Introduction

Obesity is one of the greatest public health challenges, due to its association with important morbidity and mortality, and also because of its huge economic and social costs.^1.2 Abdominal obesity is associated with excess body fat and has a complex and multifactorial etiology, resulting from the interaction of historical, ecological, economic, social, cultural, emotional and political factors.^2.3

Obesity is present in both developed and developing countries.⁴ In Brazil, the largest population-based study on the subject, the National Health Survey (PNS), conducted in 2013, showed that the prevalence of abdominal obesity was 38%, 22.3% in males and 52% in females, an example of the severity of this epidemic in rural and urban areas.⁵

This profile indicates the importance of the monitoring of nutritional status of adults, through interventions to evaluate anthropometric data and define therapeutic actions aimed at abdominal obesity and associated comorbidities.⁶ It is important to highlight that the gradual weight gain is seen in men and women of different ages and, even in economically disadvantaged populations, such as the quilombola population, obesity can coexist with malnutrition.¹

The remaining quilombola communities were recognized by the Brazilian State, with the publication of Presidential Decree No. 4,887, on November 20, 2003, especially in art. 2:

Art. 2 Ethic-racial groups are considered remaining quilombola communities, according to criteria of self-attribution, with their own historical trajectory, endowed with specific territorial relations, with presumption of black ancestry related to resistance to historical oppression suffered.⁷

Precarious health of quilombola communities and limited access to collective goods, such as schools, roads, simplified water supply systems and health care, impose living conditions with low level of quality and human development.^8.9 However, there is still little specific information on health conditions of quilombola communities in Brazil.¹⁰ Therefore, it is imperative to invest in research in order to assess the real health status of these communities, mostly geographically isolated and, consequently, with restricted access to health services. Obtaining new information can contribute to the implementation of public policies capable of minimizing the vulnerability of quilombola communities and favoring the expansion of their concepts and practices of health and well-being.¹¹ The objective of this study was to analyze the prevalence of abdominal obesity and associated factors in quilombola communities in the north of Minas Gerais State, Brazil.

Methods

This was a cross-sectional study conducted in 2019, with quilombola communities located in the northern health macro-region of Minas Gerais State, Brazil.

The North health macro-region is comprised of 86 municipalities, gathered in nine health microregions, which composed the clusters of this study.

The quilombola communities were identified from data available in the local Municipal Health and Social Development Secretariats, the Alternative Agriculture Center, and on the Documentation Center Eloy Ferreira da Silva and the Palmares Cultural Foundation websites. There were seventy-nine communities and approximately 19,000 quilombola inhabitants in the North health macro-region.

Self-declared quilombolas, aged 18 years or older, residing in the selected communities were considered eligible for the study; those with mental and cognitive impairment were excluded, as reported by their families and/or the health team, thus making it impossible for them to understand and answer the questionnaire, in addition to the elderly who were screened through the mini-mental state examination.¹²

To define the participating communities in the study, cluster sampling with probability proportional to size was designed. Thirty communities were selected along with the households to be visited. Initially, a starting point was identified in the center of each community, for the first interview and the following ones in spiral motion, given the spatial configuration of quilombola communities. The interviewers visited the households and continued with the visits until reaching the sample size previously proposed for each community.

The following variables were analyzed: the dependent variable of the study, 'abdominal obesity' (no; yes), and the independent variables:

For data collection, a semi-structured questionnaire was used, based on the PNS 2013.¹³ Data were collected between January and August 2019 by trained interviewers.

A pre-test study was conducted in a quilombola community that had not been eligible, to verify the adequacy of the questionnaire and the time required to conduct the interview. We chose to apply the instrument with 5% of the main study sample. After the pre-test, textual adjustments and changes were made in the ordering of questions. Individuals who took part in the pre-test did not comprise the final sample of the study.

To measure the waist circumference, a 150 cm inextensible measuring tape with 0.1 cm precision was used, positioned at the midpoint between the 10^th rib and the upper edge of the iliac crest.¹⁴ To define the research outcome - abdominal obesity - the cut-off point for Latin American people was adopted - waist circumference ≥90 cm for men and ≥80 cm for women.¹⁵

For the calculation of the sample, a prevalence of 50% for chronic non-communicable diseases (NCDs) precision of 5 percentage points, 95% confidence interval, drawing effect equal to 2.0 and estimated 20% of losses were adopted, due to the heterogeneity of the events analyzed, totaling 905 individuals to be included.

Data analysis was stratified by gender. Categorical variables were described by their frequency distributions; and numerical variables, by measures of central tendency and dispersion (mean and standard deviation).

To identify the factors associated with abdominal obesity, the hierarchical multiple regression model was used. The distal level was composed by the block of sociodemographic characteristics; the intermediate level by the block of health-related behavior; and the proximal level by the block of health conditions. Poisson regression with robust variance was used to calculate prevalence ratios (PR) of abdominal obesity by independent variables and 95% confidence intervals (95%CI). Bivariate analyses were performed using Pearson's chi-square test, in each block; at this stage, the variables that presented p-value <0.25 were eligible for multiple analysis.¹⁶ The distal block was the first to make up the model, acting as an adjustment factor for the other levels. Subsequently, the intermediate level was included, only the variables with p-value <0.05 remained in the model, adjusted for the variables of the previous block. The process for the proximal block was repeated, adjusted for the prior variables. Multicollinearity diagnosis was performed from the calculation of the variance inflation factor (VIF): VIF values>5 indicate problems with coefficient estimation, due to the presence of multicollinearity between the independent variables.¹⁷

The model was evaluated using the deviance (statistics), according to which, p>0.05, shows that the model has quality of adjustment. This test evaluates whether the values predicted by the model are diverted from the observed values, which the Poisson distribution does not predict. If the p-value is lower than the adopted significance level, the null hypothesis that the Poisson distribution allows a good adjustment, is rejected. The analyses were performed using the statistical program SPSS^®, Windows^®,version 22.0, and corrected by the complex design effect.

The study project was approved by the Research Ethics Committee of the State University of Montes Claros (CEP/Unimontes): Opinion No. 2,821,454, issued on August 14, 2018. All participants signed the Free and Informed Consent Form.

Results

The final sample of the study was comprised of 1,025 individuals, exceeding the minimum amount needed to represent the area of interest; pregnant or puerperal women and individuals who had answered the questionnaire, but did not authorize the measurement of the waist circumference were excluded. There was 6.8% of loss of the initial sample; there were no refusals.

The prevalence of abdominal obesity was 56.6% (95% CI 50.9;62.0), being higher in women (71.9% - 95% CI 66.3;76.9) than in men (32.4% - 95% CI 25.1;40.6) (Table 1). There was a predominance of women aged 18 to 39 years (39%), and men aged 50 to 69 years (45.1%). In both sexes, there was greater participation of black people, married men and women, those with up to 8 years of schooling and individuals whose family income was 0.5 to 1 minimum wage. Among men, there was a higher frequency of smokers and former smokers (59.9%: 25.1% and 34.8%, respectively), alcohol consumption (53.5%), and poor eating habits. Self-reported medical diagnoses of diseases were more prevalent among women, except for cancer cases.

In the bivariate analysis, among women, the variables that showed a statistical association with abdominal obesity were: age group, marital status, schooling, smoking, consumption of chicken with skin, vegetables, fruits and sweet food, self-perceived health and self-reported hypertension, diabetes mellitus and high cholesterol (Table 2). Multicollinearity between these variables was not identified, since the VIF values ranged from 1.04 to 2.18. Among men, the variables associated with the outcome were: age group, marital status, family income, smoking and self-reported hypertension, diabetes mellitus and high cholesterol (Table 2). The multicollinearity diagnosis estimated VIF values between 1.07 and 2.41, suggesting that there was no collinearity between the independent variables.

After adjustments for potential confounding factors in multiple analysis, a statistically significant association was observed in women with abdominal obesity and in the age group 50-59 (PR=1.25 - 95% CI 1.01;1.54), former smokers (PR=1.26 - 95% CI 1.00; 1.58), who reported consuming chicken with skin (PR=1.09 - 95% CI 1.00;1.19), and self-reported arterial hypertension (PR=1.22 - 95% CI 1.11;1.36) (Table 3). Among men, abdominal obesity was associated with the age group 60-69 (PR=2.52 - 95% CI 1.33; 4.75), non-smokers (PR=1.73 - 95% CI 1.17;2.55), self-reported arterial hypertension (PR=1.42 - 95% CI 1.11;1.80) (Table 4).

Discussion

The results of the study showed a high prevalence of abdominal obesity in quilombola communities in the north of Minas Gerais State, especially among women.

This study presented limitations, such as self-reported chronic diseases and health-related behaviors, given that they are susceptible to the interference of lack of attention and memory. In addition, the different waist circumference cut-off points used to define abdominal obesity, reported in the various studies that were analyzed, may compromise a reliable comparison of data related to the prevalence of abdominal obesity.

Among its attributes, this research stands out for the sample being representative of quilombola communities in the north of Minas Gerais and for the few studies related to the evaluation of abdominal obesity in this population group. As far as the researchers know, this study was conducted with the largest number of quilombolas in the region.

Different national surveys, conducted with quilombola communities in 2012, 2015 and 2016,^8.18.19 and with other populations in 2010,^6.11.20 showed a higher prevalence of abdominal adiposity and/or general obesity in women. A study based on data from the PNS 2013 observed a higher prevalence of abdominal obesity in famales, especially in rural areas.

In the rural context, manual work can be considered a protective factor related to obesity, especially in men.⁵ A study conducted in quilombola communities located in the Middle São Francisco Region, Bahia State, in 2012, reinforces that a higher prevalence of abdominal adiposity in women is possibly related to lower physical effort during work, compared to men in the same region.¹⁹

Abdominal adiposity was significantly associated with advancing age. The changes inherent to the aging process - for example, hormonal changes, basal metabolic rate and level of physical activity - cause changes in body composition that may favor fat accumulation.²¹ However, as observed in this study, it is important to highlight a linear trend decline in abdominal obesity in the elderly, possibly explained by the decrease in body weight among this age group due to reduced number of teeth and chewing difficulty, attributed to lesions in the oral cavity, use of dental prostheses or gastrointestinal disorders.²¹

It could be seen that men present higher risk behaviors for the onset of abdominal obesity and NCDs, such as the consumption of meat with fat, irregular consumption of vegetables and fruits per week, and physical activity less than 150 minutes per week. Other authors have also observed such behaviors among males in quilombola communities in the northern Minas Gerais.²³

With regard to the intake of sweet foods (e.g., cakes, candies and biscuits) during the week, it could be seen that it was more frequent than the intake of fruits and vegetables in the same period, in both sexes. Poverty among quilombolas favors access to industrialized foods, poor in nutritional value and highly energetic, but that are low in price.²⁴ It is noteworthy that the poor eating habits identified, reflect the social invisibility that quilombola communities are subjected to, without guarantee of financial support for their crop maintenance, which would certainly contribute to the adoption of a healthier diet.²³

A high prevalence of quilombolas living a sedentary lifestyle and who are insufficiently physically active was found. Although other studies have showed an association between physical inactivity and increased abdominal obesity,^6.25 this study do not corroborate this finding. A study addressing black Africans did not find an association between physical inactivity and indicators of body fat, either in men or women.²⁰

Smokers presented a lower prevalence of abdominal obesity. In fact, studies have shown that smokers tend to have lower body weight when compared to non-smokers and former smokers.^6.11.20.25 Nicotine increases the levels of the neurotransmitters, dopamine and serotonin, reducing appetite and energy need, in addition to exerting a direct effect on adipose tissue metabolism. On the contrary, smoking cessation can cause weight gain of 5 to 6 kilograms, being more prevalent in famales.²⁶

There was a significant association between abdominal obesity and hypertension in men and women. A cross-sectional study conducted in 2010 with adults from São Francisco do Conde, state of Bahia, also observed a higher prevalence of abdominal adiposity in both sexes when they reported a diagnosis of hypertension.¹¹ Waist circumference proved to be an independent predictor of hypertension, according to a study conducted with 2,726 young adults from Sub-Saharan Africa between 2009 and 2012. The same African study observed that each 1cm increase in waist circumference was associated with 9% increase in the prevalence of hypertension.²⁷

The results of the study showed that abdominal obesity is an important health problem in quilombola communities in the north of Minas Gerais State that were analyzed here. These data, aggravated by the high prevalence of chronic diseases and historical vulnerability to which the quilombolas are subjected, points to the opportunity to conduct further studies and reflections on development and strengthening of public policies aimed at this population group.