Spatial Analysis of the Sociodemographic Characteristics, Comorbidities, Hospitalization, Signs, and Symptoms Among Hospitalized Coronavirus Disease 2019 Cases in the State of Rio De Janeiro, Brazil.

After more than 1 year from the beginning of the pandemic, the coronavirus disease 2019 (COVID-19) has reached all continents. The number of infected people is still increasing, and Brazil is among the countries with the highest number of registered cases in the world. In this study, we investigated the profile of hospitalized COVID-19 cases and the eventual clusters of similar areas, using geographic information systems. The study was conducted using secondary data. Variables such as sociodemographic characteristics, comorbidities, hospitalization, signs, and symptoms among confirmed cases were considered for each microregion/city of the state of Rio de Janeiro. These proportions were used when calculating the Global Moran's I. The local indicator of spatial association was used to identify local clusters. A significant global spatial auto correlation was found in 28% of the variables. The presence of spatial autocorrelation indicates that the proportions of patients with COVID-19 according to these characteristics are spatially oriented. Moran maps reveal 2 clusters, 1 of high proportions and 1 of low proportions. Understanding the geographic patterns of COVID-19 may assist public health investigators, contributing to actions to prevent and control the pandemic in the state.

Impact of protocol change on individual factors related to course of adverse reactions to chemotherapy for breast cancer.

PURPOSE: Asthenia, myalgia, arthralgia, mucositis, abdominal pain, diarrhea, and neutropenia are adverse reactions commonly reported by women undergoing chemotherapy. Traditional approaches do not take into account the effect that chemotherapeutic changes and variable interactions can cause in adverse reactions. We aimed to identify the impact of the change of a chemotherapy protocol within the same treatment in profiles associated with adverse reactions. METHODS: A total of 166 women admitted to the Brazilian National Institute of Cancer (INCA) were followed. Polymorphisms, clinical variables, and FAC-D protocols (3 cycles of cyclophosphamide, 5-fluorouracil, and doxorubicin followed by 3 cycles of docetaxel) composed the set of independent variables analyzed. Reaction levels were recorded at the end of each chemotherapy cycle via interviews. Marginal models were fitted. RESULTS: The results of marginal models for non-hematological reactions revealed that the docetaxel phase was associated with increased reaction levels compared with the FAC phase. In addition, the set of factors associated with the reactions changed in each protocol. The post-menopausal status was related to high levels of asthenia in docetaxel protocol whereas CYP2B6 polymorphism (rs3745274) was related to high levels in FAC protocol. Regarding the docetaxel phase, high levels of abdominal pain and mucositis were related to CBR3 gene (rs8133052) polymorphism and diabetes respectively. CONCLUSION: The results suggest the need for monitoring non-hematological reactions during the docetaxel phase of FAC-D treatment. The factors related to more severe reactions depend on the chemotherapy protocol used.

Ensemble Classifiers for Predicting HIV-1 Resistance from Three Rule-Based Genotypic Resistance Interpretation Systems.

Resistance to antiretrovirals (ARVs) is a major problem faced by HIV-infected individuals. Different rule-based algorithms were developed to infer HIV-1 susceptibility to antiretrovirals from genotypic data. However, there is discordance between them, resulting in difficulties for clinical decisions about which treatment to use. Here, we developed ensemble classifiers integrating three interpretation algorithms: Agence Nationale de Recherche sur le SIDA (ANRS), Rega, and the genotypic resistance interpretation system from Stanford HIV Drug Resistance Database (HIVdb). Three approaches were applied to develop a classifier with a single resistance profile: stacked generalization, a simple plurality vote scheme and the selection of the interpretation system with the best performance. The strategies were compared with the Friedman's test and the performance of the classifiers was evaluated using the F-measure, sensitivity and specificity values. We found that the three strategies had similar performances for the selected antiretrovirals. For some cases, the stacking technique with naïve Bayes as the learning algorithm showed a statistically superior F-measure. This study demonstrates that ensemble classifiers can be an alternative tool for clinical decision-making since they provide a single resistance profile from the most commonly used resistance interpretation systems.

Exploring spatial patterns in the associations between local AIDS incidence and socioeconomic and demographic variables in the state of Rio de Janeiro, Brazil.

Access to antiretroviral therapy (ART), universally provided in Brazil since 1996, resulted in a reduction in overall morbidity and mortality due to AIDS or AIDS-related complications, but in some municipalities of Rio de Janeiro, AIDS incidence remains high. Public health surveillance remains an invaluable tool for understanding current AIDS epidemiologic patterns and local socioeconomic and demographic factors associated with increased incidence. Geographically Weighted Poisson Regression (GWPR) explores spatial varying impacts of these factors across the study area focusing attention on local variations in ecological associations. The set of sociodemographic variables under consideration revealed significant associations with local AIDS incidence and these associations varied geographically across the study area. We find the effects of predictors on AIDS incidence are not constant across the state, contrary to assumptions in the global models. We observe and quantify different local factors driving AIDS incidence in different parts of the state.

Lopinavir Resistance Classification with Imbalanced Data Using Probabilistic Neural Networks.

Resistance to antiretroviral drugs has been a major obstacle for long-lasting treatment of HIV-infected patients. The development of models to predict drug resistance is recognized as useful for helping the decision of the best therapy for each HIV+ individual. The aim of this study was to develop classifiers for predicting resistance to the HIV protease inhibitor lopinavir using a probabilistic neural network (PNN). The data were provided by the Molecular Virology Laboratory of the Health Sciences Center, Federal University of Rio de Janeiro (CCS-UFRJ/Brazil). Using bootstrap and stepwise techniques, ten features were selected by logistic regression (LR) to be used as inputs to the network. Bootstrap and cross-validation were used to define the smoothing parameter of the PNN networks. Four balanced models were designed and evaluated using a separate test set. The accuracies of the classifiers with the test set ranged from 0.89 to 0.94, and the area under the receiver operating characteristic (ROC) curve (AUC) ranged from 0.96 to 0.97. The sensitivity ranged from 0.94 to 1.00, and the specificity was between 0.88 and 0.92. Four classifiers showed performances very close to three existing expert-based interpretation systems, the HIVdb, the Rega and the ANRS algorithms, and to a k-Nearest Neighbor.

The acquired immunodeficiency syndrome in the State of Rio de Janeiro, Brazil: a spatio-temporal analysis of cases reported in the period 2001-2010.

Despite increased funding for research on the human immunodeficiency virus (HIV) and the acquired immunodeficiency syndrome (AIDS), neither vaccine nor cure is yet in sight. Surveillance and prevention are essential for disease intervention, and it is recognised that spatio-temporal analysis of AIDS cases can assist the decision-making process for control of the disease. This study investigated the dynamic, spatial distribution of notified AIDS cases in the State of Rio de Janeiro, Brazil, between 2001 and 2010, based on the annual incidence in each municipality. Sequential choropleth maps were developed and used to analyse the incidence distribution and Moran's I spatial autocorrelation statistics was applied for characterisation of the spatio-temporal distribution pattern. A significant, positive spatial autocorrelation of AIDS incidence was observed indicating that municipalities with high incidence are likely to be close to other municipalities with similarly high incidence and, conversely, municipalities with low incidence are likely to be surrounded by municipalities with low incidence. Two clusters were identified; one hotspot related to the State Capital and the other with low to intermediate AIDS incidence comprising municipalities in the north-eastern region of the State of Rio de Janeiro.

Sensitivity and specificity of the World Health Organization dengue classification schemes for severe dengue assessment in children in Rio de Janeiro.

BACKGROUND: The clinical definition of severe dengue fever remains a challenge for researchers in hyperendemic areas like Brazil. The ability of the traditional (1997) as well as the revised (2009) World Health Organization (WHO) dengue case classification schemes to detect severe dengue cases was evaluated in 267 children admitted to hospital with laboratory-confirmed dengue. PRINCIPAL FINDINGS: Using the traditional scheme, 28.5% of patients could not be assigned to any category, while the revised scheme categorized all patients. Intensive therapeutic interventions were used as the reference standard to evaluate the ability of both the traditional and revised schemes to detect severe dengue cases. Analyses of the classified cases (n = 183) demonstrated that the revised scheme had better sensitivity (86.8%, P<0.001), while the traditional scheme had better specificity (93.4%, P<0.001) for the detection of severe forms of dengue. CONCLUSIONS/SIGNIFICANCE: This improved sensitivity of the revised scheme allows for better case capture and increased ICU admission, which may aid pediatricians in avoiding deaths due to severe dengue among children, but, in turn, it may also result in the misclassification of the patients' condition as severe, reflected in the observed lower positive predictive value (61.6%, P<0.001) when compared with the traditional scheme (82.6%, P<0.001). The inclusion of unusual dengue manifestations in the revised scheme has not shifted the emphasis from the most important aspects of dengue disease and the major factors contributing to fatality in this study: shock with consequent organ dysfunction.

A framework to identify gene expression profiles in a model of inflammation induced by lipopolysaccharide after treatment with thalidomide.

BACKGROUND: Thalidomide is an anti-inflammatory and anti-angiogenic drug currently used for the treatment of several diseases, including erythema nodosum leprosum, which occurs in patients with lepromatous leprosy. In this research, we use DNA microarray analysis to identify the impact of thalidomide on gene expression responses in human cells after lipopolysaccharide (LPS) stimulation. We employed a two-stage framework. Initially, we identified 1584 altered genes in response to LPS. Modulation of this set of genes was then analyzed in the LPS stimulated cells treated with thalidomide. RESULTS: We identified 64 genes with altered expression induced by thalidomide using the rank product method. In addition, the lists of up-regulated and down-regulated genes were investigated by means of bioinformatics functional analysis, which allowed for the identification of biological processes affected by thalidomide. Confirmatory analysis was done in five of the identified genes using real time PCR. CONCLUSIONS: The results showed some genes that can further our understanding of the biological mechanisms in the action of thalidomide. Of the five genes evaluated with real time PCR, three were down regulated and two were up regulated confirming the initial results of the microarray analysis.

Random-effects models in investigating the effect of vitamin A in childhood diarrhea.

PURPOSE: By adopting more appropriate and powerful statistical methods that fully exploit longitudinal structure, we re-analyze and extend previously published results from a large community trial to investigate the effect of vitamin A supplementation on the prevalence and severity of diarrhea in young children. METHODS: Generalized linear mixed models were used to allow for repeated measures in a reanalysis of a double-blind, randomized, placebo-controlled community trial conducted in a cohort of children in northeastern Brazil during 1 year. The response variable was weekly number of days with diarrhea for each child, and Markov Chain Monte Carlo methods were used to estimate model parameters. RESULTS AND CONCLUSIONS: Random effects suitably accounted for the underlying heterogeneity between and within children, and our longitudinal analysis shows a significant beneficial effect of vitamin A supplementation that was inconclusive in previously reported simple summary analyses of these data. Risk for diarrhea infection was estimated to be 1.57 times greater for a child administered a placebo as opposed to vitamin A (95% credible interval, 1.17-2.12). Additionally, we identified previously unreported temporal effects in these data, showing a decreasing daily probability of diarrhea for both groups during the trial and treatment-time interaction.

Metodologia para estabilizaçäo de proporçöes de fatores de risco de saúde / Methodology for the stabilization of health rsk factors

O presente trabalho apresenta uma metodologia que permite a estabilizaçäo das proporçöes de fatores de risco de saúde. A seguir faz-se uma análise de como essa estabilizaçäo contribui à diferenciaçäo visual de áreas homogêneas de risco. Para fazer a comparaçäo visual dos resultados foi necessária a geraçäo de agrupamentos usando técnicas de análise multivariada, onde foram seguidos três passos. 1) estabilizaçäo de proporçöes de fatores de risco; 2) reduçäo do número de fatores de risco usando a Análise de Componetes Principais; 3) geraçäo de agrupamentos de casos usando a Análise de "Cluste".

Aplicaçäo de "path analysis" à modelagem de determinantes da mortalidade em menores de um ano / Application of the \"path analysis\" to modelling of mortality determination in under one year old

Neste trabalho utilizaram-se as técnicas de path analysis para descrever as interrelaçöes entre alguns determinantes da mortalidade em menores de um ano. Para estimar os coeficientes, efeitos indiretos e efeitos totais do modelo, utilizou-se o programa LISREL. Os resultados indicam que a área per capita de terra cultivada em cada município possui o maior efeito direto na mortalidade analisada, mesmo controlando-se os efeitos das variáveis de "renda" e "leitos". O maior efeito indireto é exercido pela variável de "alfabetizaçäo".

Mapeamento geográfico de acidentes de trânsito usando ferramentas de inferência bayesiana / Geographic mapping of traffic accidents using tool of Bayesian interference

O estudo da variação espacial de taxas de mortalidade, incluindo a escolha do método de estimação (entre clássicos e Bayesianos), foi realizado utilizando dados de mortalidade por acidentes de trânsito em São Paulo, Brasil, entre 1989 e 1993.

Redes neurais para previsäo de séries temporais epidemiológicas / Neuronal networks to predict epidemiological time series

Propomos o uso de redes neurais artificiais (RNs) como alternativa para prever séries temporais epidemiológicas. Testes com uma série de ocorrência de hepatite B nos E.U.A. obtiveram resultados próximos aos de modelos ARIMA desenvolvidos no CDC.

Priorizaçäo de alternativas em saúde com múltiplos critérios: utilizaçäo do apoio multicritério à decisäo / Priorization of health alternative with criteria multiple: use decision multicentric support

Neste trabalho é proposta a utilização do Apoio Multicritério à Decisão para priorização de alternativas em Saúde. Foi realizado um teste piloto com a Secretaria Municipal de Saúde (SMS) utilizando-se o Processo Analítico Hierárquico (AHP). Os resultados confirmam o grande potencial desta abordagem no apoio à decisão.

O SIG-APS: um sistema de informaçäo geográfica em atençäo primária em saúde / SIG-APS: geographic information system in primary health care

O presente trabalho apresenta o SIG-APS, uma ferramenta para apoio na tomada de decisões em nível de Sistemas Locais de Saúde, SILOS, no qual a análise estatística de dados é feita mediante a implementação de interfaces com aplicativos estatísticos convencionais. Como produto final é apresentada a distribuição espacial desses resultados. O SIG-APS foi desenvolvido para ser utilizado em ambiente Windows, para computadores pessoais constituindo-se assim em uma tecnologia de fácil acesso e baixo custo.