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Background-A key to reduce and eradicate racial disparities in hypertension outcomes is to understand their causes.We aimed at evaluating racial differences in antihypertensive drug utilization patterns and blood pressure control by insurance status, age, sex, and presence of comorbidities. Methods and Results-A total of 8796 hypertensive individuals ≥18 years of age were identified from the National Health and Nutrition Examination Survey (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) Racial disparities also persisted in subgroups stratified by age (≥60 and <60 years of age) and presence of comorbidities but worsened among patients <60 years of age. Conclusions-Black and Hispanic patients had poorer hypertension control compared with whites, and these differences were more pronounced in younger and uninsured patients. Although black patients received more intensive antihypertensive therapy, Hispanics were undertreated. Future studies should further explore all aspects of these disparities to improve cardiovascular outcomes. (Circ Cardiovasc Qual Outcomes. 2017;10:e003166.

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in University of Innsbruck AbstractQuantile regression provides a convenient framework for analyzing the impact of covariates on the complete conditional distribution of a response variable instead of only the mean. While frequentist treatments of quantile regression are typically completely nonparametric, a Bayesian formulation relies on assuming the asymmetric Laplace distribution as auxiliary error distribution that yields posterior modes equivalent to frequentist estimates. In this paper, we utilize a location-scale-mixture of normals representation of the asymmetric Laplace distribution to transfer different flexible modeling concepts from Gaussian mean regression to Bayesian semiparametric quantile regression. In particular, we will consider high-dimensional geoadditive models comprising LASSO regularization priors and mixed models with potentially non-normal random effects distribution modeled via a Dirichlet process mixture. These extensions are illustrated using two large-scale applications on net rents in Munich and longitudinal measurements on obesity among children.

Thin-plate splines have been widely used as spatial smoothers. In this article, we present a Bayesian adaptive thin-plate spline (BATS) suitable for modeling nonstationary spatial data. Fully Bayesian inference can be carried out through efficient Markov chain Monte Carlo simulation. Performance is demonstrated with simulation studies and by an application to a rainfall dataset. A FORTRAN program implementing the method, a proof of the theorem, and the dataset in this article are available online.

Cortical surface fMRI (cs-fMRI) has recently grown in popularity versus traditional volumetric fMRI, as it allows for more meaningful spatial smoothing and is more compatible with the common assumptions of isotropy and stationarity in Bayesian spatial models. However, as no Bayesian spatial model has been proposed for cs-fMRI data, most analyses continue to employ the classical, voxel-wise general linear model (GLM) (Worsley and Friston 1995). Here, we propose a Bayesian GLM for cs-fMRI, which employs a class of sophisticated spatial processes to flexibly model latent activation fields. We use integrated nested Laplacian approximation (INLA), a highly accurate and efficient Bayesian computation technique (Rue et al. 2009). To identify regions of activation, we propose an excursions set method based on the joint posterior distribution of the latent fields, which eliminates the need for multiple comparisons correction. Finally, we address a gap in the existing literature by proposing a novel Bayesian approach for multi-subject analysis. The methods are validated and compared to the classical GLM through simulation studies and a motor task fMRI study from the Human Connectome Project. The proposed Bayesian approach results in smoother activation estimates, more accurate false positive control, and increased power to detect truly active regions.

Long-term culture of hepatocyte spheroids with high ammonia clearance is valuable for therapeutic applications, especially the bioartificial liver. However, the optimal conditions are not well studied. We hypothesized that liver urea cycle enzymes can be induced by high protein diet and maintain on a higher expression level in rat hepatocyte spheroids by serum-free medium (SFM) culture and coculture with mesenchymal stromal cells (MSCs). Rats were feed normal protein diet (NPD) or high protein diet (HPD) for 7 days before liver digestion and isolation of hepatocytes. Hepatocyte spheroids were formed and maintained in a rocked suspension culture with or without MSCs in SFM or 10% serum-containing medium (SCM). Spheroid viability, kinetics of spheroid formation, hepatic functions, gene expression, and biochemical activities of rat hepatocyte spheroids were tested over 14 days of culture. We observed that urea cycle enzymes of hepatocyte spheroids can be induced by high protein diet. SFM and MSCs enhanced ammonia clearance and ureagenesis and stabilized integrity of hepatocyte spheroids compared to control conditions over 14 days. Hepatocytes from high protein diet-fed rats formed spheroids and maintained a high level of ammonia detoxification for over 14 days in a novel SFM. Hepatic functionality and spheroid integrity were further stabilized by coculture of hepatocytes with MSCs in the spheroid microenvironment. These findings have direct application to development of the spheroid reservoir bioartificial liver.

The smoothing spline is one of the most popular curve-fitting methods, partly because of empirical evidence supporting its effectiveness and partly because of its elegant mathematical formulation. However, there are two obstacles that restrict the use of smoothing spline in practical statistical work. Firstly, it becomes computationally prohibitive for large data sets because the number of basis functions roughly equals the sample size. Secondly, its global smoothing parameter can only provide constant amount of smoothing, which often results in poor performances when estimating inhomogeneous functions. In this work, we introduce a class of adaptive smoothing spline models that is derived by solving certain stochastic differential equations with finite element methods. The solution extends the smoothing parameter to a continuous data-driven function, which is able to capture the change of the smoothness of underlying process. The new model is Markovian, which makes Bayesian computation fast. A simulation study and real data example are presented to demonstrate the effectiveness of our method.