Aims The aim of the present study was to extend our understanding of international trends in stroke and major sequelae in Europe and countries peripheral to Europe by assessing: (1) current mortality rates, (2) the most recent 15-year prevalence trends, and (3) the relationship between systolic blood pressure in community surveys and national stroke mortality. Methods and results Data were obtained from the World Health Organization (WHO www.who.int/whosis/database/mort/table.cfm), and represent national vital statistics as reported by 39 countries (European and Central Asian countries) using a standard format and population-based cardiovascular surveys. Total numbers of deaths by stroke (International Classification of Diseases 430–438, 444) and the age, sex-adjusted incidence rates were obtained and grouped according to three standard demographic categories: A, B, and C (WHO). A Bayesian linear mixed effect model was fitted to the annual mortality rates. Higher rates of stroke mortality were observed for B and C group countries as compared with those countries belonging to Group A (e.g. Bulgaria 273.9 and 281.1; Israel 37.7 and 45.4 per 100 000 men and women, respectively). Even though the mortality rates within the country groupings were relatively similar, countries with marked deviation from the average were observed, mainly in Groups B and C. Stroke mortality decreased sharply in Group A during the period of study; conversely it had increased substantially in Group B and to a lesser extent in Group C. For both sexes markedly higher rates were noted moving from west to east, with some exceptions. Conclusion We have entered a period of rapidly increasing international inequality in stroke risk, where countries with low adult mortality in the latter 20th century extended their downward trend and countries with moderate as well as high mortality have on average seen unprecedented increases in death rates from stroke.

This paper deals with multivariate disease mapping. We propose a novel framework that encompasses most of the models already proposed. Our framework starts with a simple identity, reformulating Kronecker products of covariance matrices as simple matrix products. This formula is computationally convenient, and its generalizations reproduce most of the proposals in the disease mapping literature. Use of the identity leads to a flexible, general and computationally convenient modelling framework, making it possible to combine spatial dependence structures and different relationships between diseases with limited effort. Moreover, as the proposed modelling framework covers most of the Gaussian Markov random field-based multivariate disease mapping models in the literature, it allows comparison of all these models in a common context, thus helping us to understand them better.

One important aspect of Bayesian model selection is how to deal with huge model spaces, since the exhaustive enumeration of all the models entertained is not feasible and inferences have to be based on the very small proportion of models visited. This is the case for the variable selection problem with a moderately large number of possible explanatory variables considered in this article. We review some of the strategies proposed in the literature, from a theoretical point of view using arguments of sampling theory and in practical terms using several examples with a known answer. All our results seem to indicate that sampling methods with frequency-based estimators outperform searching methods with renormalized estimators. Supplementary materials for this article are available online.