This ecological analysis addresses the association between income inequality and health status in the municipality of Rio de

A strong genetic role in the etiology of attention-deficit hyperactivity disorder (ADHD) has been demonstrated by several studies using different methodologies. Shortcomings of genetic studies often include the lack of golden standard practices for diagnosis for ADHD, the use of categorical instead of a dimensional approach, and the disregard for assortative mating phenomenon in parents. The current study aimed to overcome these shortcomings and analyze data through a novel statistical approach, using multilevel analyses with Bayesian procedures and a specific mathematical model, which takes into account data with an elevated number of zero responses (expected in samples with few or no ADHD symptoms). Correlations of parental clinical variables (ADHD, anxiety and depression) to offspring psychopathology may vary according to gender and type of symptoms. We aimed to investigate how those variables interact within each other. One hundred families, comprising a proband child or adolescent with ADHD or a typically developing child or adolescent were included and all family members (both biological parents, the proband child or adolescent and their sibling) were examined through semi-structured interviews using DSM-IV criteria. Results indicated that: (a) maternal clinical variables (ADHD, anxiety and depression) were more correlated with offspring variables than paternal ones; (b) maternal inattention (but not hyperactivity) was correlated with both inattention and hyperactivity in the offspring; (c) maternal anxiety was correlated with offspring inattention; on the other hand, maternal inattention was correlated with anxiety in the offspring. Although a family study design limits the possibility of revealing causality and cannot disentangle genetic and environmental factors, our findings suggest that ADHD, anxiety and depression are variables that correlate in families and should be addressed together. Maternal variables significantly correlated with offspring variables, but the paternal variables did not.

SUMMARYIn this article, we present an application of models with temporal and spatial components, from the Bayesian point of view, on data pollutants collected in 16 different monitoring sites located in the Metropolitan Area of Rio de Janeiro during 1999. All the models considered here assume conditionally independent observations, with a mean specified by the sum of random temporal and spatial components and a linear function of the maximum daily temperature and indicators of the day of the week. Our aim here is to analyze distinct specifications for the components, assuming different kinds of modeling that are not usually compared. The comparison is based on the posterior predictive loss function proposed by Gelfand and Ghosh (1998). The best specifications for the spatial component were the ones which considered a geostatistical approach to its correlation function. The best specification for the temporal component was the stationary autoregressive form. The pollutant concentrations were interpolated in a grid of points in the area of higher population density at a fixed period of time for the selected model.

This article proposes a modeling approach for handling spatial heterogeneity present in the study of the geographical pattern of deaths due to cerebrovascular disease.The framework involvesa point pattern analysis with components exhibiting spatial variation. Preliminary studies indicate that mortality of this disease and the effect of relevant covariates do not exhibit uniform geographic distribution. Our model extends a previously proposed model in the literature that uses spatial and non-spatial variables by allowing for spatial variation of the effect of non-spatial covariates. A number of relative risk indicators are derived by comparing different covariate levels, different geographic locations, or both. The methodology is applied to the study of the geographical death pattern of cerebrovascular deaths in the city of Rio de Janeiro. The results compare well against existing alternatives, including fixed covariate effects. Our model is able to capture and highlight important data information that would not be noticed otherwise, providing information that is required for appropriate health decision-making.

In this work we present a Bayesian analysis in linear regression models with spatially varying coefficients for modeling and inference in spatio-temporal processes. This kind of model is particularly appealing in situations where the effect of one or more explanatory processes on the response present substantial spatial heterogeneity. We describe for this model how to make inference about the regression coefficients and response processes under two scenarios: when the explanatory processes are known throughout the study region, and when they are known only at the sampling locations. Using a simulation experiment we investigate how parameter inference and interpolation performance are affected by some features of the data and prior distribution that is used. The proposed methodology is used to model the dataset on PM 10 levels in the metropolitan region of Rio de Janeiro presented in Paez and Gamerman (2003).