Sociobehavioral, Biological, and Health Characteristics of Riverside People in the Xingu Region, Pará, Brazil

Dalberto Lucianelli Junior, Adenilson Leão Pereira, Ozélia Sousa Santos, Maria do Carmo Faria Paes, Yuji Magalhães Ikuta, Rodrigo Silveira () and Fernanda Nogueira Valentin ()
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Dalberto Lucianelli Junior: Postgraduate Program in Health of Amazon, Nucleus of Tropical Medicine, Federal University of Pará, Belém 66075-110, Brazil
Adenilson Leão Pereira: Faculty of Medicine, Federal University of Pará, Altamira 68372-040, Brazil
Ozélia Sousa Santos: Faculty of Medicine, Federal University of Pará, Altamira 68372-040, Brazil
Maria do Carmo Faria Paes: Institute for Environmental Research, Rheinisch-Westfälische Technische Hochschule Aachen University, 52074 Aachen, Germany
Yuji Magalhães Ikuta: Postgraduate Program in Health of Amazon, Nucleus of Tropical Medicine, Federal University of Pará, Belém 66075-110, Brazil
Rodrigo Silveira: Capital Campus: Cidade Universitária Armando de Salles Oliveira (CUASO), University of São Paulo, São Paulo 05508-065, Brazil
Fernanda Nogueira Valentin: Faculty of Medicine, Federal University of Pará, Altamira 68372-040, Brazil

IJERPH, 2023, vol. 20, issue 8, 1-16

Abstract: This study aimed to evaluate the sociodemographic, behavioral, and biological profile and its relationship with the emergence of chronic non-communicable diseases in riverside populations in the Xingu region, Pará, Brazil. Characteristics related to health indicators and which risk factors are considered most important were analyzed. This is a cross-sectional, exploratory, and descriptive study. The sample consisted of riverside people of over 18 years of both sexes. The sample size (n = 86) was calculated with a confidence level of 95% and a sample error of 5%. The K-means clustering algorithm was adopted through an unsupervised method to divide the groups, and the values were expressed as a median. For continuous and categorical data, the Mann-Whitney and chi-square tests were used, respectively, and the significance level was set at p < 5%. The multi-layer perceptron algorithm was applied to classify the degree of importance of each variable. Based on this information, the sample was divided into two groups: the group with low or no education, with bad habits and worse health conditions, and the group with opposite characteristics. The risk factors considered for cardiovascular diseases and diabetes in the groups were low education ( p < 0.001), sedentary lifestyle ( p < 0.01), smoking, alcoholism, body mass index ( p < 0.05), and waist–hip ratio, with values above the expected being observed in both groups. The factors considered important so as to be considered to have good health condition or not were the educational and social conditions of these communities, and one part of the riverside population was considered healthier than the other.

Keywords: education level; eating behavior; healthy lifestyle habits; non-communicable chronic diseases; machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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