Healthcare-associated infection in hematopoietic stem cell transplantation patients: risk factors and impact on outcome

Objective: The objective of this study was to analyze the incidence of and risk factors for healthcare-associated infections (HAI) among hematopoietic stem cell transplantation (HSCT) patients, and the impact of such infections on mortality during hospitalization. Methods: We conducted a 9-year (2001-2009) retrospective cohort study including patients submitted to HSCT at a reference center in Sao Paulo, Brazil. The incidence of HAI was calculated using days of neutropenia as the denominator. Data were analyzed using EpiInfo 3.5.1. Results: Over the 9-year period there were 429 neutropenic HSCT patients, with a total of 6816 days of neutropenia. Bloodstream infections (BSI) were the most frequent infection, presenting in 80 (18.6%) patients, with an incidence of 11.7 per 1000 days of neutropenia. Most bacteremia was due to Gram-negative bacteria: 43 (53.8%) cases were caused by Gram-negative species, while 33 (41.2%) were caused by Gram-positive species, and four (5%) by fungal species. Independent risk factors associated with HAI were prolonged neutropenia (odds ratio (OR) 1.07, 95% confidence interval (CI) 1.04-1.10) and duration of fever (OR 1.20, 95% CI 1.12-1.30). Risk factors associated with death in multivariate analyses were age (OR 1.02, 95% CI 1.01-1.43), being submitted to an allogeneic transplant (OR 3.08, 95% CI 1.68-5.56), a microbiologically documented infection (OR 2.96, 95% CI 1.87-4.6), invasive aspergillosis disease (OR 2.21, 95% CI 1.1-4.3), and acute leukemias (OR 2.24, 95% CI 1.3-3.6). Conclusions: BSI was the most frequent HAI, and there was a predominance of Gram-negative microorganisms. Independent risk factors associated with HAI were duration of neutropenia and fever, and the risk factors for a poor outcome were older age, type of transplant (allogeneic), the presence of a microbiologically documented infection, invasive aspergillosis, and acute leukemia. Further prospective studies with larger numbers of patients may confirm the role of these risk factors for a poor clinical outcome and death in this transplant population. (C) 2012 Published by Elsevier Ltd on behalf of International Society for Infectious Diseases.

An online data access prediction and optimization approach for distributed systems

Current scientific applications have been producing large amounts of data. The processing, handling and analysis of such data require large-scale computing infrastructures such as clusters and grids. In this area, studies aim at improving the performance of data-intensive applications by optimizing data accesses. In order to achieve this goal, distributed storage systems have been considering techniques of data replication, migration, distribution, and access parallelism. However, the main drawback of those studies is that they do not take into account application behavior to perform data access optimization. This limitation motivated this paper which applies strategies to support the online prediction of application behavior in order to optimize data access operations on distributed systems, without requiring any information on past executions. In order to accomplish such a goal, this approach organizes application behaviors as time series and, then, analyzes and classifies those series according to their properties. By knowing properties, the approach selects modeling techniques to represent series and perform predictions, which are, later on, used to optimize data access operations. This new approach was implemented and evaluated using the OptorSim simulator, sponsored by the LHC-CERN project and widely employed by the scientific community. Experiments confirm this new approach reduces application execution time in about 50 percent, specially when handling large amounts of data.

Utility Harmonic Impedance Measurement Based on Data Selection

Determination of the utility harmonic impedance based on measurements is a significant task for utility power-quality improvement and management. Compared to those well-established, accurate invasive methods, the noninvasive methods are more desirable since they work with natural variations of the loads connected to the point of common coupling (PCC), so that no intentional disturbance is needed. However, the accuracy of these methods has to be improved. In this context, this paper first points out that the critical problem of the noninvasive methods is how to select the measurements that can be used with confidence for utility harmonic impedance calculation. Then, this paper presents a new measurement technique which is based on the complex data-based least-square regression, combined with two techniques of data selection. Simulation and field test results show that the proposed noninvasive method is practical and robust so that it can be used with confidence to determine the utility harmonic impedances.

On the impact of disproportional samples in credit scoring models: An application to a Brazilian bank data

Statistical methods have been widely employed to assess the capabilities of credit scoring classification models in order to reduce the risk of wrong decisions when granting credit facilities to clients. The predictive quality of a classification model can be evaluated based on measures such as sensitivity, specificity, predictive values, accuracy, correlation coefficients and information theoretical measures, such as relative entropy and mutual information. In this paper we analyze the performance of a naive logistic regression model (Hosmer & Lemeshow, 1989) and a logistic regression with state-dependent sample selection model (Cramer, 2004) applied to simulated data. Also, as a case study, the methodology is illustrated on a data set extracted from a Brazilian bank portfolio. Our simulation results so far revealed that there is no statistically significant difference in terms of predictive capacity between the naive logistic regression models and the logistic regression with state-dependent sample selection models. However, there is strong difference between the distributions of the estimated default probabilities from these two statistical modeling techniques, with the naive logistic regression models always underestimating such probabilities, particularly in the presence of balanced samples. (C) 2012 Elsevier Ltd. All rights reserved.

Accuracy of five electronic foramen locators with different operating systems: na ex vivo study

Objective: The aim of this study was to evaluate, ex vivo, the precision of five electronic root canal length measurement devices (ERCLMDs) with different operating systems: the Root ZX, Mini Apex Locator, Propex II, iPex, and RomiApex A-15, and the possible influence of the positioning of the instrument tips short of the apical foramen. Material and Methods: Forty-two mandibular bicuspids had their real canal lengths (RL) previously determined. Electronic measurements were performed 1.0 mm short of the apical foramen (-1.0), followed by measurements at the apical foramen (0.0). The data resulting from the comparison of the ERCLMD measurements and the RL were evaluated by the Wilcoxon and Friedman tests at a significance level of 5%. Results: Considering the measurements performed at 0.0 and -1.0, the precision rates for the ERCLMDs were: 73.5% and 47.1% (Root ZX), 73.5% and 55.9% (Mini Apex Locator), 67.6% and 41.1% (Propex II), 61.7% and 44.1% (iPex), and 79.4% and 44.1% (RomiApex A-15), respectively, considering ±0.5 mm of tolerance. Regarding the mean discrepancies, no differences were observed at 0.0; however, in the measurements at -1.0, the iPex, a multi-frequency ERCLMD, had significantly more discrepant readings short of the apical foramen than the other devices, except for the Propex II, which had intermediate results. When the ERCLMDs measurements at -1.0 were compared with those at 0.0, the Propex II, iPex and RomiApex A-15 presented significantly higher discrepancies in their readings. Conclusions: Under the conditions of the present study, all the ERCLMDs provided acceptable measurements at the 0.0 position. However, at the -1.0 position, the ERCLMDs had a lower precision, with statistically significant differences for the Propex II, iPex, and RomiApex A-15.

Socioeconomic and psychosocial predictors of dental healthcare use among Brazilian preschool children

Abstract Background Disparities in utilization of oral healthcare services have been attributed to socioeconomic and individual behavioral factors. Parents’ socioeconomic status, demographics, schooling, and perceptions of oral health may influence their children’s use of dental services. This cross-sectional study assessed the relationships between socioeconomic and psychosocial factors and the utilization of dental health services by children aged 1–5 years. Methods Data were collected through clinical exams and a structured questionnaire administered during the National Day of Children’s Vaccination. A Poisson regression model was used to estimate prevalence ratios and 95% confidence intervals. Results Data were collected from a total of 478 children. Only 112 (23.68%) were found to have visited a dentist; 67.77% of those had seen the dentist for preventive care. Most (63.11%) used public rather than private services. The use of dental services varied according to parental socioeconomic status; children from low socioeconomic backgrounds and those whose parents rated their oral health as “poor” used dental services less frequently. The reason for visiting the dentist also varied with socioeconomic status, in that children of parents with poor socioeconomic status and who reported their child’s oral health as “fair/poor” were less likely to have visited the dentist for preventive care. Conclusion This study demonstrated that psychosocial and socioeconomic factors are important predictors of the utilization of dental care services.

A Fuzzy Ontology-Driven Approach to Semantic Interoperability in e-Government Big Data

With the increasing production of information from e-government initiatives, there is also the need to transform a large volume of unstructured data into useful information for society. All this information should be easily accessible and made available in a meaningful and effective way in order to achieve semantic interoperability in electronic government services, which is a challenge to be pursued by governments round the world. Our aim is to discuss the context of e-Government Big Data and to present a framework to promote semantic interoperability through automatic generation of ontologies from unstructured information found in the Internet. We propose the use of fuzzy mechanisms to deal with natural language terms and present some related works found in this area. The results achieved in this study are based on the architectural definition and major components and requirements in order to compose the proposed framework. With this, it is possible to take advantage of the large volume of information generated from e-Government initiatives and use it to benefit society.