Multilevel Conditional Autoregressive models for longitudinal and spatially referenced epidemiological data.

Multilevel Conditional Autoregressive (CAR) models help to explain the spatial effect in epidemiological studies, where subjects are nested within geographical units. This paper has two goals. Firstly, it further develops the multilevel models for longitudinal data by adding existing random effects with CAR structures that change over time. We name these models MLM tCARs. We compare the MLM tCARs to the classical multilevel growth model via simulation studies. We observe a better performance of the MLM tCARs, to retrieve the true regression coefficients and with better fit. Secondly, it provides a comprehensive decision tree for analysing data in epidemiological studies with spatially nested structure: we also consider the Multilevel CAR models (MLM CARs) for cross-sectional studies in simulation studies. We apply the models comparatively on the analysis of the association between greenness and depression in the longitudinal Heinz Nixdorf Recall Study. The results show negative association between greenness and depression.

Spatio-temporal analysis of the risk of depression at district-level and association with greenness based on the Heinz Nixdorf Recall Study.

In urban health studies where spatial and temporal changes are of importance, spatio-temporal variations are usually neglected. For the Heinz Nixdorf Recall Study, we investigate spatio-temporal variation in analyses of effects of urban greenness on depression by including spatio-temporal random effect terms in a Poisson model on district level. Our results show negative associations between greenness and depression. The findings suggest strong temporal autocorrelation and weak spatial effects. Even if the weak spatial effects are suggestive of neglecting them, as in our case, spatio-temporal random effects should be taken into account to provide reliable inference in urban health studies.

A Bayesian changepoint analysis of ChIP-Seq data of Lamin B.

The spatial organisation of the chromosomes in the nucleus is influenced by chromatin regions binding to the nucleic lamina, i.e., the inner part of the nucleic envelope. To investigate the architecture of chromosomes in the interphase nucleus, it is thus of high interest to detect such chromatin segments. This goal can be achieved by considering the fibrous protein Lamin B as a surrogate, since regions of high abundance of Lamin B can indicate chromatin segments attached to the nucleic lamina. We analyse ChIP-Seq (Chromatin-Immunoprecipitation Sequencing) data from an experiment that is designed to record Lamin B abundance. We introduce a Bayesian segmentation procedure in which a Markov Chain Monte Carlo (MCMC) algorithm is used for inference about the desired segmentation. The procedure is based on a Bayesian hierarchical model. Inference allows the distinction between regions of high versus low levels of Lamin B, and therefore, gives an insight into the binding of the chromatin to the nucleic envelope. An implementation of this approach is available in the statistical software environment R. This article is part of a special issue entitled: Computational proteomics in the post-identification era. Guest Editors: Martin Eisenacher and Christian Stephan.

Munich Oktoberfest experience: remarkable impact of sex and age in ethanol intoxication.

Approximately 5,000 of 6 million annual visitors of the Oktoberfest in Munich have to undergo medical treatment. Patients with alcohol intoxication without trauma or further complications are all treated in a specialized medical camp. We studied these patients in order to identify risk factors and to assess the relevance of the Glasgow Coma Score (GCS) and of ethanol blood concentrations for patient management. In 2004 totally 405 patients suffering from ethanol intoxication without trauma were treated in the medical camp. A complete set of the following data was obtained from all 405 patients: GCS, ethanol blood concentration, age, sex, blood pressure (mean, systolic and diastolic), body temperature, heart rate, blood sugar, GOT, gamma-GT, and CK. A multivariate logistic regression model was applied to identify risk factors predicting patients at increased risk of hospitalization. Low GCS (< or =8 vs. >8, OR: 4.18, CI: 1.96-8.65) low age (20-29 vs. > or =30 years, OR: 2.35, CI: 1.05-5.65) and male gender (male vs. female, OR: 3.58, CI: 1.36-9.34) independently predicted patients that had to be hospitalized. All other parameters including ethanol blood concentrations were not explanatory. Patients with GCS < or = 8 (n = 66) had a lower median blood pressure (P = 0.0312) and showed a smaller increase in blood pressure during the observation period compared to patients with GCS > 8 (P < 0.001), suggesting that this subgroup may require longer recovery periods. Men aged 20-29 years were at highest risk for hospital admission. Increased risk could not be explained by higher ethanol blood concentrations in this subgroup. Importantly, GCS < 6 does not justify endotracheal intubation in ethanol intoxicated patients, when further complications, such as trauma, can be excluded.