As causas mal definidas de morte refletem problemas de acesso aos serviços de saúde e de qualidade da assistência médica, e indicam precariedade no registro de dados no Sistema de Informações sobre Mortalidade (SIM). Selecionou-se uma amostra de municípios na Macrorregião Nordeste de Minas Gerais, Brasil, com o objetivo de investigar as mortes por causas mal definidas e os óbitos não notificados ao SIM em 2007, por meio do método da autópsia verbal. Esse método possibilitou esclarecer 87% das causas dos óbitos investigados, das quais 17% (n = 37) eram por causas violentas. Ao final do estudo, dentre os 779 investigados, 9,5% (n = 74) eram óbitos por causa externa encontrados fora do SIM. A distribuição por causas foi semelhante entre os óbitos notificados e não notificados ao SIM para as causas naturais, mas diferente quando incluídas as causas externas. Conclui-se que a investigação de óbitos com a metodologia da autópsia verbal pode ser um instrumento de grande valia para o aprimoramento do SIM no estado possibilitando o esclarecimento das causas de morte e também quanto à cobertura dos eventos.

No abstract

a b s t r a c tClasses of shape mixtures of independent and dependent multivariate skew-normal distributions are considered and some of their main properties are studied. If interpreted from a Bayesian point of view, the results obtained in this paper bring tractability to the problem of inference for the shape parameter, that is, the posterior distribution can be written in analytic form. Robust inference for location and scale parameters is also obtained under particular conditions.

Some recent research on fluvial processes suggests the idea that some hydrological variables, such as flood flows, are upper-bounded. However, most probability distributions that are currently employed in flood frequency analysis are unbounded to the right. This paper describes an exploratory study on the joint use of an upper-bounded probability distribution and non-systematic flood information, within a Bayesian framework. Accordingly, the current PMF maximum discharge appears as a reference value and a reasonable estimate of the upper-bound for maximum flows, despite the fact that PMF determination is not unequivocal and depends strongly on the available data. In the Bayesian context, the uncertainty on the PMF can be included into the analysis by considering an appropriate prior distribution for the maximum flows. In the sequence, systematic flood records, historical floods, and paleofloods can be included into a compound likelihood function which is then used to update the prior information on the upper-bound. By combining a prior distribution describing the uncertainties of PMF estimates along with various sources of flood data into a unified Bayesian approach, the expectation is to obtain improved estimates of the upper-bound. The application example was conducted with flood data from the American river basin, near the Folsom reservoir, in California, USA. The results show that it is possible to put together concepts that appear to be incompatible: the deterministic estimate of PMF, taken as a theoretical limit for floods, and the frequency analysis of maximum flows, with the inclusion of non-systematic data. As compared to conventional analysis, the combination of these two concepts within the logical context of Bayesian theory, contributes an advance towards more reliable estimates of extreme floods.Keywords Probable maximum flood Á Hydrologic extreme events Á Flood frequency Á Bayesian analysis List of symbols LB Lower-bounded floods as referring to the floods that are larger than a given low threshold LB UB Upper-bounded floods as referring to the floods that are smaller than a given high threshold UB DB Double-bounded floods as referring to the floods that are comprised within the discharge interval (LR, UR) EX Annual flood-peaks with exact values a Upper bound of the LN4 distribution eLower bound of the LN4 distribution r Y

No abstract