Dietary patterns and associated factors among quilombola older adults: a latent class analysis

Abstract  This study aimed to identify dietary consumption patterns among quilombola older adults and examine the associations between these patterns and the individuals’ socioeconomic and demographic characteristics. This is a cross-sectional study conducted with a census of 236 older adults (aged ≥60 years) from 11 quilombola communities in the municipality of Bequimão, Maranhão, Brazil. The prevalence of dietary markers was assessed. Latent Class Analysis was applied to identify the most common dietary patterns. Relative frequencies were calculated, and associations between socioeconomic variables and latent classes were tested using Pearson’s chi-square test or the Mann-Whitney test (α=0.05). Two dietary patterns were identified: ‘Unhealthy and hypercaloric diet’ (13.6%) and ‘Unhealthy diet’ (86.4%). Moreover, sex, age, and educational level were associated with the identified patterns. The older adults exhibited dietary patterns characterized by the accumulation of unhealthy food consumption, associated with socioeconomic and demographic conditions. Targeted interventions are essential to reduce unhealthy dietary behaviors and promote more equitable opportunities for food and nutrition security.

Keywords
Older Adults; Groups of African Descent; Chronic Diseases; Latent Class Analysis.

Resumo

Este estudo buscou identificar padrões de consumo alimentar que ocorrem entre pessoas idosas quilombolas e examinar a associação desses padrões identificados com as características socioeconômicas e demográficas desses indivíduos. Trata-se de um estudo transversal realizado com um censo de 236 pessoas idosas (≥60 anos de idade) de 11 comunidades quilombolas da cidade de Bequimão, Maranhão, Brasil. Foi investigada a prevalência de marcadores de alimentação. A análise de Classe Latente foi utilizada para se identificar os padrões alimentares mais comuns desse consumo. Foram calculadas as frequências relativas e verificadas associações das variáveis socioeconômicas com as classes latentes por meio do teste de qui-quadrado de Pearson ou do teste de Mann-Whitney (α=0,05). Foram identificados dois padrões de consumo: ‘Alimentação não saudável e hipercalórica’ (13,6%) e ‘Alimentação não saudável (86,4%)’. Além disso, sexo, idade e escolaridade estiveram associados aos padrões identificados. As pessoas idosas apresentaram padrão de consumo alimentar que caracteriza o acúmulo de consumo não saudável e que estão associados às características socioeconômicas e demográficas. Intervenções específicas são essenciais para reduzir o consumo alimentar não saudável e favorecer oportunidades de segurança alimentar e nutricional mais equitativas.

Palavras-chave
Pessoas Idosas; Grupos de Ascendência Africana; Doenças Crônicas; Análise de Classe Latente.

INTRODUCTION

Afro-descendant communities located in rural and remote areas, formed by individuals who escaped from the slavery system, are found in several Latin American countries. In Brazil, these communities are known as quilombolas. Data from the Brazilian Institute of Geography and Statistics (IBGE)¹ indicate that 0.65% of the Brazilian population (n = 1,327,802) identifies as quilombola. The Northeast region is home to the largest share of this population (n = 905,415; 68.2%), followed by the state of Maranhão (n = 269,074; 20.6%), where they are distributed across 81 territories encompassing 34 municipalities².

The subsistence of quilombola communities is largely based on traditional practices such as artisanal fishing, the cultivation of legumes, fruits, and vegetables, plant extractivism, and small-scale animal husbandry³. However, their food culture and dietary practices are also shaped by the socioeconomic and environmental challenges they have faced over the centuries⁴,⁵.

Most quilombola communities face profound social vulnerability, precarious healthcare services, and state neglect in supporting local production, coupled with limited access to healthy foods. These factors restrict access to healthcare guidance and services, reduce adherence to healthy behaviors, and increase the risk of morbidity and mortality from chronic conditions⁶,⁷.

An analysis of changes in health behaviors among older adults in Brazil using data from the 2013 and 2019 editions of the National Health Survey (PNS) revealed that healthy behaviors—such as the consumption of fruits, vegetables, and legumes, and engagement in physical activity—were more prevalent in the country’s capital cities. The promotion of healthy habits relies more on actions related to income access, food production and commercialization, price control, taxation, subsidies, and urban infrastructure than on awareness campaigns or general population advertising⁸. In the quilombola context, this issue is exacerbated, as these communities are even more vulnerable, with most of their settlements located far from urban centers⁶.

Healthy eating is vital for health, functional capacity, and psychological well-being, especially among the older adult population⁹, as it helps the body maintain balance and prevent diseases⁹. However, the dietary intake of quilombola older adults is marked by food and nutrition insecurity, which negatively affects their nutritional status, aggravates existing health problems, and limits their quality of life¹⁰,¹¹.

Previous studies have found that quilombola older adults present a high prevalence of underweight, loss of muscle mass, and elevated cardiovascular risk, with greater risk observed among women and older age groups¹². The prevalence of functional impairment, physical inactivity during leisure time, alcohol abuse, and chronic-degenerative conditions is also higher in this population than among older adults in the general population¹³,¹⁴.

Quilombola older adults represent a socially vulnerable segment of the population whose living conditions are marked by historical and persistent inequalities⁷. The various healthcare demands and needs of this population highlight the importance of studying their dietary conditions. However, dietary consumption patterns among quilombola older adults have not yet been identified, which still hinders the provision of health care tailored to subgroups of older adults with less healthy health patterns.

Therefore, this study aims to identify dietary consumption patterns frequently observed among quilombola older adults living in contexts of social and health vulnerability. In addition, it examines the association between the identified patterns and the socioeconomic and demographic characteristics of this population.

METHOD

This is a cross-sectional, household-based study conducted in 11 quilombola remnant communities in the municipality of Bequimão, Maranhão, Brazil. All communities are officially recognized as remnant quilombola communities by the Palmares Cultural Foundation (Fundação Cultural Palmares – FCP), which is linked to the Ministry of Culture (Map 1). The STROBE protocol was adopted for the writing of this article.

Figure 1
Geographic location of the communities and residences of quilombola older adults in Bequimão, Maranhão, Brazil, 2020.

This study is part of the project "Population-Based Survey on the Living and Health Conditions of Quilombola Older Adults in a City in the Baixada Maranhense Region" (IQUIBEQ Project). Data were collected from individuals aged 60 years or older residing in quilombola communities. Based on a survey conducted by the municipal Department of Social Assistance and Community Health Workers (CHWs) from the respective communities, a total of 245 older adults were identified. All were invited to participate in the research through door-to-door outreach efforts by CHWs and community leaders. However, due to refusals, difficulty locating individuals in the communities after two separate attempts, and the presence of cognitive impairment (identified through the Mini-Mental State Examination)¹⁵ that could compromise comprehension of the questions, the final study sample consisted of 236 participants aged 60 years or older (96.3%). Considering the bilateral difference between comparison groups and a 5.0% significance level, this final sample yielded a statistical power of 98.1%.

Data collection was conducted on weekdays during business hours, from July 2018 to April 2019, in the participants’ homes and community spaces (churches and associations). The study used data collected through a questionnaire adapted from the 2013 National Health Survey, which addressed socioeconomic and demographic conditions, health status, and access to and use of healthcare services by the respondents. The questions related to dietary intake were extracted from the 2013 Vigitel survey.

Dietary consumption was identified through self-reporting by participants. The analysis included the consumption of healthy foods: vegetables or legumes (raw or cooked), fruits, and beans at least five days per week (yes, no), and fish at least three days per week (yes, no); and unhealthy foods: fatty red meat, chicken with skin, and whole milk at least one day per week (yes, no), as well as the consumption of sweetened beverages (soda and artificial juice) and sweets at least three days per week (yes, no).

Among the socioeconomic and demographic variables considered were: sex (male or female), age (in years), literacy (yes or no), housing adequacy (whether the materials used for the construction of walls, roof, and floor are collectively adequate and ideal or not), and family income in terms of the minimum wage of 954.00 BRL (in 2018) (<1 minimum wage or 1 to 2 minimum wages). The normality of numerical variables was tested using the Shapiro-Wilk test.

The prevalence of dietary consumption markers and socioeconomic and demographic variables was estimated. To identify recurrent food combinations, all items were tested in pairs through possible combinations (C⁹2=36). The combinations were complemented with Pearson’s chi-square (χ2) test or Fisher's Exact test to identify statistically significant associations or food types associated with each other (α < 0.05).

Latent Class Analysis (LCA) was used to identify frequently occurring consumption patterns. LCA is a statistical procedure used to identify unobserved homogeneous (latent) subgroups within a population. The approach generates subgroups that are homogeneous internally and heterogeneous between themselves¹⁶-¹⁸. In this study, the subgroups were identified based on the self-reported categorical responses collected on the mentioned foods. Thus, a probability value was assigned to each older adult to determine their classification within the identified groups.

A series of latent class models was fitted before selecting the optimal number of latent classes. The existing literature does not define a definitive method for determining the best criteria. However, the most widely accepted are statistical criteria¹⁶-¹⁸. For this study, multiple fit statistics were considered, along with parsimony and theoretical interpretability. The selection criterion was based on several model fit indices: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), adjusted Bayesian Information Criterion (aBIC), and consistent Akaike Information Criterion (cAIC)¹⁶-¹⁸. These criteria are well-established in the literature for selecting the number of latent classes. Parsimony and theoretical interpretability were preferred for identifying the optimal number (n) of disease classes. Additionally, fit quality χ2, G2 statistics, entropy, and likelihood ratio tests were reported¹⁶-¹⁸. Furthermore, the class with the highest entropy was selected as the one that best defined the dietary consumption patterns.

These ‘n’ classes were labeled based on the response probabilities for the selected chronic morbidity items included¹⁶-¹⁸. The existing literature suggests that item response probabilities are similar to factor loadings¹⁶,¹⁷. Therefore, a loading threshold of 0.3 or higher was recommended for defining a particular factor¹⁶-¹⁸. Thus, a cutoff point of 0.3 was chosen for assigning labels. In this study, an item related to a type of consumption, with a response probability of ≥0.3, represented a strong association with the identified latent class pattern. Therefore, this item was considered informative and used for assigning labels to a specific latent class.

Once the number of classes was determined, relative frequencies were calculated, and associations between socioeconomic and anthropometric variables and the latent classes were examined using Pearson’s chi-square test or the Mann-Whitney test. Additionally, the Lo-Mendell-Rubin ad-hoc adjusted likelihood ratio test was performed to verify statistically significant differences in the likelihood ratio between the selected latent class model and the immediately higher-class model¹⁹. Normality was tested for the age variable. A significance level of 5% was adopted in all analyses.

After data collection, the data were transferred for analysis using the open-source software RStudio, version 2022.7.2.576 (R Foundation for Statistical Computing, Boston, United States of America). The “poLCA” package was used.

The research was approved by the Research Ethics Committee for studies involving human subjects (approval number: 3.121.981) and was conducted following ethical guidelines for human research outlined in Resolution number 466/2012. All participants signed the Informed Consent Form (ICF).

DATA AVAILABILITY

The complete dataset supporting the results of this study is available upon request from the corresponding author.

RESULTS

This study included 236 older adults (≥60 years old), representing 96.3% of the total population identified across the eleven communities. This sample had a statistical power of 98.1% for the tests performed. The median age was 69 years (Q25: 64; Q75: 77). Figure 2 illustrates the dietary consumption profile, relationships, and prevalence of all possible bidirectional combinations between the selected food types. The values in the diagonal cells (shaded in gray) represent the prevalence of all foods included in the study. Among healthy foods, the highest prevalence was for fish consumption (80.1%) and fruit (45.8%), while the lowest was for beans (6.4%) and vegetables and legumes (12.7%). For unhealthy foods, the highest prevalence was for whole milk consumption (68.2%) and chicken with skin (18.2%), while the lowest was for sweets (7.6%) and sweetened beverages (14.8%). The values outside the diagonal represent the prevalence (%) of all possible bidirectional combinations. Additionally, the blue color indicates associations between two food items in specific combinations.

Figure 2
Prevalence (%) of healthy and unhealthy dietary consumption and their association based on the combination of healthy and unhealthy food types among quilombola older adults aged ≥60 years (N=236). Bequimão (IQUIBEQ Project), MA, Brazil, 2019.

The study explored 36 consumption type combinations (C⁹2=36), of which five were statistically significant (p-value<0.05): fruits with vegetables and legumes, beans and fruits, fatty red meat and fish, chicken with skin and fatty red meat, and whole milk with sweets. Among healthy food combinations, only one had a prevalence below 10.0%, while the highest were fruit and beans consumption (40.0%; p-value<0.05) and fish and fruit (46.0%). For unhealthy food combinations, all had a prevalence equal to or greater than 10.0%, with the highest being fatty red meat and chicken with skin (39.0%; p-value<0.05) and sweets and chicken with skin (27.8%). Lastly, the combination of healthy and unhealthy foods revealed that most had a prevalence above 10.0%, with the highest prevalence observed for foods associated with fish, such as chicken with skin (81.4%), sweets (83.3%), whole milk (82.6%), sweetened beverages (74.3%), and fatty red meat (65.7%; p-value<0.05).

The model fit results for the LCA are presented in Table 1. AIC, BIC, cAIC, and aBIC decreased until the two-class model and then increased up to the six-class model. The two-class model was selected as optimal based on the lowest BIC value (moreover, all other indices were consistent), along with the theoretical interpretability of the model. This two-class model reported an acceptable level of entropy (entropy = 0.837). Furthermore, the Lo-Mendell-Rubin ad-hoc adjusted likelihood ratio test indicated no statistically significant difference between the two- and three-class models (p-value = 0.213), meaning there was no additional advantage in using the three-class model.

Table 1
Model fit and diagnostic criteria for Latent Class Analysis of healthy and unhealthy dietary consumption among quilombola older adults aged ≥60 years (N=236), Bequimão (IQUIBEQ Project), MA, Brazil, 2019.

Class	log-likelihood	resid. df	AIC	BIC	aBIC	cAIC	X2 goodness of fit	likelihood-ratio	Entropia
1	-948	227	1913	1962	1916	1953	411	195	--
2	-929	217	1896	1944	1901	1981	366	158	83.7
3	-922	207	1902	2002	1910	2031	352	144	68.9
4	-915	197	1908	2043	1919	2082	401	130	56.6
5	-908	187	1911	2084	1929	2133	156	116	53.6
6	-900	177	1919	2123	1936	2182	142	101	52.2

• AIC: Akaike Information Criteria; BIC: Bayesian Information Criteria; aBIC: Adjusted Bayesian Information Criteria; cAIC: Consistent Akaike Information Criteria; df: degree of freedom; LL: log likelihood.

The item response probabilities (ρ) for each food item are presented in Figure 3. These estimated probabilities (ρ ≥ 0.3) were used to assign labels to the identified two-class model. Class 1 included individuals with a high probability of consuming seven out of the nine food types. Only the consumption of sweets and sugar-sweetened beverages did not reach a relevant probability level. This group was labeled as “Unhealthy and hypercaloric diet”: they do not consume vegetables, legumes, fruits, beans, or fish, but they consume fatty red meat, chicken with skin, and whole milk. Class 2 was labeled as “Unhealthy diet”, as it included individuals with ρ ≥ 0.3 for four out of the nine assessed food items: they do not consume vegetables and legumes, fruits, or beans, and they consume whole milk (Figure 3).

Figure 3
Conditional probabilities of healthy and unhealthy dietary consumption among quilombola older adults aged ≥60 years (N = 236). Bequimão (IQUIBEQ Project), MA, Brazil, 2019.

Table 2 presents the proportion of Classes 1 and 2 and the distribution of socioeconomic and demographic characteristics across these classes. The proportion of older adults in Class 1 was 13.6%, while Class 2 accounted for 86.4%. Statistically significant differences were observed between Classes 1 and 2 for the variables sex, median age, and ability to read and write. Older adults in Class 1, labeled as Unhealthy and hypercaloric diet, had a higher proportion of women, older median age, and greater ability to read and write.

Table 2
Socioeconomic and demographic characterization according to the latent classes formed based on healthy and unhealthy dietary consumption among quilombola older adults aged ≥60 years (N = 236). Bequimão (IQUIBEQ Project), MA, Brazil, 2019.

Variables	Latent Classes		p-value*
	Unhealthy and hypercaloric diet (13,6%)	Unhealthy diet (86,4%)
Sex
Male	23.1	76.9	0.001*
Female	6.1	93.9
Median age (in years)	66.5	70.0	0.001**
Literacy
Yes	19.8	80.2	0.010*
No	8.5	91.5
Housing adequacy***
Adequate	13.3	86.7	0.800*
Not adequate	14.5	85.5
Household income in minimum wage of 954.00 BRL
<1	16.1	83.9	0.390*
1 a 2	12.1	87.9

• Class 1: Unhealthy and Hypercaloric Diet; Class 2: Unhealthy Diet; *Pearson's chi-square test; **Mann-Whitney test; ***Indicates whether the material used in the construction of walls, roof, and floor is adequately or inadequately combined.

DISCUSSION

The results identified dietary patterns among quilombola older adults and their association with socioeconomic and demographic characteristics. The most consumed healthy food markers were fish (80.1%) and fruits (45.8%), while the most consumed unhealthy items were whole milk (68.2%) and chicken with skin (18.2%). These findings may reflect the cultural traits and food access conditions in quilombola communities, where food security is primarily ensured through artisanal fishing, hunting, small animal husbandry, and family horticulture practices¹⁹,²⁰.

Previous studies suggest that fish consumption may be associated with cardiovascular benefits. Regular fish intake has been linked to a reduced risk of heart disease and mortality²¹,²². However, a study by Silva et al.²³ on nutritional and cardiovascular risk among quilombola older adults revealed that 54.1% of participants were at cardiovascular risk, with higher rates among women.

Frequent consumption of vegetables and legumes can reduce the incidence of chronic diseases and improve quality of life. However, the low intake of beans (6.4%) and vegetables and legumes (12.7%) is concerning, as these foods are rich in essential nutrients—fiber, vitamins, and minerals—fundamental to population health. Beans, in particular, are rich in phytochemicals such as flavonoids and phenolic acids, which have cardioprotective, antidiabetic, and antioxidant effects. As an important symbol of Brazilian food culture, beans are being consumed less frequently, potentially due to urbanization and changing dietary patterns. This decline may deprive the body of these beneficial nutrients and compounds, increasing the risk of chronic diseases ²⁴,²⁵.

A likely explanation for the higher fruit consumption is the presence of home gardens and orchards located near households. In the backyards of these residences, it is common to find fruit trees such as mango, jackfruit, cashew, and banana, which also serve as important food sources³. However, it is worth noting that fruit and vegetable consumption among quilombola communities was significantly lower than that observed in the older adult population in Brazil in 2013 and 2019, regardless of place of residence or sex. Fruit consumption was approximately 10% lower, and the intake of vegetables and legumes was nearly four times lower⁸. A study conducted with quilombola communities in Bahia confirmed these findings, with 69.3% reporting satisfactory daily fruit and vegetable intake, and 39.8% reporting no diagnosis of chronic disease²⁶.

Among unhealthy foods, the high consumption of whole milk and chicken with skin contributes to excessive intake of saturated fats, which is associated with increased risk of cardiovascular diseases. Conversely, the low prevalence of sweet consumption (7.6%) and sweetened beverages (14.8%) is a positive finding, as these foods are linked to a higher risk of obesity and type 2 diabetes. The food combinations analyzed in this study showed that fruits and beans, as well as fish and fruits, were the most prevalent among healthy food markers. These combinations indicate dietary patterns that can provide a nutrient-rich diet, promoting health and preventing chronic diseases. This prevalence is closely associated with traditional practices and the sustainable use of natural resources, which play a key role in subsistence and the local economy. Practices such as artisanal fishing and agriculture remain fundamental to food security and the well-being of these communities²⁰.

Among unhealthy foods, combinations of fatty red meat and chicken with skin reflect dietary patterns that may increase the risk of metabolic and cardiovascular diseases. A study conducted in quilombola communities in northern Minas Gerais in 2019 demonstrated an association between these food types and abdominal obesity²⁷.

Regarding the emerging dietary patterns identified, the “Unhealthy diet” category predominated (86.4%), while the “Unhealthy and hypercaloric diet” accounted for a smaller proportion (13.6%). This latter category was associated with men, younger individuals, and those who are literate. Such a more deleterious dietary pattern accumulates risks that can affect various dimensions of health among quilombola older adults. This condition may be related to the accessibility of industrialized foods—nutritionally poor, highly caloric, and yet financially affordable—highlighting how inadequate eating habits have been shaped by the historical social invisibility and precarious socioeconomic conditions to which these communities have long been subjected²⁶.

Quilombola knowledge is culturally transmitted, rooted in popular/traditional practices, and interwoven with biomedical understandings, shaping their interpretations of how to remain or become healthy. The concept of health as the absence of disease still prevails, and the idea of being healthy is strongly linked to work capacity, which is considered a primary indicator of health¹⁴. These communities face ongoing difficulties in the implementation of public policies, and the discontinuity of state-led actions contributes to deepening inequities and vulnerabilities⁴.

Quilombola older adults have less access to socioeconomic resources and food distribution infrastructure, which hinders adherence to healthy eating behaviors. Their limited exposure to state-led initiatives and health promotion efforts may result in fewer socioeconomic, community, and cultural stimuli for a healthier life. The consequences of this scenario are reflected in inequalities in living and health conditions¹³, as well as in the high nutritional and cardiovascular risks previously reported in these quilombola older adult cohorts²³.

Despite these findings, the studys presents some limitations. As a cross-sectional study, reverse causality may have influenced the results. It remains unclear whether the dietary patterns are adopted simultaneously or whether the adoption of one leads to the other. The analysis was based on self-reported measures, which may be subject to recall bias, and survival bias may have also attenuated the observed findings.

The dietary intake questions were derived from those used in the Vigitel survey. While valid and relevant, they may not have captured the specificities of quilombola older adults. Additionally, they represent dietary markers rather than a comprehensive assessment of food consumption. The classification of latent classes was based on probability-based methods, which are a potential source of bias. Finally, the class labels were determined by the author, and the subjectivity inherent in such naming decisions cannot be overlooked. Nevertheless, the study employed a robust technique using categorical variables to examine clustering and dietary consumption patterns, generating more homogeneous groups with important implications for designing health promotion strategies and reducing food insecurity among quilombola older adults.

CONCLUSION

Quilombola older adults exhibited a dietary consumption pattern marked by an accumulation of unhealthy eating habits, associated with socioeconomic and demographic characteristics. The estimated rates of unhealthy food consumption were even higher than those observed in the general older population in Brazil. Targeted interventions are essential to promote access to healthy foods among older adults in rural and remote areas and to support strategies that enhance food sovereignty. Social opportunities for food acquisition and production can help meet the diverse needs and social demands of these individuals, contributing to healthier and more equitable dietary and nutritional intake throughout the aging process of quilombola communities.

ACKNOWLEDGMENTS

The authors thank the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brazil (CAPES) – Funding Code 001, and the Fundação de Amparo à Pesquisa e ao Desenvolvimento Científico e Tecnológico do Maranhão (FAPEMA). Oliveira, BLCA is a FAPEMA research productivity fellow.

• Funding
  The present study was conducted with support from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) - Funding Code 001, Universidade Federal do Maranhão, and the Fundação de Pesquisa do Estado Maranhão (FAPEMA), Brasil. BLCA Oliveira is a FAPEMA research productivity fellow.

References

• ¹ Correa RCF. IBGE Educa Jovens. IBGE - Educa | Jovens. Disponível em: https://educa.ibge.gov.br/jovens/conheca-o-brasil/populacao/22327-quilombolas.html Acesso em: 24 Abr 2024.
  » https://educa.ibge.gov.br/jovens/conheca-o-brasil/populacao/22327-quilombolas.html
• ² Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa nacional de saúde 2019: percepção do estado de saúde, estilos de vida, doenças crônicas e saúde bucal - Brasil e grandes regiões [Internet]. Rio de Janeiro: IBGE; 2020. Disponível em: https://www.pns.icict.fiocruz.br/wp-content/uploads/2021/02/liv101764.pdf Acesso em: 26 Abr 2024.
  » https://www.pns.icict.fiocruz.br/wp-content/uploads/2021/02/liv101764.pdf
• ³ Santana BF de, Voeks RA, Funch LS. Quilombola Ethnomedicine: The Role of Age, Gender, and Culture Change. Acta Bot Bras. 29 de abril de 2022;36:e2020abb0500. Disponível em: https://doi.org/10.1590/0102-33062020abb0500. Acesso em: 05 Mai 2024.
• ⁴ Furtado BNS, Olinda RA de, Costa GMC, Menezes TN de. Fatores relacionados à capacidade física de membros superiores e inferiores de idosos quilombolas. Ciênc saúde coletiva. 25 de outubro de 2021;26:4591–602. Disponível em: https://doi.org/10.1590/1413-812320212610.11252021. Acesso em: 12 Abr 2024.
• ⁵ Brandão A, Jorge AL. Comunidades quilombolas, acesso a programas sociais e segurança alimentar e nutricional. In: Rocha C, Burlandy L, Magalhães R, orgs. Segurança alimentar e nutricional: perspectivas, aprendizados e desafios para as políticas públicas. Rio de Janeiro: Editora Fiocruz; 2013. p. 213-225.
• ⁶ Rosa RS, Ribeiro ÍJ do S, Silva JK da, Souza LHR, Cruz DP, Damasceno RO, et al. Riesgo cardiovascular y factores asociados a la salud en personas afrodescendientes hipertensas residentes en la comunidad Quilombola. Revista Cuidarte. 24 de maio de 2021;12(2). . Disponível em: https://doi.org/10.15649/cuidarte.1165. Acesso em: 10 Abr 2024.
• ⁷ Nascimento VB do, Arantes ACV, Carvalho LG de. Vulnerabilidade e saúde de mulheres quilombolas em uma área de mineração na Amazônia. Saude soc. 10 de outubro de 2022;31:e210024pt. Disponível em: https://doi.org/10.1590/S0104-12902022210024pt. Acesso em: 15 Dez 2024
• ⁸ Oliveira BLCA de, Pinheiro AKB. Mudanças nos comportamentos de saúde em idosos brasileiros: dados das Pesquisa Nacional de Saúde 2013 e 2019. Ciênc saúde coletiva. 10 de novembro de 2023;28:3111–22. Disponível em: https://doi.org/10.1590/1413-812320232811.16702022. Acesso em: 2 Jul 2024.
• ⁹ Organização Mundial da Saúde (OMS). Envelhecimento ativo: uma política de saúde [Internet]. Brasília: OMS; 2005. Disponível em: https://www.gov.br/mdh/pt-br/centrais-de-conteudo/pessoa-idosa/envelhecimento-ativo-uma-politica-de-saude/view Acesso em: 3 Jul 2024.
  » https://www.gov.br/mdh/pt-br/centrais-de-conteudo/pessoa-idosa/envelhecimento-ativo-uma-politica-de-saude/view
• ¹⁰ Brasil. Ministério da Saúde. Secretaria de Políticas de Saúde. Departamento de Formulação de Políticas de Saúde. Política Nacional de Alimentação e Nutrição. Brasília: Ministério da Saúde; 2013. Disponível em: https://www.gov.br/saude/pt-br/composicao/saps/pnan Acesso em : 05 Jul 2024.
  » https://www.gov.br/saude/pt-br/composicao/saps/pnan
• ¹¹ Cardoso CS, Melo LO de, Freitas DA. Condições de saúde nas comunidades quilombolas. Revista de Enfermagem UFPE on line. 4 de abril de 2018;12(4):1037–45.Disponível em: https://doi.org/10.5205/1981-8963-v12i4a110258p1037-1045-2018 Acesso em: 01 Jul 2024.
• ¹² Silva JL, Marques APO, Leal MCC, Alencar DL, Melo EMA. Fatores associados à desnutrição em idosos institucionalizados. Rev Bras Geriatr Gerontol. 2015;18(2):443-51. Disponível em: https://doi.org/10.1590/1809-9823.2015.14026 . Acesso em: 06 Jul 2024
• ¹³ Costa DV de P, Lopes MS, Mendonça R de D, Malta DC, Freitas PP de, Lopes ACS. Diferenças no consumo alimentar nas áreas urbanas e rurais do Brasil: Pesquisa Nacional de Saúde. Ciênc saúde coletiva. 30 de agosto de 2021;26:3805–13. Disponível em: 10.1007/s10389-020-01198-y. Acesso em: 29 Mai 2025.
• ¹⁴ Santos FV dos, Rodrigues ILA, Nogueira LMV, Andrade EGR de, Soares AS, Andrade ÉFR de. Saberes e práticas sobre saúde entre homens quilombolas: contribuições para a atenção à saúde [Internet]. Rev Bras Enferm. 2023;76(2):1-10. Disponível em: https://doi.org/10.1590/0034-7167-2023-0138pt. Acesso em: 11 Abr 2024. Acesso em: 06 Jun 2024.
• ¹⁵ Melo DM de, Barbosa AJG. O uso do Mini-Exame do Estado Mental em pesquisas com idosos no Brasil: uma revisão sistemática. Ciênc saúde coletiva. dezembro de 2015;20:3865–76.. Disponível em: https://doi.org/10.1590/1413-812320152012.06032015 . Acesso em: 27 Fev 2025.
• ¹⁶ Atorkey P, Asante KO. Clustering of multiple health risk factors among a sample of adolescents in Liberia: a latent class analysis. J Public Health (Berl). 1o de junho de 2022;30(6):1389–97. Disponível em: https://doi.org/10.1007/s10389-020-01465-y. Acesso em: 24 Abr 2024.
• ¹⁷ Laxer RE, Brownson RC, Dubin JA, Cooke M, Chaurasia A, Leatherdale ST. Clustering of risk-related modifiable behaviours and their association with overweight and obesity among a large sample of youth in the COMPASS study. BMC Public Health. 21 de janeiro de 2017;17(1):102.. Disponível em: https://doi.org/10.1186/s12889-017-4034-0 . Acesso em: 11 Jul 2024.
• ¹⁸ Puri P, Singh SK, Pati S. Identifying noncommunicable disease multimorbidity patterns and associated factors: a latent class analysis approach [Internet]. BMJ Open. 2022;12:e053981 . Disponível em: https://doi.org/10.1136/bmjopen-2021-053981. Acesso em: 05 Jul 2024.
• ¹⁹ Lo Y, Mendell N, Rubin DB. Testing the number of components in a normal mixture. Biometrika. 2001;88(3):767-78. Disponível em: https://doi.org/10.1093/biomet/88.3.767. Acesso em: 11 Jul 2024.
• ²⁰ Corrêa NA, Silva HP. Da Amazônia ao guia: os dilemas entre a alimentação quilombola e as recomendações do guia alimentar para a população brasileira. Saude soc. 19 de março de 2021;30:e190276. . Disponível em: http://dx.doi.org/10.1590/s0104-12902021190276. Acesso em: 05 Jul 2024.
• ²¹ Giosuè A, Calabrese I, Lupoli R, Riccardi G, Vaccaro O, Vitale M. Relations between the Consumption of Fatty or Lean Fish and Risk of Cardiovascular Disease and All-Cause Mortality: A Systematic Review and Meta-Analysis. Advances in Nutrition. 1o de setembro de 2022;13(5):1554–65.. Disponível em: https://doi.org/10.1093/advances/nmac006. Acesso em: 10 Jul 2024.
• ²² Tokgozoglu L, Hekimsoy V, Costabile G, Calabrese I, Riccardi G. Diet, Lifestyle, Smoking. 2020 Apr 10. In: von Eckardstein A, Binder CJ, editors. Prevention and Treatment of Atherosclerosis: Improving State-of-the-Art Management and Search for Novel Targets [Internet]. Cham (CH): Springer; 2022. Disponível em: 10.1007/164_2020_353 . Acesso em: 11 Jul 2024.
• ²³ Silva TC da, Martins Neto C, Carvalho CA de, Viola PC de AF, Rodrigues L dos S, Oliveira BLCA de. Risco nutricional e cardiovascular em idosos quilombolas. Ciênc saúde coletiva. 17 de janeiro de 2022; 27:219–30. Disponível em: https://doi.org/10.1590/1413-81232022271.30132020. Acesso em: 29 Jun 2024.
• ²⁴ Ganesan K, Xu B. Polyphenol-Rich Dry Common Beans (Phaseolus vulgaris L.) and Their Health Benefits. International Journal of Molecular Sciences. novembro de 2017;18(11):2331. Disponível em: https://doi.org/10.3390/ijms18112331. Acesso em: 20 Jun 2024.
• ²⁵ Costa DV de P, Lopes MS, Mendonça R de D, Malta DC, Freitas PP de, Lopes ACS. Diferenças no consumo alimentar nas áreas urbanas e rurais do Brasil: Pesquisa Nacional de Saúde. Ciênc saúde coletiva. 30 de agosto de 2021;26:3805–13. Disponível em: https://doi.org/10.1590/1413-81232021269.2.26752019. Acesso em: 18 Abr 2024.
• ²⁶ Kochergin CN, Proietti FA, César CC. Comunidades quilombolas de Vitória da Conquista, Bahia, Brasil: autoavaliação de saúde e fatores associados. Cad Saúde Pública. julho de 2014;30:1487–501. Acesso em: 24 Jun 2024
• ²⁷ Queiroz P de SF, Miranda L de P, Oliveira PSD, Rodrigues Neto JF, Sampaio CA, Oliveira TL, et al. Obesidade abdominal e fatores associados em comunidades quilombolas do Norte de Minas Gerais, 2019. Epidemiol Serv Saúde. 23 de agosto de 2021;30:e2020833. Disponível em: https://doi.org/10.1590/S1679-49742021000300023. Acesso em: 20 Jun 2024.

Edited by

• Edited by Yan Nogueira Leite de Freitas
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Publication Dates

• Publication in this collection
  11 Aug 2025
• Date of issue
  2025

History

• Received
  09 Sept 2024
• Accepted
  16 Apr 2025