The Effect of Hepatitis C on Maternal Bile Acid Level and the Fetal Left Ventricular Tei Index

Hepatitis C (HCV) is a common form of liver disease encountered in pregnancy. The purpose of this study is to evaluate if hepatitis C is associated with elevated maternal serum bile acids and abnormal fetal cardiac function measured by the left ventricular Tei index in the absence of intrahepatic cholestasis of pregnancy. This is a prospective cohort study on pregnant women with hepatitis C seen through Marshall University’s high-risk obstetrics clinic from 2013 to 2014. Women with hepatitis C had a laboratory evaluation and an ultrasound on the fetus to calculate the left ventricular Tei index. Demographic information and delivery outcomes were recorded. There were 77 participants with hepatitis C recruited and consented for this study. Sixty-one participants had complete laboratory and delivery information available for analysis. Twenty-one participants had a viral load that was not detectable and 40 participants had a detectable viral load. The mean viral load overall was 1943771 IU/mL (SD 4257143). There was no difference in Tei index between detectable and non-detectable viral load, 0.41 and 0.38 respectively (p = 0.41). There was no statistical difference in bile acid level between detectable and undetectable viral load, 12 and 8 µmol/L respectively (p = 0.05). Hepatic liver disease manifested by elevated hepatitis C viral load or elevated bile acids did not affect the left ventricular Tei index

Religiosity and Sexual Risk Behaviors Among African American Cocaine Users in the Rural South: Religion and Sex Risk

Racial and geographic disparities in human immunodeficency virus (HIV) are dramatic and drug use is a significant contributor to HIV risk. Within the rural South, African Americans who use drugs are at extremely high risk. Due to the importance of religion within African American and rural Southern communities, it can be a key element of culturally-targeted health promotion with these populations. Studies have examined religion’s relationship with sexual risk in adolescent populations, but few have examined specific religious behaviors and sexual risk behaviors among drug-using African American adults. This study examined the relationship between well-defined dimensions of religion and specific sexual behaviors among African Americans who use cocaine living in the rural southern United States

Measuring and controlling medical record abstraction (MRA) error rates in an observational study.

BACKGROUND: Studies have shown that data collection by medical record abstraction (MRA) is a significant source of error in clinical research studies relying on secondary use data. Yet, the quality of data collected using MRA is seldom assessed. We employed a novel, theory-based framework for data quality assurance and quality control of MRA. The objective of this work is to determine the potential impact of formalized MRA training and continuous quality control (QC) processes on data quality over time. METHODS: We conducted a retrospective analysis of QC data collected during a cross-sectional medical record review of mother-infant dyads with Neonatal Opioid Withdrawal Syndrome. A confidence interval approach was used to calculate crude (Wald\u27s method) and adjusted (generalized estimating equation) error rates over time. We calculated error rates using the number of errors divided by total fields ( all-field error rate) and populated fields ( populated-field error rate) as the denominators, to provide both an optimistic and a conservative measurement, respectively. RESULTS: On average, the ACT NOW CE Study maintained an error rate between 1% (optimistic) and 3% (conservative). Additionally, we observed a decrease of 0.51 percentage points with each additional QC Event conducted. CONCLUSIONS: Formalized MRA training and continuous QC resulted in lower error rates than have been found in previous literature and a decrease in error rates over time. This study newly demonstrates the importance of continuous process controls for MRA within the context of a multi-site clinical research study

A Method to Quantify Mouse Coat-Color Proportions

Coat-color proportions and patterns in mice are used as assays for many processes such as transgene expression, chimerism, and epigenetics. In many studies, coat-color readouts are estimated from subjective scoring of individual mice. Here we show a method by which mouse coat color is quantified as the proportion of coat shown in one or more digital images. We use the yellow-agouti mouse model of epigenetic variegation to demonstrate this method. We apply this method to live mice using a conventional digital camera for data collection. We use a raster graphics editing program to convert agouti regions of the coat to a standard, uniform, brown color and the yellow regions of the coat to a standard, uniform, yellow color. We use a second program to quantify the proportions of these standard colors. This method provides quantification that relates directly to the visual appearance of the live animal. It also provides an objective analysis with a traceable record, and it should allow for precise comparisons of mouse coats and mouse cohorts within and between studies

Selected topics in statistical discriminant analysis.

Includes bibliographical references (p. 110-114).This dissertation consists of three selected topics in statistical discriminant analysis: dimension reduction, regularization methods, and imputation methods. In Chapter 2 we first derive a new linear dimension-reduction method to determine a low-dimensional hyperplane that preserves or nearly preserves the separation of the individual populations and the Bayes probability of misclassification. Next, we derive a new low-dimensional representation-space approach for multiple high-dimensional multivariate normal populations. Third, we develop a linear dimension reduction method for quadratic discriminant analysis when the class population parameters must be estimated. Using a Monte Carlo simulation with several different parameter configurations, we compare our new methodology with two competing linear dimension-reduction procedures for statistical discrimination in terms of expected error rates. We find that under certain conditions, our new dimension-reduction method yields superior results for a majority of the configurations we consider. In addition, we determine that in several configurations, classification performance is actually enhanced by our new feature-reduction method when the sample size is sufficiently small relative to the original feature space dimension. In Chapter 3 we compare and contrast the efficacy of seven regularization methods for the quadratic discriminant function under a variety of parameter configurations. In particular, we use the expected error rate to assess the efficacy of these regularized quadratic discriminant functions. A two-parameter family of regularized class covariance-matrix estimators derived by Friedman (1989) yields superior classification results relative to its six competitors for the configurations, training-sample sizes, and original feature dimensions examined here. Finally, in Chapter 4 we consider the statistical classification problem for two multivariate normal populations with equal covariance matrices when the training samples contain observations missing at random. That is, we analyze the effect of missing-at-random data on Anderson's linear discriminant function. We use a Monte Carlo simulation to examine the expected probabilities of misclassification under several single and multiple imputation methods. The seven missing-data algorithms include: complete observation, mean substitution, expectation maximization, regression, predictive mean matching, propensity score, and MCMC. The regression, predictive mean, and propensity score multiple imputation approaches are, in general, superior to the other methods for the configurations and training-sample sizes we consider.by Songthip T. Ounpraseuth.Ph.D