BackgroundLack of regular physical activity, high sedentary behavior and presence of unbalanced alimentary practices are attitudes associated with an inadequate lifestyle among female adolescents.Objectiveto assess the lifestyle of female adolescents based on measurements of behavioral variables.MethodsCross-sectional study with 405 female adolescents between 14 and 19 years old, resident and attending public schools in Viçosa (state of Minas Gerais). Their lifestyle was analyzed by the Physical Activity Recall, number of steps, screen time (ST), cellphone time (CT), sitting time, food frequency questionnaire (FFQ), and alcohol and tobacco consumption. With multiple correspondence analysis it was possible to observe dispersion and approximation of the variables’ categories. Latent class analysis (LCA) was used for modeling the “lifestyle” variable, having been conducted in the poLCA (Polychromous Variable Latent Class Analysis) package of the R statistical software.ResultsThe mean age was 15.92 ± 1.27 years. Most of the adolescents were considered physically inactive (78%) and with low number of steps (82.57%); 41.45% reported not performing Moderate to Vigorous Physical Activities (MVPA) adequately. Sedentary behavior was found high when assessing ST (72.90%) and CT (65.31%). It was found the best fitted latent class model for the lifestyle (p-G2 = 0.055, p-χ2 = 0.066) featured three latent classes and one covariate (alcohol): Class 1, ‘Inactive and Sedentary’ (γ = 77.5%); Class 2, ‘Inactive and Non-sedentary lifestyle (γ=16.31%); and Class 3, ‘Active and sedentary’ (γ=6.19%). Female adolescents that had ‘never consumed alcohol’ were 2.26 times as likely (log OR = 0.8174; p = 0.033) to belong to class 3 (Active & Sedentary lifestyle) than to class 1 (Inactive & Sedentary lifestyle).ConclusionLatent class analysis model with five manifest variable (MVPA, number of steps, ST, sitting time and number of meals) and alcohol consumption like covariate showed itself to be an accurate and objective method in the assessment of female adolescents’ lifestyle. Female adolescents that had ‘never consumed alcohol’ were more as likely to belong to class ‘Active & Sedentary lifestyle’ than to class Inactive & Sedentary lifestyle. An inactive and sedentary lifestyle is coupled to other unhealthy behaviors during adolescence, possibly carrying over into adult life.

Mortality due to colorectal cancer is increasing in Brazil, but an organised approach to screening and prevention is lacking. Considering the importance of this disease, the present study examines recent mortality trends of colorectal cancer mortality in the meso- and microregions in the state of Mato Grosso with the objective of analysing spatiotemporal trends to help guide the allocation of health services related to this type of cancer. Mortality data from the Brazilian national public health system from 1996 to 2015 were analysed investigating spatiotemporal trends using Conditional Autoregressive (CAR) models, a class of Bayesian hierarchical models that rely on Markov Chain Monte Carlo (MCMC) simulations. Convergence issues arose with several types of CAR models, but notably not with the linear variant, which models the mortality within each spatial region as a linear function of time. Men and women of all ages displayed higher and increasing mortality rates in the Cuiabá and Rondonópolis microregions. Additional regions of increasing mortality were found for specific age and gender strata. It was concluded that spatiotemporal modelling is a useful tool for the characterisation of diseases, including cancer, which are influenced by several factors and need to be monitored over space and time. The combination of spatial and temporal analyses of mortality shown in this paper unveils important information regarding the small areas dynamics, which may guide discussions, regulation and application of decentralised public health policies.

ResumoNeste estudo utilizamos uma base de dados de pesquisa vinculada ao desempenho do Programa Bolsa Família (PBF) no ano de 2009. Este programa implica na transferência direta de renda com condicionantes nas áreas de educação, saúde e assistência social, visando atender famílias pobres e extremamente pobres -assim classificadas segundo um determinado valor percapita mensal. Esta base contém informações de cunho financeiro (renda e gastos das famílias), e também grau de instrução dos indivíduos, e elementos descritores do ambiente domiciliar (moradia e entorno). A aplicação dos algoritmos de predição visou averiguar a eficiência desses processos a partir das variáveis que descrevem as famílias, identificando corretamente se estas atendiam ou não ao perfil de beneficiárias do programa. Os algoritmos utilizados foram regressão logística, árvore binária de decisão e rede neural artificial em múltiplas camadas. Diversas medidas de desempenho foram calculadas, a partir da matriz de confusão resultante de cada algoritmo. Os valores encontrados para estas medidas foram baixos frente a uma das classes a serem identificadas. As intervenções aplicadas foram o reembaralhamento aleatório e também superamostragem da classe minoritária e sub-amostragem da classe majoritária. Embora tenha ocorrido alguma melhora, o desempenho no reconhecimento da classe minoritária permaneceu baixo o que aponta para a necessidade de novos experimentos.Palavras-chave: Árvores de decisão. Programa Bolsa Família. Classificador. Predição. Regressão logística. Redes neurais. AbstractIn this study we used a research database focused on the performance of the Bolsa Família Program (PBF) in 2009. In this program, direct income transfer is carried out under conditions of education, health and social assistance, aiming to serve poor and extremely poor families -Thus classified according to a certain monthly per capita value. This database contains financial information (income and family expenses), as well as the degree of education of the individuals, and elements describing the home environment (housing and environment). Applying prediction algorithms, the objective was to ascertain the efficiency of these processes from the variables that describe the families, correctly identifying if they met the beneficiaries profile of the program or not. The algorithms used were logistic regression, binary decision tree and artificial neural network in multiple layers. Several performance measures were calculated from the confusion matrix resulting from each algorithm. The values found for these measures were low compared to one of the classes to be identified. The interventions applied were random remarshaling and also super-sampling of the minority class and sub-sampling of the majority class. Although some improvement occurred, the performance in the minority class recognition remained low, which indicates the need for new experiments.