945 resultados para multivariate regression tree


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IntroductionThe prevalence of sexual dysfunction (SD) and dissatisfaction with sexual life (DSL) in patients with chronic hepatitis C virus infection (CHC) was jointly investigated via a thorough psychopathological analysis, which included dimensions such as fatigue, impulsiveness, psychiatric comorbidity, health-related quality of life (HRQL) and sociodemographic and clinical characteristics.MethodsMale and female CHC patients from an outpatient referral center were assessed using the Brief Fatigue Inventory, the Barrat Impulsiveness Scale, the Beck Depression Inventory (BDI), the Hospital Anxiety and Depression Scale, the Hamilton Anxiety Scale (HAM-A), and the World Health Organization Quality of Life Scale-Brief Version (WHOQOL-BREF). Structured psychiatric interviews were performed according to the Mini-International Neuropsychiatric Interview. SD was assessed based on specific items in the BDI (item 21) and the HAM-A (item 12). DSL was assessed based on a specific question in the WHOQOL-BREF (item 21). Multivariate analysis was performed according to an ordinal linear regression model in which SD and DSL were considered as outcome variables.ResultsSD was reported by 60 (57.1%) of the patients according to the results of the BDI and by 54 (51.4%) of the patients according to the results of the HAM-A. SD was associated with older age, female gender, viral genotype 2 or 3, interferon-α use, impulsiveness, depressive symptoms, antidepressant and benzodiazepine use, and lower HRQL. DSL was reported by 34 (32.4%) of the patients and was associated with depressive symptoms, anxiety symptoms, antidepressant use, and lower HRQL.ConclusionsThe prevalence of SD and DSL in CHC patients was high and was associated with factors, such as depressive symptoms and antidepressant use. Screening and managing these conditions represent significant steps toward improving medical assistance and the HRQL of CHC patients.

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IntroductionRecently, pathogen ecology has been recognized as an important epidemiological determinant of healthcare-associated infections (HAIs). Acinetobacter baumannii is one of the most important agents known to cause HAIs. It is widespread in healthcare settings and exhibits seasonal variations in incidence. Little is known about the impact of competition with other hospital pathogens on the incidence of A. baumannii infection.MethodsWe conducted an ecological study, enrolling patients who presented with healthcare-associated bloodstream infections (HA-BSIs) from 2005 to 2010 at a 450-bed teaching hospital in Brazil. HA-BSIs were said to be present when bacteria or fungi were recovered from blood cultures collected at least three days after admission. Monthly incidence rates were calculated for all HA-BSIs (overall or caused by specific pathogens or groups of pathogens). Multivariate Poisson regression models were used to identify the impacts of the incidence of several pathogens on the incidence of A. baumannii.ResultsThe overall incidence rate of HA-BSI caused by A. baumannii was 2.5 per 10,000 patient-days. In the multivariate analysis, the incidence of HA-BSI caused by A. baumanniiwas negatively associated with the incidence rates of HA-BSI due to Staphylococcus aureus (rate ratio [RR]=0.88; 95% confidence interval [CI]=0.80-0.97), Enterobacter spp. (RR=0.84; 95%CI=0.74-0.94) and a pool of less common gram-negative pathogens.ConclusionsOur results suggest that competition between pathogens influences the etiology of HA-BSIs. It would be beneficial to take these findings into account in infection control policies.

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Abstract: INTRODUCTION: Geographic information systems (GIS) enable public health data to be analyzed in terms of geographical variability and the relationship between risk factors and diseases. This study discusses the application of the geographic weighted regression (GWR) model to health data to improve the understanding of spatially varying social and clinical factors that potentially impact leprosy prevalence. METHODS: This ecological study used data from leprosy case records from 1998-2006, aggregated by neighborhood in the Duque de Caxias municipality in the State of Rio de Janeiro, Brazil. In the GWR model, the associations between the log of the leprosy detection rate and social and clinical factors were analyzed. RESULTS: Maps of the estimated coefficients by neighborhood confirmed the heterogeneous spatial relationships between the leprosy detection rates and the predictors. The proportion of households with piped water was associated with higher detection rates, mainly in the northeast of the municipality. Indeterminate forms were strongly associated with higher detections rates in the south, where access to health services was more established. CONCLUSIONS: GWR proved a useful tool for epidemiological analysis of leprosy in a local area, such as Duque de Caxias. Epidemiological analysis using the maps of the GWR model offered the advantage of visualizing the problem in sub-regions and identifying any spatial dependence in the local study area.

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Many conditions are associated with hyperglycemia in preterm neonates because they are very susceptible to changes in carbohydrate homeostasis. The purpose of this study was to evaluate the occurrence of hyperglycemia in preterm infants undergoing glucose infusion during the first week of life, and to enumerate the main variables predictive of hyperglycemia. This prospective study (during 1994) included 40 preterm neonates (gestational age <37 weeks); 511 determinations of glycemic status were made in these infants (average 12.8/infant), classified by gestational age, birth weight, glucose infusion rate and clinical status at the time of determination (based on clinical and laboratory parameters). The clinical status was classified as stable or unstable, as an indication of the stability or instability of the mechanisms governing glucose homeostasis at the time of determination of blood glucose; 59 episodes of hyperglycemia (11.5%) were identified. A case-control study was used (case = hyperglycemia; control = normoglycemia) to derive a model for predicting glycemia. The risk factors considered were gestational age (<=31 vs. >31 weeks), birth weight (<=1500 vs. >1500 g), glucose infusion rate (<=6 vs. >6 mg/kg/min) and clinical status (stable vs. unstable). Multivariate analysis by logistic regression gave the following mathematical model for predicting the probability of hyperglycemia: 1/exp{-3.1437 + 0.5819(GA) + 0.9234(GIR) + 1.0978(Clinical status)} The main predictive variables in our study, in increasing order of importance, were gestational age, glucose infusion rate and, the clinical status (stable or unstable) of the preterm newborn infant. The probability of hyperglycemia ranged from 4.1% to 36.9%.

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RESUMO - A prevalência de obesidade infantil em Portugal é das mais elevadas da Europa. No concelho da Murtosa (Aveiro), para estimar a prevalência de excesso de peso e obesidade entre os 3 e os 6 anos e determinar os factores que lhe estão associados, desenvolveu-se, durante o ano de 2008, um estudo transversal que consistiu na avaliação estatoponderal das crianças frequentadoras dos estabelecimentos de ensino pré-escolar do concelho e aplicação de um questionário aos pais sobre antecedentes e perinatais, hábitos alimentares, actividades da criança e características da família. Através de um modelo de regressão logística multivariada identificaram-se as variáveis associadas ao excesso de peso/obesidade. Participaram no estudo 258 crianças, estimando-se uma prevalência de excesso de peso de 15,5 % (IC 95 % : 11,6 % a 20,4 %) e de obesidade de 6,2 % (IC 95 % : 3,9 % a 9,8 % ). Observou-se uma maior prevalência de excesso de peso nos meninos (19,5 %) e de obesidade nas meninas (10,4 %). O excesso de peso materno e o hábito de comer a ver televisão aumentaram o risco de excesso de peso/obesidade (OR: 10,548; OR: 13,815); o maior número de horas de sono diário, o maior número de refeições diárias e o aumento ponderal materno durante a gravidez (OR: 0,490; OR: 0,366; OR: 0,804) associaram-se a um menor risco de excesso de peso/obesidade. Justifica-se o desenvolvimento de programas de prevenção primária e secundária da obesidade infantil dirigidos aos factores de risco modificáveis identificados, sugerindo-se a necessidade de uma abordagem familiar e da avaliação sistemática deste tipo de intervenções.

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RESUMO - Contexto: As desigualdades sociais em saúde são uma questão central de justiça social. No contexto de forte envelhecimento populacional em Portugal, as desigualdades nos idosos representam um desafio crucial para o futuro, sobre as quais existe pouca evidência. Este estudo pretende investigar a existência de desigualdades socioeconómicas em saúde nos idosos, em Portugal. Metodologia: Foram utilizados os dados para Portugal, da quarta vaga do Survey of Health, Ageing and Retirement in Europe. O estudo engloba 2017 indivíduos com 50 ou mais anos. Foram utilizados quatro indicadores de saúde: problemas de saúde, saúde auto-reportada, doenças de longa duração e atividade limitada. Foi utilizado o nível de educação como indicador socioeconómico. As desigualdades socioeconómicas foram avaliadas através de regressões logísticas multivariadas. Resultados: Existem desigualdades socioeconómicas em saúde nos idosos favoráveis aos mais educados. Os indivíduos com menor educação estão em maior risco de reportar má saúde (OR=5,5); maior risco em ter problemas de saúde, existindo um gradiente social na Hipertensão Arterial (OR=2,4) e na Artrite (OR=7,0); maior risco de doenças de longa duração (OR=1,6) e maior risco de limitação nas atividades diárias (OR=5,1). As desigualdades socioeconómicas diminuem com a idade. Conclusão: De forma a melhorar a saúde e reduzir as desigualdades socioeconómicas em saúde nos idosos, os resultados apontam para a necessidade de implementar medidas no âmbito dos problemas de saúde em que existe um gradiente social, melhorar o nível de educação da população geral e implementar medidas de educação para a saúde, aumentando a literacia em saúde nos idosos mais jovens.

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PURPOSE: To find out the prevalence of hypertension in employees of the Hospital and relate it to social demographic variables. METHODS: Blood pressure measurement was performed with a mercury sphygmomanometer, using an appropriate cuff size for arm circumference, weight, and height in a population sample of 864 individuals out of the 9,905 employees of a University General Hospital stratified by gender, age, and job position. RESULTS: Hypertension prevalence was 26% (62% of these reported being aware of their hypertension and 38% were unaware but had systolic/diastolic blood pressures of >140 and/or >90 mm Hg at the moment of the measurement). Of those who were aware of having hypertension, 51% were found to be hypertensive at the moment of the measurement. The prevalence was found to be 17%, 23%, and 29% (P <.05) in physicians, nursing staff, and "others", respectively. The univariate analysis showed a significant odds ratio for the male gender, age >50 years, work unit being the Institute of Radiology and the Administration Building, educational level 10 years, and body mass index >30 kg/m². The multivariate logistic regression model revealed a statistically significant association of hypertension with the following variables: gender, age, skin color, family income, and body mass index. CONCLUSIONS: Hypertension prevalence was high, mainly in those who were not physicians or members of the nursing staff. High-risk groups (obese, non-white, men, low family income) should be better advised of prevention and early diagnosis of hypertension by means of special programs.

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A large-scale inventory of trees > 10cm DBH was conducted in the upland "terra firme" rain forest of the Distrito Agropecuário da SUFRAMA (Manaus Free Zone Authority Agricultural District) approximately 65Km north of the city of Manaus (AM), Srasil. Thegeneral appearance and structure of the forest is described together with local topography and soil texture. Thepreliminary results of the Inventory provide a minimum estimate of 698 tree species in 53 families in the 40Km radius sampled, including 17 undescribed species. Themost numerically abundant families, Lecythidaceae, Leguminosae, 5apotaceae and Burseraceae as also among the most species rich families. One aspect of this diverse assemblage is the proliferation of species within certain genera, Including 26 genera In 17 families with 6 or more species or morphospecies. Most species have very low abundances of less than 1 tree per hectare. While more abundant species do exist at densities ranging up to a mean of 12 trees per ha, many have clumped distributions leading to great variation in local species abundance. The degree of similarity between hectare samples based int the Coefficient of Community similarity Index varies widely over different sample hectares for five ecologically different families. Soil texture apparently plays a significant role In determining species composition in the different one hectare plots examined while results for other variable were less consistent. Greater differences in similarity indices are found for comparisons with a one hectare sample within the same formation approximately 40Km to the south. It is concluded that homogeneity of tree community composition within this single large and diverse yet continuous upland forest formation can not be assumed.

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Two canopies of a widely distributed Amazonian tree species, Goupia glabra Aubl. (Celastraceae, height 38 and 45m) were fogged several times with 1% natural pyrethrum during the rainy and dry seasons (1991-1994) in the Adolpho Ducke Forest Reserve near Manaus/Brazil. Between 50 and 158 ind./m2 of arthropods were obtained per tree and fogging event. Hymenoptera, mostly Formicidae, and Diptcra dominated. A total of 95 ant species occurred on a single tree. Most ants were permanently foraging in the canopy and their recolonization after fogging seems to follow stochastic pathways. Data indicated an interaction between - 1) predating Formicidae and gall building Cecidomyiidae and - 2) Cecidomyiidae and the parasitic Hymenoptera.

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The aim of this study was to test the hypothesis that the monodominant non-pioneer Peltogyne gracilipes, typically does not suffer density-dependent herbivory (Janzen-Connell model). Two components of intraspecific variation in leaf herbivory were measured: 1) the variation between individuals in the population at the same time and 2) the temporal variation in rates of damage to each individual. The study was carried out on Maracá Island, Roraima, Brazil in three plots (50 m χ 50 m) in each of three forest types: Peltogyne-rich forest (PRF), Peltogyne-poor forest (PPF), and forest without Peltogyne (FWP). Two other non-pioneer species (Ecclinusa guianensis and Pradosia surinamensis) were chosen for comparison because they were fairly abundant and their seedlings could be readily identified. The values of leaf area removed by herbivores of trees and seedlings of the three study species were in the range reported for other tropical tree species (2-16%, standing damage). There were no differences within species between forests. However, there was a significant difference among species but this was not correlated with seedling density. Peltogyne seedlings showed no evidence of density-dependent herbivory as predicted by the Janzen-Connell model despite the fact that adult trees were observed to suffer a mass defoliation in April 1992. This result suggests that Peltogyne may be dominant partly due to escape from herbivory in the early stages of its life although it may suffer occasional mass defoliation as an adult.

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Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.

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This study investigates patterns of forest structure and tree species diversity in an anthropogenic palm grove and undisturbed areas at the seasonally-dry Pinkaití research station, in the Kayapó Indigenous Area. This site, managed by the Conservation International do Brasil, is the most southeastern site floristically surveyed in the Amazon until now. The secondary and a nearby undisturbed forest were sampled in a group of 52 floristic plots of 0.0625-ha (25x25-m) where all trees with DBH > 10 cm were measured and identified. The analyses were complemented with other two floristic plots of 1-ha (10x1000-m). The present study has shown that the Pinkaití, like other seasonally-dry forests, have great heterogeneity in forest structure and composition, associated with biotic characteristics of the most important tree species, natural disturbance and history of land-use. The palm grove, moderately dominated by the arborescent palm Attalea maripa (Aubl.) Mart., presented high tree species diversity and was floristically similar to undisturbed forests at the study site. It is discussed the importance of large arborescent palms for the seasonally-dry Amazon forests regeneration.

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Few studies have been conducted to verify how the structure of the forest affects the occurence and abundance of neotropical birds. Our research was undertaken between January 2002 and July 2004 at the Reserva Ducke, near Manaus (02º55',03º01'S; 59º53',59º59'W) in central Amazonia, to verify how the forest structure affects the occurrence and abundance of two bird species: the Plain-brown Woodcreeper Dendrocincla fuliginosa and the White-chinned Woodcreeper Dendrocincla merula. Bird species occurrence was recorded using lines of 20 mist-nets (one sample unit), along 51 1-km transects distributed along 9 pararel 8 km trails covering an area of 6400 ha. Along these transects, we placed 50 x 50m plots where we recorded forest structure components (tree abundance, canopy openness, leaf litter, standing dead trees, logs, proximity to streams, and altitude). We then related these variables to bird occurence and abundance using multiple logistic and multiple linear regression models, respectively. We found that D. fuliginosa frequently used plateau areas; being more abundant in areas with more trees. On the other hand, D. merula occurred more frequently and was more abundant in areas with low tree abundance. Our results suggest that although both species overlap in the reserve (both were recorded in at least 68% of the sampled sites), they differ in the way they use the forest microhabitats. Therefore, local variation in the forest structure may contribute to the coexistence of congeneric species and may help to maintain local alpha diversity.

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Extreme value models are widely used in different areas. The Birnbaum–Saunders distribution is receiving considerable attention due to its physical arguments and its good properties. We propose a methodology based on extreme value Birnbaum–Saunders regression models, which includes model formulation, estimation, inference and checking. We further conduct a simulation study for evaluating its performance. A statistical analysis with real-world extreme value environmental data using the methodology is provided as illustration.

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In longitudinal studies of disease, patients may experience several events through a follow-up period. In these studies, the sequentially ordered events are often of interest and lead to problems that have received much attention recently. Issues of interest include the estimation of bivariate survival, marginal distributions and the conditional distribution of gap times. In this work we consider the estimation of the survival function conditional to a previous event. Different nonparametric approaches will be considered for estimating these quantities, all based on the Kaplan-Meier estimator of the survival function. We explore the finite sample behavior of the estimators through simulations. The different methods proposed in this article are applied to a data set from a German Breast Cancer Study. The methods are used to obtain predictors for the conditional survival probabilities as well as to study the influence of recurrence in overall survival.