Risk factors for Hospital-Acquired Pneumonia in nonventilated adults

Although most recent publications focus on Ventilator-associated Pneumonia, Non-Ventilator-associated Hospital-acquired pneumonia (NVHAP) is still worrisome. We studied risk factors for NVHAP among patients admitted to a small teaching hospital. Sixty-six NVHAP case patients and 66 controls admitted to the hospital from November 2005 through November 2006 were enrolled in a case-control study. Variables under investigation included: demographic characteristics, comorbidities, procedures, invasive devices and use of medications (Sedatives, Antacids, Steroids and Antimicrobials). Univariate and multivariable analysis (hierarchical models of logistic regression) were performed. The incidence of NVHAP in our hospital was 0.68% (1.02 per 1,000 patients-day). Results from multivariable analysis identified risk factors for NVHAP: age (Odds Ratio[OR]=1.03, 95% Confidence Interval[CI]=1.01-1.05, p=0.002), use of Antacids (OR=5.29, 95%CI=1.89-4.79, p=0.001) and Central Nervous System disease (OR=3.13, 95%CI=1.24-7.93, p=0.02). Although our findings are coherent with previous reports, the association of Antacids with NVHAP recalls a controversial issue in the physiopathology of Hospital-Acquired Pneumonia, with possible implications for preventive strategies.

Spatial distribution of dengue incidence and socio-environmental conditions in Campinas, São Paulo State, Brazil, 2007

This study aimed to analyze the spatial distribution of dengue risk and its association with socio-environmental conditions. This was an ecological study of the counts of autochthonous dengue cases in the municipality of Campinas, São Paulo State, Brazil, in the year 2007, aggregated according to 47 coverage areas of municipal health centers. Spatial models for mapping diseases were constructed with Bayesian hierarchical models, based on Integrated Nested Laplace Approximation (INLA). The analyses were stratified according to two age groups, 0 to 14 years and above 14 years. The results indicate that the spatial distribution of dengue risk is not associated with socio-environmental conditions in the 0 to 14 year age group. In the age group older than 14 years, the relative risk of dengue increases significantly as the level of socio-environmental deprivation increases. Mapping of socio-environmental deprivation and dengue cases proved to be a useful tool for data analysis in dengue surveillance systems.

Modelos bayesianos para análise de características reprodutivas de fêmeas Nelore

Pós-graduação em Genética e Melhoramento Animal - FCAV

Modelo dinâmico bayesiano multivariado para análise espaço-temporal de dados de área

Pós-graduação em Matematica Aplicada e Computacional - FCT

Validation of the Geriatric Oral Health Assessment Index in complete denture wearers

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Modelos lineares generalizados bayesianos para dados longitudinais

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternity

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Class of self-dual models in three dimensions

In the present paper we introduce a hierarchical class of self-dual models in three dimensions, inspired in the original self-dual theory of Towsend-Pilch-Nieuwenhuizen. The basic strategy is to explore the powerful property of the duality transformations in order to generate a new field. The generalized propagator can be written in terms of the primitive one (first order), and also the respective order and disorder correlation functions. Some conclusions about the charge screening and magnetic flux were established. ©1999 The American Physical Society.

Environment mapping for mobile robots navigation using hierarchical neural network and omnivision

Autonomous robots must be able to learn and maintain models of their environments. In this context, the present work considers techniques for the classification and extraction of features from images in joined with artificial neural networks in order to use them in the system of mapping and localization of the mobile robot of Laboratory of Automation and Evolutive Computer (LACE). To do this, the robot uses a sensorial system composed for ultrasound sensors and a catadioptric vision system formed by a camera and a conical mirror. The mapping system is composed by three modules. Two of them will be presented in this paper: the classifier and the characterizer module. The first module uses a hierarchical neural network to do the classification; the second uses techiniques of extraction of attributes of images and recognition of invariant patterns extracted from the places images set. The neural network of the classifier module is structured in two layers, reason and intuition, and is trained to classify each place explored for the robot amongst four predefine classes. The final result of the exploration is the construction of a topological map of the explored environment. Results gotten through the simulation of the both modules of the mapping system will be presented in this paper. © 2008 IEEE.

Visualization of Protein Folding Funnels in Lattice Models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)