Detection of nosocomial malnutrition is improved in Amazon region by a standard clinical nutrition education program

Background: In Brazil hospital malnutrition is highly prevalent. physician awareness of malnutrition is low, and nutrition therapy is underprescribed. One alternative to approach this problem is to educate health care providers in clinical nutrition. The present study aims to evaluate the effect of an intensive education course given to health care professionals and students on the diagnosis ability concerning to hospital malnutrition. Materials and methods: An intervention study based on a clinical nutrition educational program, offered to medical and nursing students and professionals, was held in a hospital of the Amazon region. Participants were evaluated through improvement of diagnostic ability, according to agreement of malnutrition diagnosis using Subjective Global Assessment before and after the workshop, as compared to independent evaluations (Kappa Index, k). To evaluate the impact of the educational intervention on the hospital malnutrition diagnosis, medical records were reviewed for documentation of parameters associated with nutritional status of in-patients. The SPSS statistical software package was used for data analysis. Results: A total of 165 participants concluded the program. The majority (76.4%) were medical and nursing students. Malnutrition diagnosis improved after the course (before k = 0.5; after k = 0.64; p < 0.05). A reduction of false negatives from 50% to 33.3% was observed. During the course, concern of nutritional diagnosis was increased W = 17.57; p < 0.001) and even after the course, improvement on the height measurement was detected chi(2) 12.87;p < 0.001). Conclusions: Clinical nutrition education improved the ability of diagnosing malnutrition; however the primary impact was on medical and nursing students. To sustain diagnostic capacity a clinical nutrition program should be part of health professional curricula and be coupled with continuing education for health care providers.

O impacto da exposição a experiências adversas na infância na ocorrência de dor crônica e depressão na vida adulta

A associação entre experiências adversas na infância e o desencadeamento de depressão ou dor crônica na vida adulta tem sido documentada, assim como a relação entre os sintomas de dor crônica e depressão. No entanto, há poucos estudos avaliando o papel da exposição a experiências adversas na infância na ocorrência dessa comorbidade. O objetivo deste trabalho é avaliar a influência da exposição a experiências adversas na infância na ocorrência de dor crônica, de depressão e na comorbidade dor crônica e depressão na vida adulta, em uma amostra da população geral adulta (maiores de 18 anos) residente na Região metropolitana de São Paulo, Brasil. Os dados são resultantes do Estudo Epidemiológicos dos Transtornos Mentais São Paulo Megacity. Os respondentes foram avaliados usando a versão desenvolvida para o Estudo Mundial de Saúde Mental do Composite International Diagnostic Interview da Organização Mundial da Saúde (WMH-CIDI), que é composto por módulos clínicos e nãoclínicos provendo diagnósticos de acordo com os critérios do Manual Diagnóstico e Estatístico dos Transtornos Mentais 4ª edição (DSM-IV). Um total de 5.037 indivíduos foi entrevistado, com uma taxa global de resposta de 81,3%. Foram realizadas análises descritivas para médias e proporções, e associações (Razões de Chances – OR) entre experiências adversas na infância, dor crônica e depressão através de regressão logística. Todas as análises foram realizadas através do programa estatístico Data Analysis and Statistical Software versão 12.0 (STATA 12.0), com testes bi-caudais com nível de significância de 5%. Uma elevada taxa de prevalência de dor crônica (31%, Erro Padrão [ER]=0.8) foi encontrada, Dor Crônica esteve associada aos transtornos de ansiedade (OR=2,3; 95% IC=1,9 – 3,0), transtornos de humor (OR=3,3; IC=2,6 – 4,4) em qualquer transtorno mental (OR=2,7; 95% IC=2,3 – 3,3). As adversidades na infância estiveram fortemente associadas aos respondentes com dor crônica e depressão concomitante, principalmente quanto ao abuso físico (OR=2,7; 95% IC=2,1 – 3,5) e sexual (OR=7,4; 95% IC=3,4 – 16,1).

An SPSS R-Menu for Ordinal Factor Analysis

Exploratory factor analysis is a widely used statistical technique in the social sciences. It attempts to identify underlying factors that explain the pattern of correlations within a set of observed variables. A statistical software package is needed to perform the calcula- tions. However, there are some limitations with popular statistical software packages, like SPSS. The R programming language is a free software package for statistical and graphical computing. It o ers many packages written by contributors from all over the world and programming resources that allow it to overcome the dialog limitations of SPSS. This paper o ers an SPSS dialog written in the R programming language with the help of some packages, so that researchers with little or no knowledge in programming, or those who are accustomed to making their calculations based on statistical dialogs, have more options when applying factor analysis to their data and hence can adopt a better approach when dealing with ordinal, Likert-type data.

Caracterização de regiões encefálicas no cérebro normal por tensor de difusão e aplicação à epilepsia pós-traumática

Mestrado em Radiações Aplicadas às Tecnologias da Saúde.

Efficiency Measures in the Agricultural Sector: With Applications

Copyright 2013 Springer Netherlands.

Arrow plot and CA maps on microarray preprocessing methods

Microarray allow to monitoring simultaneously thousands of genes, where the abundance of the transcripts under a same experimental condition at the same time can be quantified. Among various available array technologies, double channel cDNA microarray experiments have arisen in numerous technical protocols associated to genomic studies, which is the focus of this work. Microarray experiments involve many steps and each one can affect the quality of raw data. Background correction and normalization are preprocessing techniques to clean and correct the raw data when undesirable fluctuations arise from technical factors. Several recent studies showed that there is no preprocessing strategy that outperforms others in all circumstances and thus it seems difficult to provide general recommendations. In this work, it is proposed to use exploratory techniques to visualize the effects of preprocessing methods on statistical analysis of cancer two-channel microarray data sets, where the cancer types (classes) are known. For selecting differential expressed genes the arrow plot was used and the graph of profiles resultant from the correspondence analysis for visualizing the results. It was used 6 background methods and 6 normalization methods, performing 36 pre-processing methods and it was analyzed in a published cDNA microarray database (Liver) available at http://genome-www5.stanford.edu/ which microarrays were already classified by cancer type. All statistical analyses were performed using the R statistical software.

Phytoplankton analysis of portuguese reservoirs: a cluster analysis with R

In this work, cluster analysis is applied to a real dataset of biological features of several Portuguese reservoirs. All the statistical analysis is done using R statistical software. Several metrics and methods were explored, as well as the combination of Euclidean metric and the hierarchical Ward method. Although it did not present the best combination in terms of internal and stability validation, it was still a good solution and presented good results in terms of interpretation of the problem at hand.

Enteric parasites in HIV-1/AIDS-infected patients from a Northwestern São Paulo reference unit in the highly active antiretroviral therapy era

INTRODUCTION: We describe the epidemiology of intestinal parasites in patients from an AIDS reference service in Northeastern São Paulo, Brazil. METHODS: Retrospective evaluation was done for all HIV-1/AIDS-positive patients whose Hospital de Base/São José do Rio Preto laboratorial analysis was positive for enteroparasites after diagnosis of HIV-1 infection, from January 1998 to December 2008. Statistical analysis was performed using the R statistical software version 2.4.1. The level of significance adopted was 5%. RESULTS: The most frequent protozoan was Isospora belli (4.2%), followed by Giardia lamblia (3.5%), Entamoeba coli (2.8%), and Cryptosporidium parvum (0.3%). Ancylostoma duodenale (1.4%) was the most frequently detected helminth, while Taenia saginata and Strongiloides stercoralis were found in 0.7% of the samples. The results showed that diarrhea was significantly associated with giardiasis and isosporiasis. However, no association was observed between CD4+ cell counts, viral load, and the characteristics of any particular parasite. CONCLUSIONS: Our data may be useful for further comparisons with other Brazilian regions and other developing countries. The data may also provide important clues toward improving the understanding, prevention, and control of enteric parasites around the world.

Organização e gestão da manutencão de ferramentas numa empresa de produção de peças metálicas

Dissertação de mestrado Engenharia e Gestão da Qualidade

What Belongs Where? Variable Selection for Zero-Inflated Count Models with an Application to the Demand for Health Care

This paper develops stochastic search variable selection (SSVS) for zero-inflated count models which are commonly used in health economics. This allows for either model averaging or model selection in situations with many potential regressors. The proposed techniques are applied to a data set from Germany considering the demand for health care. A package for the free statistical software environment R is provided.

Time-series regression models to study the short-term effects of environmental factors on health

Time series regression models are especially suitable in epidemiology for evaluating short-term effects of time-varying exposures on health. The problem is that potential for confounding in time series regression is very high. Thus, it is important that trend and seasonality are properly accounted for. Our paper reviews the statistical models commonly used in time-series regression methods, specially allowing for serial correlation, make them potentially useful for selected epidemiological purposes. In particular, we discuss the use of time-series regression for counts using a wide range Generalised Linear Models as well as Generalised Additive Models. In addition, recently critical points in using statistical software for GAM were stressed, and reanalyses of time series data on air pollution and health were performed in order to update already published. Applications are offered through an example on the relationship between asthma emergency admissions and photochemical air pollutants

Study and implementation of some quantitative trading models

Quantitative or algorithmic trading is the automatization of investments decisions obeying a fixed or dynamic sets of rules to determine trading orders. It has increasingly made its way up to 70% of the trading volume of one of the biggest financial markets such as the New York Stock Exchange (NYSE). However, there is not a signi cant amount of academic literature devoted to it due to the private nature of investment banks and hedge funds. This projects aims to review the literature and discuss the models available in a subject that publications are scarce and infrequently. We review the basic and fundamental mathematical concepts needed for modeling financial markets such as: stochastic processes, stochastic integration and basic models for prices and spreads dynamics necessary for building quantitative strategies. We also contrast these models with real market data with minutely sampling frequency from the Dow Jones Industrial Average (DJIA). Quantitative strategies try to exploit two types of behavior: trend following or mean reversion. The former is grouped in the so-called technical models and the later in the so-called pairs trading. Technical models have been discarded by financial theoreticians but we show that they can be properly cast into a well defined scientific predictor if the signal generated by them pass the test of being a Markov time. That is, we can tell if the signal has occurred or not by examining the information up to the current time; or more technically, if the event is F_t-measurable. On the other hand the concept of pairs trading or market neutral strategy is fairly simple. However it can be cast in a variety of mathematical models ranging from a method based on a simple euclidean distance, in a co-integration framework or involving stochastic differential equations such as the well-known Ornstein-Uhlenbeck mean reversal ODE and its variations. A model for forecasting any economic or financial magnitude could be properly defined with scientific rigor but it could also lack of any economical value and be considered useless from a practical point of view. This is why this project could not be complete without a backtesting of the mentioned strategies. Conducting a useful and realistic backtesting is by no means a trivial exercise since the \laws" that govern financial markets are constantly evolving in time. This is the reason because we make emphasis in the calibration process of the strategies' parameters to adapt the given market conditions. We find out that the parameters from technical models are more volatile than their counterpart form market neutral strategies and calibration must be done in a high-frequency sampling manner to constantly track the currently market situation. As a whole, the goal of this project is to provide an overview of a quantitative approach to investment reviewing basic strategies and illustrating them by means of a back-testing with real financial market data. The sources of the data used in this project are Bloomberg for intraday time series and Yahoo! for daily prices. All numeric computations and graphics used and shown in this project were implemented in MATLAB^R scratch from scratch as a part of this thesis. No other mathematical or statistical software was used.

The pattern of health care use of older prisoners in Switzerland.

Introduction: Population ageing is a worldwide phenomenon that forces us to make radical changes on multiple levels of society. So far, studies have concluded that the health, both physical and mental, of prisoners in general and older prisoners in particular is worse than that of the general population. Prisoners are reported to age faster as compared to adults in the community. However, to date, very little is known about the actual healthcare conditions of older prisoners and almost no substantial knowledge is available concerning their patterns of healthcare use. Method: A quantitative study was conducted in four prisons for male prisoners in Switzerland, including two open and two closed prisons situated in different cantons. In this study, medical records of older prisoners (50+) were obtained from the respective authority upon consent and total anonymity was ensured. Data gathered from all available medical records included basic demographic information, education and prison sentencing. Healthcare data obtained were extensive in nature encompassing data related to illness types, number of visits to different health care providers and hospitals. The corresponding reasons for visits and outcomes of these visits were extracted. All data are analysed using statistical software SPSS 20.0. Results: Data were extracted for a total of 50 older prisoners living in Switzerland. The chosen prisons are located in German-speaking cantons. Preliminary results show that the age average was 56 years. For more than half, this was their first imprisonment. Nevertheless, a third of them were sentenced to measures (Art. 64 Swiss Criminal Code) which means that the length of the detention is indefinite and while release is possible it is in most cases not very likely. This entails that these prisoners will grow old in prison and some will even spend their remaining years there. Concerning their health, a third of the sample reported respiratory and cardiovascular illnesses and half reported suffering from some form of musculoskeletal related pain. Older prisoners were prescribed on average only 3.5 medications, which is significantly fewer than the number of medication prescribed to younger prisoners, whose data were also sampled. Conclusion: Access to healthcare is a right given to all prisoners through the principle of equivalence which is generally exercised in Switzerland. Prisoners growing old in prison will represent a challenge for prison health care services.

Statistics in archaeology: New directions

Connections between Statistics and Archaeology have always appeared veryfruitful. The objective of this paper is to offer an outlook of somestatistical techniques that are being developed in the most recentyears and that can be of interest for archaeologists in the short run.

Tying up the loose ends in simple correspondence analysis

Although correspondence analysis is now widely available in statistical software packages and applied in a variety of contexts, notably the social and environmental sciences, there are still some misconceptions about this method as well as unresolved issues which remain controversial to this day. In this paper we hope to settle these matters, namely (i) the way CA measures variance in a two-way table and how to compare variances between tables of different sizes, (ii) the influence, or rather lack of influence, of outliers in the usual CA maps, (iii) the scaling issue and the biplot interpretation of maps,(iv) whether or not to rotate a solution, and (v) statistical significance of results.