and recent periods, with a reduction among women without a partner/spouse and an increase among women with a partner/spouse (Table 1).
Over the complete period (2006-2023), the prevalence of pre-obesity increased in the total population (0.61 pp/year) and across all sociodemographic groups. The most significant increases were observed among women aged 18 to 35 years (0.75 pp/year), those with 12 or more years of study (0.86 pp/year), and without a partner/spouse (0.78 pp/year). In the most recent period (2017-2023), while the overall prevalence of pre-obesity remained stable, specific subgroups showed increases, women aged 18 and 35 years (0.49 pp/year), those with 0 to 8 (0.97 pp/year) years of schooling, and without a partner/spouse (0.40 pp/year). The projected prevalence of pre-obesity for 2030 in the total population is 36.9%, with higher projections among women aged 36 to 40 years (39.5%), with 0 to 8 years of schooling (42.3%) and with a partner/spouse (40.7%) (Table 2).
Over the complete period (2006-2023), there was an increase in the obesity prevalence in the total population (0.79 pp/year) and across all sociodemographic groups. The highest increases were among women aged 36 to 40 years (1.03 pp/year), those with 9 to 11 years of schooling (1.07 pp/year), with a partner/spouse (0.90 pp/year). In the recent period (2017 to 2023), the increase in the prevalence of obesity intensified (1.19 pp/year), with the highest increases among women aged 36 to 40 years (2.32 pp/year), those with more than 12 years of study (1.59 pp/year), followed by those with 9 to 11 years of schooling (1.41 pp/year) and with a partner/spouse (1.30 pp/year). The projected prevalence of obesity for 2030 in the total population is 28.0%, with higher projections among women aged 36 to 40 years (35.2%), with 0 to 8 years of schooling (33.2%) and among women who are married or in a stable union (33.2%) (Table 3).

DISCUSSION

This study demonstrates that pre-obesity and obesity have increased among adult women of reproductive age in Brazil from 2006 to 2023, with upward trends observed across all age groups, schooling levels, and partner/spouse status. However, in the most recent period (2017-2023), while the pre-obesity prevalence stabilized, obesity rates accelerated. Furthermore, during this period, pre-obesity increased among younger women (18-35 years), those with lower levels of schooling, and without a partner/spouse. Obesity, in the same period, increased among women of all age groups, those women with more than 9 years of schooling, and regardless of the presence of partner/spouse. Projections for 2030 indicate continued increases in pre-obesity and obesity across the total population and all age groups, education levels, and presence of partner/spouse.
The increasing prevalence of pre-obesity and obesity is described as a global epidemic11, especially among women26. In over 20 countries, women's obesity rates exceed men's by 10%26. For women of reproductive age, this trend increases risks for NCD and complications such as pre-eclampsia, gestational diabetes12, and early fetal/neonatal death27. Maternal obesity also contributes to children’s weight gain and long-term NCD risk6,12. In Brazil, projections show women will be most affected by the obesity epidemic16.
This research demonstrated that in the most recent period, pre-obesity remained stable while obesity increased. This underscores the challenge for public health policies focused on preventing and controlling obesity among women in the pre-obesity stage to halt the progression of obesity and related comorbidities. The early onset of pre-obesity among younger women (18-35 years), emphasizing the need for targeted primary prevention measures, such as comprehensive policies to protect people from the harmful impact of food marketing; nutrition labelling policies (including front-of-pack labelling); fiscal policies (including taxes and subsidies to promote healthy diets); public food procurement and reformulation policies; as well as physical activity18.
The rising trend in weight gain among these women may be linked to changes in the dietary habits. In Brazil, the Household Budget Survey 2017/201828 revealed across the entire population an increase in the consumption of sugars, fats, and Ultra-Processed Foods (UPF), alongside a displacement of natural and minimally processed options28. These changes in the food environment affect all social groups and are strongly shaped by food availability, pricing, marketing strategies, and time constraints, rather than individual choices alone.
Obesity is also determined by a broad set of socioeconomic, demographic, and structural health determinants in both, high and low-income countries1,13. In the present study, the recent rise in obesity among women with higher schooling may reflect the effects of higher income, which is associated with greater purchasing power and access to a wide range of foods, including energy-dense and ultra-processed products14. Furthermore, higher educational attainment is often associated with occupational transitions that involve less physical activity during working hours, which may contribute to changes in overall energy expenditure at the population level. In a study29 using data from the Korean National Health and Nutrition Examination Survey, Phases I to VI (1998–2015), which analyzed trends in obesity prevalence among adult Korean workers, female workers in the sales and service sectors—classified as non-manual/intellectual occupations—showed a notably lower prevalence of obesity, with a significant decline from 30.8% to 23.9% (p for trend = 0.0048) over the study period29. Importantly, these patterns should not be interpreted as individual behavioral shortcomings, but rather as outcomes of broader social, economic, and labor-market dynamics that shape health risks differently across social groups.
The increase of obesity in the Brazilian population reflects a global trend observed in recent decades. However, the Covid-19 pandemic has further exacerbated this issue in Brazil. Between 2020 and 2023, 708,638 deaths from Covid-19 shifted healthcare priorities30, worsening obesity rates31. Studies identify Covid-19 as a risk factor for the NCD, including obesity32,33. Social isolation during the pandemic intensified sedentary habits, increased the consumption of UPF, and reduced fruits and vegetables intake, contributing to weight gain34,35. This pattern was particularly evident among women with lower levels of education during the most recent period of the study (2017–2023), which coincided with the pandemic. However, it is important to emphasize that the eating behaviors observed in this group cannot be attributed solely to poor dietary choices34; rather, they must be understood within the broader context of food and nutrition insecurity and limited access to food during that period26.
The study also demonstrated, in the most recent period, an increase in the prevalence of obesity among women regardless of marital status and pre-obesity among unmarried women. Although marital status can influence obesity in women, evidence shows that this association is strongly shaped by socioeconomic and cultural factors. In some populations, single women may present an equal or even higher prevalence of obesity, particularly when social disadvantage is considered, highlighting that the relationship between marital status and obesity is complex and context-dependent15,36.
Although new strategies are needed to slow the rise in obesity and pre-obesity among women of reproductive age by 2030, it is important to question whether solutions should focus solely on the Unified Health System (SUS, in Portuguese) or depend exclusively on improving the quality of care provided to women. While improvements are always possible, substantial progress in women’s health indicators has been documented over the 35 years of the SUS. Moreover, national policies that operate partly within the SUS—such as the PROTEJA Strategy37, which promotes healthy eating and nutrition education for families and school communities, and the National Plan of Policies for Women (PNPM)38, which coordinates multisectoral actions for comprehensive women’s health—already address key determinants of obesity. Within this broader policy context, the NCD Plan aimed to maintain obesity prevalence among adults at 25.9%17. However, projections show it will reach 28% among women of reproductive age by 2030, higher for those aged 36 to 40, those with less education, and those who in stable relationships. The current study also demonstrated increased pre-obesity rates in the total population of women and the same sociodemographic groups, reinforcing the need for intersectoral strategies that extend beyond the health system alone.
In addition to the NCD Plan, the Ministry of Health has developed initiatives to contain the advancement of obesity and other NCD in the country, such as the Basic Care Notebook number 3824, the Women's Health Protocol21, that add up to others clinical guidelines for the treatment of obesity within the scope of Primary Health Care25. There is also the Dietary Guide for the Brazilian Population23, which recommends avoiding UPF and prioritizing a diet based on in natura and minimally processed foods23, a key strategy for eating healthily and preventing the obesity and other NCD23. However, the lack of significant results in reducing obesity prevalence, especially among women of reproductive age, it is not possible to conclude that the policies implemented within the Brazilian Unified Health System have been ineffective. Policies such as the National Food and Nutrition Policy (PNAN)39, the National Health Promotion Policy (PNPS)40, and the PNAISM8 have contributed to important advances in women’s health over the 35 years of the SUS41, and without them, the current prevalence of obesity might be even higher. Thus, interpreting the upward trend in obesity requires situating it within a broader context of social inequalities, changes in the food environment, and socioeconomic determinants1,2, highlighting the need for comprehensive and intersectoral strategies for obesity prevention and control.
The use of self-reported data can represent a limitation due to the tendency of under-reporting of weight by the female population, however, the use of self-reported weight and height by women of reproductive age has already been validated in a previous study42. Furthermore, until 2021, the Vigitel sample consisted only of individuals living in households with a landline, located in the Brazilian capitals and Federal District, which could harm the representation of some sociodemographic groups. However, this limitation is overcome through the application of sampling weight to correct possible selection biases19,20. The absence of 2022 data also represents a limitation due to the one-year discontinuity in the series. However, this gap does not compromise the analyses, as the evaluated period still includes pre- and post-pandemic years with large, representative samples. The use of post-stratification weights (Rake method), calibrated with Census data and official population projections, further strengthens the estimates19.

CONCLUSION

This research spanning seventeen years, highlights rising trends in pre-obesity and obesity among adult’s Brazilian women of reproductive age, regardless of age, education, and presence of partner spouse. The projection for 2030 shows nearly 3 in 10 women will present obesity, missing the goals set by the NCD Plan. From 2017 to 2023, pre-obesity stabilized while obesity intensifies, especially among younger, less educated, and without a partner/spouse. In this scenario, effective responses must go beyond individual recommendations and focus on structural and intersectoral policies that create real conditions for healthier living. This includes improving access to adequate and healthy food, expanding community opportunities for safe and feasible physical activity, and addressing socioeconomic constraints that shape daily life for women of reproductive age. Strategies that integrate health, social protection, education, and urban policies are more likely to produce meaningful reductions in obesity than isolated lifestyle-oriented approaches.

ACKNOWLEDGMENTS

The authors are grateful to the Brazilian Ministry of Health (TED 37/2022), the Research Supporting Foundation of Minas Gerais [Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) in Portuguese] [grant number 5.18/2022], the Brazilian Coordination of Superior Level Staff Improvement [Coordenac?a?o de Aperfeic?oamento de Pessoal de Nível Superior (CAPES) in Portuguese] [grant number 001], and the National Council for Scientific and Technological Development [Conselho Nacional de Desenvolvimento Científico e Tecnolo?gico (CNPQ) in Portuguese] [grant number 311170/2019-6] for the grants.

DECLARAÇÃO DE DISPONIBILIDADE DE DADOS
As fontes dos dados utilizados na pesquisa estão indicadas no corpo do artigo.

REFERENCES

1. World Health Organization (WHO). Obesity and overweight fact sheet. Geneva: WHO; 2016. [cited 2019 Dec 19]. Available from: http://www.who.int/mediacentre/factsheets/fs311/en/

2. World Health Organization (WHO). Health service delivery framework for prevention and management of obesity. Geneva: WHO; 2023. [cited 2025 Jan 13]. Available from: https://iris.who.int/bitstream/handle/10665/367784/9789240073234-eng.pdf?sequence=1

3. World Obesity Federation. World Obesity Atlas 2023. [cited 2025 Jan 14]. Available from: https://data.worldobesity.org/publications/WOF-Obesity-Atlas-V5.pdf

4. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population representative studies with 222 million children, adolescents, and adults. Lancet 2024;403(10431):1027–1050.

5. Pan American Health Organization (PAHO); World Health Organization (WHO): Americas Region. Overweight and obesity. [cited 2025 Aug 3]. Available from: https://www.paho.org/en/enlace/overweight-and-obesity

6. Voerman E, Santos S, Patro Golab B, Amiano P, Ballester F, Barros H, et al. Maternal body mass index, gestational weight gain, and the risk of overweight and obesity across childhood: an individual participant data meta-analysis. PLoS Med 2019;16(2):e1002744.
7. Cena, H, Fiechtner L, Vincenti A, Magenes VC, De Giuseppe R, Manuelli M, Zuccotti GV, Calcaterra V. COVID-19 Pandemic as Risk Factors for Excessive Weight Gain in Pediatrics: The Role of Changes in Nutrition Behavior. A Narrative Review. Nutrients 2021;13(12):4255.

8. Brasil. Ministério da Saúde (MS). Política Nacional de Atenção Integral à Saúde da Mulher: princípios e diretrizes. Brasília: MS; 2004. [cited 2025 Nov 26]. Available from: https://bvsms.saude.gov.br/bvs/publicacoes/politica_nac_atencao_mulher.pdf

9. Pan American Health Organization (PAHO). World Health Organization (WHO): Americas Region. Global Strategy for Women’s, Children’s and Adolescents’ Health (2016–2030). Geneva: WHO; 2016. [cited 2025 Nov 28]. Available from: https://www.paho.org/pt/documentos/global-strategy-womens-childrens-and-adolescents-health-2016-2030-disponivel-somente-em

10. World Health Organization (WHO). Obesity: preventing and managing the global epidemic. Report of a World Health Organization Consultation. WHO Obesity Technical Report Series, n. 894. Geneva: WHO; 2000. [cited 2025 Jan 6]. Available from: https://portaldeboaspraticas.iff.fiocruz.br/wp-content/uploads/2018/05/WHO_TRS_894.pdf

11. Kinnunen TI, Bastola K, Neupane S. Trends in the Prevalence of Overweight and Obesity Among Women of Reproductive Age. J Nepal Health Res Counc 2021; 19(2):252-258.

12. Araújo FG, Velasquez-Melendez G, Felisbino-Mendes MS. Prevalence trends of overweight, obesity, diabetes and hypertension among Brazilian women of reproductive age based on sociodemographic characteristics. Health Care Women Int 2019;40(4):386–406.

13. Kholmatova K, Krettek A, Leon DA, Malyutina S, Cook S, Hopstock LA, Lovsleteen O, Kudryavtsev AV. Obesity Prevalence and Associated Socio-Demographic Characteristics and Health Behaviors in Russia and Norway. Int J Environ Res Public Health 2022; 19(15):9428.

14. Oguoma VM, Coffee NT, Alsharrah S, Abu-Farha M, Al-Refaei FH, Al-Mulla F, Daniel M. Prevalence of overweight and obesity, and associations with socio-demographic factors in Kuwait. BMC Public Health 2021; 21(667):1-13.

15. Nikolic Turnic, T, Jakovljevic V, Strizhkova Z, Polukhin N, Ryaboy D, Kartashova M, Korenkova M, Kolchina V, Reshetnikov V. The Association between Marital Status and Obesity: A Systematic Review and Meta-Analysis. Diseases 2024; 12(7):146.

16. Estivaleti JM, Guzman-Habinger J, Lobos J, Azeredo CM, Claro R, Ferrari G, Adami F, Rezende LFM. Time trends and projected obesity epidemic in Brazilian adults between 2006 and 2030. Sci Rep 2022;12(12699):1-8.

17. Brasil. Ministério da Saúde (MS). Secretaria de Vigilância em Saúde. Departamento de Análise em Saúde e Vigilância de Doenças Não Transmissíveis. Plano de Ações Estratégicas para o Enfrentamento das Doenças Crônicas e Agravos não Transmissíveis no Brasil 2021-2030. Brasília: MS; 2021. [cited 2025 Jan 6]. Available from: https://www.gov.br/saude/pt-br/centrais-de-conteudo/publicacoes/svsa/doencas-cronicas-nao-transmissiveis-dcnt/09-plano-de-dant-2022_2030.pdf

18. World Health Organization (WHO). Acceleration plan to stop obesity. Geneva: WHO; 2023. [cited 2025 Jan 6]. Available from: https://iris.who.int/bitstream/handle/10665/370281/9789240075634-eng.pdf?sequence=1
19. Brasil. Ministério da Saúde (MS). Vigitel Brasil, 2023: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico. Brasília: MS; 2023. [cited 2025 Jan 6]. Available from: https://www.gov.br/saude/pt-br/centrais-de-conteudo/publicacoes/svsa/vigitel/vigitel-brasil-2023-vigilancia-de-fatores-de-risco-e-protecao-para-doencas-cronicas-por-inquerito-telefonico

20. Brasil. Ministério da Saúde (MS). Vigitel Brasil 2022: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico. Brasília: MS; 2022. [cited 2025 Jan 6]. Available from: https://bvsms.saude.gov.br/bvs/publicacoes/vigitel_brasil_2021.pdf

21. Brasil. Ministério da Saúde (MS). Protocolo da atenção básica – saúde das mulheres. Brasília: MS; 2016. [cited 2025 Jan 13]. Available from: https://bvsms.saude.gov.br/bvs/publicacoes/protocolos_atencao_basica_saude_mulheres.pdf

22. World Obesity Federation. World Obesity. Obesity classification. 2022. [cited 2025 Jan 6]. Available from: https://www.worldobesity.org/about/about-obesity/obesity-classification

23. Brasil. Ministério da Saúde (MS). Secretaria de Atenção à Saúde. Departamento de Atenção Básica. Guia alimentar para a população brasileira. 2. ed. Brasília: MS; 2014. [cited 2025 Jan 6]. Available from: https://bvsms.saude.gov.br/bvs/publicacoes/guia_alimentar_populacao_brasileira_2ed.pdf

24. Brasil. Ministério da Saúde (MS). Estratégias para o cuidado da pessoa com doença crônica: obesidade. Cadernos de atenção básica, n. 38. Brasília: MS; 2014. [cited 2025 Jan 13]. Available from: https://bvsms.saude.gov.br/bvs/publicacoes/estrategias_cuidado_doenca_cronica_obesidade_cab38.pdf
25. Brasil. Ministério da Saúde (MS). Manual de como organizar o cuidado de pessoas com doenças crônicas na APS no contexto da pandemia. Brasília: MS; 2020. [cited 2025 Jan 13]. Available from: https://www.gov.br/saude/pt-br/arquivos/2020/manual_como-organizar-o-cuidado-de-pessoas-com-doencas-cronicas-na-aps-no-contexto-da-pandemia.pdf

26. Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO), Fondo Internacional de Desarrollo Agrícola (FIDA), Organización Panamericana de la Salud (OPS), Programa Mundial de Alimentos (WFP), Fondo de las Naciones Unidas para la Infancia (UNICEF). Panorama de La seguridad alimentaria y nutriciónen América Latina y el Caribe 2020. [cited 2025 Jan 13]. Available from: https://iris.paho.org/bitstream/handle/10665.2/53143/9789251336656_fao_spa.pdf?sequence=1&isAllowed=y

27. Colli EH, Campos CBS, Tavares MASB, Lacerda AB, Silva BMBA, Oliveira FA, Silvestre MEA, Benítez DFD, Davalos SRA, Mosciaro HA, Nogueira FF, Battini CT, Santos TF. Obesity: major maternal-fetal disorders. Braz J Develop. 2022;8(9):64863–64873.

28. Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa de orçamentos familiares 2017-2018: avaliação nutricional da disponibilidade domiciliar de alimentos no Brasil. Rio de Janeiro: IBGE; 2020. [cited 2025 Jan 13]. Available from: https://biblioteca.ibge.gov.br/visualizacao/livros/liv101704.pdf

29. Lee JY, Lee YR, Kim HR, Myong JP, Kang MY. Trends in obesity prevalence by occupation based on Korean National Health and Nutrition Examination Survey from 1998 to 2015. Saf Health Work 2020;11(1):97–102.

30. Brasil. Departamento de informática do Sistema Único de Saúde do Brasil (DATASUS). Secretaria de Vigilância em Saúde e Ambiente do Ministério da Saúde. Painel de casos de doença pelo coronavírus 2019 (COVID-19) no Brasil pelo Ministério da Saúde, 2023. [cited 2024 Mar 29]. Available from: https://covid.saude.gov.br/

31. Malta DC, Gomes CS, Silva AG, Cardoso LSM, Barros MBA, Lima MG, Souza Junior PRB, Szwarcwald CL. Use of health services and adherence to social distancing by adults with Noncommunicable Diseases during the COVID-19 pandemic, Brazil, 2020. Cienc Saúde Colet 2021;26(7):2833-2842.

32. Heinberg LJ, Steffen K. Social Isolation and Loneliness During the COVID-19 Pandemic: Impact on Weight. Curr Obes Rep 2021; 10(3):365-370.

33. Borgatti AC, Schneider-Worthington CR, Stager LM, Krantz OM, Davis AL, Blevins M, Howell CR, Dutton GR. The COVID-19 pandemic and weight management: Effective behaviors and pandemic-specific risk factors. Obes Res Clin Pract 2021;15(5):518–521.

34. Malta DC, Szwarcwald CL, Barros MBA, Gomes CS, Machado IE, Souza Júnior PRB, Romero DE, Lima MG, Damacena GN, Pina MF, Freitas MIF, Werneck AO, Silva DRP, Azevedo LO, Gracie R. The COVID-19 Pandemic and changes in adult Brazilian lifestyles: a cross-sectional study, 2020. Epidemiol Serv Saude 2020;29(4):e2020407.

35. Andrade GC, Levy RB, Leite MA, Rauber F, Claro RM, Coutinho JG, Mais LA. Changes in dietary markers during the covid-19 pandemic in Brazil. Rev Saude Publica 2023;57(1):54.

36. Sobal J, Rauschenbach BS, Frongillo EA Jr. Marital status, fatness and obesity. Soc Sci Med 1992;35(7):915–923.

37. Brasil. Ministério da Saúde (MS). Estratégia PROTEJA – Programa de Prevenção da Obesidade Infantil. Brasília: MS; 2021. [cited 2025 Dec 12]. Available from: http://189.28.128.100/dab/docs/portaldab/publicacoes/orienta_proteja.pdf

38. Brasil. Presidência da República. Secretaria Especial de Políticas para as Mulheres. Plano Nacional de Políticas para as Mulheres (PNPM). Brasília: Secretaria Especial de Políticas para as Mulheres; 2004. [cited 2025 Dec 12]. Available from: https://bvsms.saude.gov.br/bvs/publicacoes/PNPM.pdf

39. Brasil. Ministério da Saúde (MS). Política Nacional de Alimentação e Nutrição (PNAN). Brasília: MS; 2014. [cited 2025 Nov 26]. Available from: https://bvsms.saude.gov.br/bvs/publicacoes/politica_nacional_alimentacao_nutricao.pdf

40. Brazil. Secretariat of Health Surveillance. Department of Noncommunicable Diseases Surveillance an Health Promotion. National Health Promotion Policy: PNPS: Annex I of the Consolidation Ordinance No. 2, dated September 28, 2017, which consolidates the norms on the national health policies of SUS / Ministry of Health, Secretariat of Health Surveillance, Secretariat of Health Care. Brasília: Ministry of Health; 2019. [cited 2025 Nov 26]. Available from: https://bvsms.saude.gov.br/bvs/publicacoes/national_health_promotion_policy_2019.pdf

41. Brasil. Presidência da República. Orgulho do Brasil, SUS completa 35 anos. 2025. [cited 2025 Nov 26]. Available from: https://www.gov.br/planalto/pt-br/acompanhe-o-planalto/noticias/2025/09/orgulho-brasil-sus-completa-35-anos
42. Huber LRB. Validity of Self-reported Height and Weight in Women of Reproductive Age. Matern Child Health J 2007;11(2):137-144.