Mortality due to myocardial infarction in the Bavarian population during World Cup Soccer 2006.

BACKGROUND: Previously, we had demonstrated that the World Cup Soccer 2006 provoked levels of emotional stress sufficient to increase the incidence of acute cardiovascular events. We sought to assess whether mortality was also increased as a result. METHOD: We analyzed daily data on mortality due to myocardial infarction (MI) and total mortality using data from the Bavarian State Office for Statistics. We retrospectively assessed study periods from 2006, 2005 and 2003. Quasi-Poisson regression with a log link to model the number of daily deaths was used. To be able to account for a possible delay, we also fitted a cubic distributed lag quasi-Poisson model for both 1 and 2 weeks post-exposure. RESULTS: A total of 6,699 deaths due to MI were investigated. No increase in death was found on days of World Cup matches either with or without German participation compared to the matched control periods. In addition, none of the analyses showed a significant effect of the (lagged) exposure to the risk period. Likewise, total mortality rates remained unchanged over the entire period of our analysis. CONCLUSION: During World Cup Soccer, the number of deaths due to myocardial infarction was not measurably increased compared to a matched control period. Thus, we could not demonstrate a translation of a stress-induced increase of cardiac morbidity into a noticeable increase in mortality. However, our findings are based on a public mortality registry, which may be flawed in many ways, regarding ascertainment of causes of death, in particular.

Spatial quantitative analysis of fluorescently labeled nuclear structures: problems, methods, pitfalls.

The vast majority of microscopic data in biology of the cell nucleus is currently collected using fluorescence microscopy, and most of these data are subsequently subjected to quantitative analysis. The analysis process unites a number of steps, from image acquisition to statistics, and at each of these steps decisions must be made that may crucially affect the conclusions of the whole study. This often presents a really serious problem because the researcher is typically a biologist, while the decisions to be taken require expertise in the fields of physics, computer image analysis, and statistics. The researcher has to choose between multiple options for data collection, numerous programs for preprocessing and processing of images, and a number of statistical approaches. Written for biologists, this article discusses some of the typical problems and errors that should be avoided. The article was prepared by a team uniting expertise in biology, microscopy, image analysis, and statistics. It considers the options a researcher has at the stages of data acquisition (choice of the microscope and acquisition settings), preprocessing (filtering, intensity normalization, deconvolution), image processing (radial distribution, clustering, co-localization, shape and orientation of objects), and statistical analysis.