Condições de vida e estrutura ocupacional associadas a transtornos mentais comuns

OBJECTIVE: To determine the prevalence of common mental disorders (CMD) and evaluate their association with living conditions and occupational organization. METHODS: A cross-sectional survey of a random sample of private households was carried out in Olinda, Brazil, in 1993. The sample consisted of 621 adults aged 15 years or over and the participants were interviewed using the Self-Reporting Questionnaire (SRQ-20) and a second questionnaire on social and economic characteristics. Crude and adjusted odds ratios were estimated using logistic regression analysis. RESULTS: The overall prevalence of CMD was 35%. Only the variables education level (pOBJETIVO: Determinar a prevalência de transtornos mentais comuns e analisar sua associação a condições de vida e inserção na estrutura ocupacional. MÉTODOS: Estudo transversal conduzido em 1993, em Olinda, PE, envolvendo 621 adultos de 15 ou mais anos em uma amostra domiciliar aleatória, aos quais se aplicaram o Self Reporting Questionnaire (SRQ-20) e um questionário socioeconômico. Estimaram-se os odds-ratios (OR) simples e ajustados, utilizando-se regressão logística. RESULTADOS: A prevalência total dos transtornos mentais comuns (TMC) foi de 35%. As variáveis relativas às condições de vida foram ajustadas entre si e por sexo, idade e situação conjugal. Apenas escolaridade (

An investigation of the predictability of the Brazilian three-modal hand-based behavioural biometric: a feature selection and feature-fusion approach

Abstract: New security systems, methods or techniques need to have their performance evaluated in conditions that closely resemble a real-life situation. The effectiveness with which individual identity can be predicted in different scenarios can benefit from seeking a broad base of identity evidence. Many approaches to the implementation of biometric-based identification systems are possible, and different configurations are likely to generate significantly different operational characteristics. The choice of implementational structure is, therefore, very dependent on the performance criteria, which is most important in any particular task scenario. The issue of improving performance can be addressed in many ways, but system configurations based on integrating different information sources are widely adopted in order to achieve this. Thus, understanding how each data information can influence performance is very important. The use of similar modalities may imply that we can use the same features. However, there is no indication that very similar (such as keyboard and touch keystroke dynamics, for example) basic biometrics will perform well using the same set of features. In this paper, we will evaluate the merits of using a three-modal hand-based biometric database for user prediction focusing on feature selection as the main investigation point. To the best of our knowledge, this is the first thought-out analysis of a database with three modalities that were collected from the same users, containing keyboard keystroke, touch keystroke and handwritten signature. First, we will investigate how the keystroke modalities perform, and then, we will add the signature in order to understand if there is any improvement in the results. We have used a wide range of techniques for feature selection that includes filters and wrappers (genetic algorithms), and we have validated our findings using a clustering technique

Male responsibility and maternal morbidity: a cross-sectional study in two Nigerian states

<p>Abstract</p> <p>Background</p> <p>Nigeria continues to have high rates of maternal morbidity and mortality. This is partly associated with lack of adequate obstetric care, partly with high risks in pregnancy, including heavy work. We examined actionable risk factors and underlying determinants at community level in Bauchi and Cross River States of Nigeria, including several related to male responsibility in pregnancy.</p> <p>Method</p> <p>In 2009, field teams visited a stratified (urban/rural) last stage random sample of 180 enumeration areas drawn from the most recent censuses in each of Bauchi and Cross River states. A structured questionnaire administered in face-to-face interviews with women aged 15-49 years documented education, income, recent birth history, knowledge and attitudes related to safe birth, and deliveries in the last three years. Closed questions covered female genital mutilation, intimate partner violence (IPV) in the last year, IPV during the last pregnancy, work during the last pregnancy, and support during pregnancy. The outcome was complications in pregnancy and delivery (eclampsia, sepsis, bleeding) among survivors of childbirth in the last three years. We adjusted bivariate and multivariate analysis for clustering.</p> <p>Findings</p> <p>The most consistent and prominent of 28 candidate risk factors and underlying determinants for non-fatal maternal morbidity was intimate partner violence (IPV) during pregnancy (ORa 2.15, 95%CIca 1.43-3.24 in Bauchi and ORa 1.5, 95%CI 1.20-2.03 in Cross River). Other spouse-related factors in the multivariate model included not discussing pregnancy with the spouse and, independently, IPV in the last year. Shortage of food in the last week was a factor in both Bauchi (ORa 1.66, 95%CIca 1.22-2.26) and Cross River (ORa 1.32, 95%CIca 1.15-1.53). Female genital mutilation was a factor among less well to do Bauchi women (ORa 2.1, 95%CIca 1.39-3.17) and all Cross River women (ORa 1.23, 95%CIca 1.1-1.5).</p> <p>Interpretation</p> <p>Enhancing clinical protocols and skills can only benefit women in Nigeria and elsewhere. But the violence women experience throughout their lives – genital mutilation, domestic violence, and steep power gradients – is accentuated through pregnancy and childbirth, when women are most vulnerable. IPV especially in pregnancy, women's fear of husbands or partners and not discussing pregnancy are all within men's capacity to change.</p

Violence and post-traumatic stress disorder in Sao Paulo and Rio de Janeiro, Brazil: the protocol for an epidemiological and genetic survey

Background: violence is a public health major concern, and it is associated with post-traumatic stress disorder and other psychiatric outcomes. Brazil is one of the most violent countries in the world, and has an extreme social inequality. Research on the association between violence and mental health may support public health policy and thus reduce the burden of disease attributable to violence. the main objectives of this project were: to study the association between violence and mental disorders in the Brazilian population; to estimate the prevalence rates of exposure to violence, post-traumatic stress disorder, common metal disorder, and alcohol hazardous use and dependence: and to identify contextual and individual factors, including genetic factors, associated with the outcomes.Methods/design: one phase cross-sectional survey carried out in São Paulo and Rio de Janeiro, Brazil. A multistage probability to size sampling scheme was performed in order to select the participants (3000 and 1500 respectively). the cities were stratified according to homicide rates, and in São Paulo the three most violent strata were oversampled. the measurements included exposure to traumatic events, psychiatric diagnoses (CIDI 2.1), contextual (homicide rates and social indicators), and individual factors, such as demographics, social capital, resilience, help seeking behaviours. the interviews were carried between June/2007 February/2008, by a team of lay interviewers. the statistical analyses will be weight-adjusted in order to take account of the design effects. Standardization will be used in order to compare the results between the two centres. Whole genome association analysis will be performed on the 1 million SNP (single nucleotide polymorphism) arrays, and additional association analysis will be performed on additional phenotypes. the Ethical Committee of the Federal University of São Paulo approved the study, and participants who matched diagnostic criteria have been offered a referral to outpatient clinics at the Federal University of São Paulo and Federal University of Rio de Janeiro

An evolutionary algorithm for automated machine learning focusing on classifier ensembles: an improved algorithm and extended results

A large number of classification algorithms have been proposed in the machine learning literature. These algorithms have different pros and cons, and no algorithm is the best for all datasets. Hence, a challenging problem consists of choosing the best classification algorithm with its best hyper-parameter settings for a given input dataset. In the last few years, Automated Machine Learning (Auto-ML) has emerged as a promising approach for tackling this problem, by doing a heuristic search in a large space of candidate classification algorithms and their hyper-parameter settings. In this work we propose an improved version of our previous Evolutionary Algorithm (EA) – more precisely, an Estimation of Distribution Algorithm – for the Auto-ML task of automatically selecting the best classifier ensemble and its best hyper-parameter settings for an input dataset. The new version of this EA was compared against its previous version, as well as against a random forest algorithm (a strong ensemble algorithm) and a version of the well-known Auto-ML method Auto-WEKA adapted to search in the same space of classifier ensembles as the proposed EA. In general, in experiments with 21 datasets, the new EA version obtained the best results among all methods in terms of four popular predictive accuracy measures: error rate, precision, recall and F-measure