Prevalência de sintomas do climatério em mulheres dos meios rural e urbano no Rio Grande do Norte, Brasil

OBJETIVO: avaliar a sintomatologia climatérica e fatores relacionados entre mulheres dos meios urbano e rural do Rio Grande do Norte. MÉTODOS: estudo transversal, descritivo, envolvendo casuística de 261 mulheres climatéricas residentes em Natal e Mossoró (grupo urbano; n=130) e Uruaçu, em São Gonçalo do Amarante (grupo rural; n=131). A sintomatologia climatérica foi avaliada pelo Índice Menopausal de Blatt-Kupperman (IMBK) e Escala Climatérica de Greene (ECG). A análise estatística constou de comparações das medianas dos escores entre os grupos e regressão logística. Defi niram-se como “muito sintomáticas” as pacientes com escores ≥20, para ambos instrumentos (variável dependente). As variáveis independentes foram: idade, procedência, alfabetização, obesidade e prática de atividade física. RESULTADOS: o grupo urbano apresentou escores signifi cativamente superiores ao grupo rural, tanto para o IMBK (medianas de 26,0 e 17,0, respectivamente; p<0,0001), quanto para a ECG (medianas de 27,0 e 16,0, respectivamente; p<0,0001). Na amostra total, evidenciou-se que 56,3% (n=147) das mulheres foram classifi cadas como “muito sintomáticas”. Na comparação intergrupos, essa prevalência foi signifi cativamente mais elevada nas mulheres urbanas em relação às rurais (79,2 e 33,6%, respectivamente; p<0,05). Pela análise de regressão logística, evidenciou-se que a chance de pertencer ao grupo defi nido como “muito sintomáticas” foi maior para mulheres do meio urbano [odds ratio ajustado (OR)=7,1; 95% intervalo de confi ança a 95% (IC95%)=3,69-13,66] e alfabetizadas (OR=2,19; IC95%=1,16-4,13). A idade superior a 60 anos associou-se com menor chance de ocorrência de sintomas signifi cativos (OR=0,38; IC95%=0,17-0,87). CONCLUSÕES: a prevalência de sintomas climatéricos signifi cativos é menor em mulheres do meio rural, demonstrando que fatores socioculturais e ambientais estão fortemente relacionados ao surgimento dos sintomas climatéricos em nossa população.___________________________________ABSTRACT PURPOSE: to evaluate climacteric symptoms and related factors in women living in rural and urban areas of Rio Grande do Norte, Brazil. METHODS: a cross-sectional study involving 261 women in the climacteric was performed. A total of 130 women from Natal and Mossoró (urban group) and 131 from Uruaçu, in São Gonçalo do Amarante (rural group), were studied. Climacteric symptoms were assessed by the Blatt-Kupperman Menopausal Index (BKMI) and Greene Climacteric Scale (GCE). Statistical analysis involved comparison of median between groups and logistic regression analysis. Patients were defi ned as “very symptomatic” when the climacteric score was ≥20 for both questionnaires (dependent variable). Independent variables were: age, living area, schooling, obesity and physical activity. RESULTS: the urban group had signifi cantly higher scores than those of the rural group, both for BKMI (median of 26.0 and 17.0, respectively; p<0.0001) and for GCE (median of 27.0 and 16.0, respectively; p<0.0001). For the entire sample, a total of 56.3% (n=147) of the women were classifi ed as “very symptomatic”. This prevalence was signifi cantly higher in urban than in rural women (79.2 and 33.6%, respectively; p<0.05). Logistic regression analysis showed that the likelihood of belonging to the group defi ned as “very symptomatic” was greater for urban women [adjusted odds ratio (OR)=7.1; confi dence interval at 95% (95%CI)=3.69-13.66] who were literate (OR=2.19; 95%CI=1.16- 4.13). Individuals over the age of 60 years had less chance of having signifi cant symptoms (OR=0.38; 95%CI=0.17-0.87). CONCLUSIONS: the prevalence of signifi cant climacteric symptoms is less in women from a rural environment, showing that sociocultural and environmental factors are strongly related to the appearance of climacteric symptoms in our population

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model