Climate extreme events are becoming increasingly frequent worldwide, causing floods, drought, forest fires, landslides and heat or cold waves. Several studies have been developed on the assessment of trends in the occurrence of extreme events. However, most of these studies used traditional models, such as Poisson or negative binomial models. Thus, the main objective of this study is to use a space–time data counting approach in the modeling of the number of days with extreme precipitation as an alternative to the commonly used statistical methods. The study area is the Northeast Brazil region, and the analysis was carried out for the period between 1 January 1980 and 31 December 2010, by assessing the frequency of extreme precipitation represented by the R10 mm, R20 mm and R* indices.

To estimate the prevalence of coronary artery disease (CAD) in valvular heart disease of rheumatic (RVHD) and non-rheumatic (NVHD) etiology, assessing possible predictive factors for the presence of CAD. This is a cross-sectional study of a series of cases obtained from a pre-defined population, wherein 1,412 patients referred for heart surgery of any etiology were evaluated. Of these, 294 primary heart disease patients aged > or =40 submitted to cinecoronary arteriography (CA) were identified and studied. patients with RVHD presented lower prevalence of CAD (4%) when compared to NVHD (33.61%), p or =55, SAH, TACP, diabetes and dyslipidemia were all related directly to CAD, with the latter three being the most important variables for the disease. the prevalence of CAD among RVHD patients is low, whereas it is high among NVHD patients; the rheumatic etiology does not seem to have any beneficial effects on the prevalence of CAD; gender, age, SAH, TACP, dyslipidemia and diabetes were identified as being strongly associated with the presence of CAD. It is possible to define the criteria that indicate the need for pre-surgical CA in heart valve replacements, so that the standard indication after the age of 40 years can be avoided.

This paper develops an extension of spatiotemporal models that handle count data using nonhomogeneous Poisson processes. In this new proposal, we incorporate a seasonal cycle component in the definition of the intensity function to control possible effects produced by the occurrence of the event of interest in regular periods. The seasonal cycle can cause problems in estimating the shape parameter of the Weibull and generalized Goel intensity functions. This shape parameter serves to confront the research hypothesis that seeks to identify a trend in the occurrence rate of an event of interest. In the case of the Weibull intensity function, a value significantly equal to one of the shape parameters indicates a constant rate of occurrence, less than one indicates a decreasing rate, and greater than one indicates an increasing rate. In the case of the Goel intensity function, parameter values less than or equal to one indicate a decreasing occurrence rate, and values greater than one indicate the presence of a change point. We also built a spatial model using the Musa-Okumoto intensity function as an alternative to approximate counting processes for which there is a decreasing trend in the occurrence rate of the event of interest. We estimated the parameters of the proposed method from a Bayesian perspective. Finally, we fitted the proposed model and compared it with other approximations to analyze the frequency of extreme rainfall in the northern region of the states of Maranhão and Piauí in northeastern Brazil over ten years. Among the main results, we found that (1) the proposed method has proven superior in terms of fit and prediction performance than the other models, and (2) unlike other approximations, the proposed model does not detect changes in the rate of extreme rainfall occurrences.