Thaís C. O. Fonseca

A influência das doenças crônicas na capacidade funcional dos idosos do Município de São Paulo, Brasil

The main focus of this study was the effect of chronic disease (hypertension, diabetes mellitus, heart disease, lung disease, cancer, and arthropathy) on the functional status (activities of daily living - ADL, instrumental activities of daily living - IADL) among the elderly, controlling for age, gender, living arrangements, education, and comorbidity. The analysis was based on information provided by the SABE Project, from the city of Sao Paulo, Brazil, including individuals 60 years of age and over (n = 1,769), from January 2000 to March 2001. A multinomial logistic regression model was used. Compared to the absence of dependency category, heart disease (OR = 1.82), arthropathy (OR = 1.59), lung disease (OR = 1.50), and hypertension (OR = 1.39) were the main diseases that affected the IADL dependency category. Lung disease (OR = 2.58), arthropathy (OR = 2.27), hypertension (OR = 2.13), and heart disease (OR = 2.10) had important impact on the IADL and ADL dependency categories. The results were statistically significant (p < 0.05).

Association between genital intraepithelial lesions and anal squamous intraepithelial lesions in HIV-negative women

OBJECTIVE The objective of the study was to evaluate the risk of anal squamous intraepithelial lesions (ASILs) in immunocompetent women with genital squamous intraepithelial lesions (GSILs). STUDY DESIGN This was a cross-sectional study that included 260 immunocompetent women divided into 2 study groups: 1 group included 184 women diagnosed with GSIL by genital colposcopy and biopsy, and the other included 76 controls. All subjects were submitted to anoscopy followed by a biopsy if pertinent. RESULTS Of 184 GSIL women, 32 (17.4%) had ASIL (P<.001). The risk of ASIL was 13.1 times greater for GSIL women when there were 3 or 4 genital sites involved. All cases of high-grade ASIL were found in women with cervical GSILs. Among risk factors, anal intercourse without a condom demonstrated an important association with ASIL (prevalence ratio adjusted for age=2.6). CONCLUSION There seems to be a strong association between ASIL and multicentric GSIL. Another factor related to ASIL was the practice of unprotected anal intercourse.

Dynamic Multiscale Spatiotemporal Models for Poisson Data

ABSTRACT We propose a new class of dynamic multiscale models for Poisson spatiotemporal processes. Specifically, we use a multiscale spatial Poisson factorization to decompose the Poisson process at each time point into spatiotemporal multiscale coefficients. We then connect these spatiotemporal multiscale coefficients through time with a novel Dirichlet evolution. Further, we propose a simulation-based full Bayesian posterior analysis. In particular, we develop filtering equations for updating of information forward in time and smoothing equations for integration of information backward in time, and use these equations to develop a forward filter backward sampler for the spatiotemporal multiscale coefficients. Because the multiscale coefficients are conditionally independent a posteriori, our full Bayesian posterior analysis is scalable, computationally efficient, and highly parallelizable. Moreover, the Dirichlet evolution of each spatiotemporal multiscale coefficient is parametrized by a discount factor that encodes the relevance of the temporal evolution of the spatiotemporal multiscale coefficient. Therefore, the analysis of discount factors provides a powerful way to identify regions with distinctive spatiotemporal dynamics. Finally, we illustrate the usefulness of our multiscale spatiotemporal Poisson methodology with two applications. The first application examines mortality ratios in the state of Missouri, and the second application considers tornado reports in the American Midwest.

Covariance Modeling for Multivariate Spatial Processes Based on Separable Approximations

The computational treatment of high dimensionality problems is a challenge. In the context of geostatistics, analyzing multivariate data requires the specification of the cross-covariance function, which defines the dependence between the components of a response vector for all locations in the spatial domain. However, the computational cost to make inference and predictions can be prohibitive. As a result, the use of complex models might be unfeasible. In this paper, we consider a flexible nonseparable covariance model for multivariate spatiotemporal data and present a way to approximate the full covariance matrix from two separable matrices of minor dimensions. The method is applied only in the likelihood computation, keeping the interpretation of the original model. We present a simulation study comparing the inferential and predictive performance of our proposal and we see that the approximation provides important gains in computational efficiency without presenting substantial losses in predictive terms.

Data Mining for Longitudinal Data with Different Treatments

The CLAssification and Data Analysis Group (CLADAG) of the Italian Statistical Society recently organised a competition, the ‘Young Researcher Data Mining Prize’ sponsored by the SAS Institute. This paper was the winning entry and in it we detail our approach to the problem proposed and our results. The main methods used are linear regression, mixture models, Bayesian autoregressive and Bayesian dynamic models.