Elimination of racial/ethnic Hepatitis B vaccination disparities in the US in the 2007--2008 National Health and Nutrition Examination Survey

Viral hepatitis is a significant public health problem worldwide and is due to viral infections that are classified as Hepatitis A, B, C, D, and E. Hepatitis B is one of the five known hepatic viruses. A safe and effective vaccine for Hepatitis B was first developed in 1981, and became adopted into national immunization programs targeting infants since 1990 and adolescents since 1995. In the U.S., this vaccination schedule has led to an 82% reduction in incidence from 8.5 cases per 100,000 in 1990 to 1.5 cases per 100,000 in 2007. Although there has been a decline in infection among adolescents, there is still a large burden of hepatitis B infection among adults and minorities. There is very little research in regards to vaccination gaps among adults. Using the National Health and Nutrition Examination Survey (NHANES) question "{Have you/Has SP (Study Participant)} ever received the 3-dose series of the hepatitis B vaccine?" the existence of racial/ethnic gaps using a cross-sectional study design was explored. In this study, other variables such as age, gender, socioeconomic variables (federal poverty line, educational attainment), and behavioral factors (sexual practices, self-report of men having sex with men, and intravenous drug use) were examined. We found that the current vaccination programs and policies for Hepatitis B had eliminated racial and ethnic disparities in Hepatitis B vaccination, but that a low coverage exists particularly for adults who engage in high risk behaviors. This study found a statistically significant 10% gap in Hepatitis B vaccination between those who have and those who do not have access to health insurance.^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

NMR structural analysis of cardiac troponin C: Monitoring conformational changes induced by binding calcium, troponin I, and calcium -sensitizing drugs

Contraction of cardiac muscle is regulated through the Ca2+ dependent protein-protein interactions of the troponin complex (Tn). The critical role cardiac troponin C (cTnC) plays as the Ca2+ receptor in this complex makes it an attractive target for positive inotropic compounds. In this study, the ten Met methyl groups in cTnC, [98% 13C ϵ]-Met cTnC, are used as structural markers to monitor conformational changes in cTnC and identify sites of interaction between cTnC and cardiac troponin I (cTnI) responsible for the Ca2+ dependent interactions. In addition the structural consequences that a number of Ca2+-sensitizing compounds have on free cTnC and the cTnC·cTnI complex were characterized. Using heteronuclear NMR experiments and monitoring chemical shift changes in the ten Met methyl 1H-13C correlations in 3Ca2+ cTnC when bound to cTnI revealed an anti-parallel arrangement for the two proteins such that the N-domain of cTnI interacts with the C-domain of cTnC. The large chemical shifts in Mets-81, -120, and -157 identified points of contact between the proteins that include the C-domain hydrophobic surface in cTnC and the A, B, and D helical interface located in the regulatory N-domain of cTnC. TnI association [cTnI(33–80), cTnI(86–211), or cTnI(33–211)] was found also to dramatically reduce flexibility in the D/E central linker of cTnC as monitored by line broadening in the Met 1H- 13C correlations of cTnC induced by a nitroxide spin label, MTSSL, covalently attached to cTnC at Cys 84. TnI association resulted in an extended cTnC that is unlike the compact structure observed for free cTnC. The Met 1H-13C correlations also allowed the binding characteristics of bepridil, TFP, levosimendan, and EMD 57033 to the apo, 2Ca2+, and Ca2+ saturated forms of cTnC to be determined. In addition, the location of drug binding on the 3Ca2+cTnC·cTnI complex was identified for bepridil and TFP. Use of a novel spin-labeled phenothiazine, and detection of isotope filtered NOEs, allowed identification of drug binding sites in the shallow hydrophobic cup in the C-terminal domain, and on two hydrophobic surfaces on N-regulatory domain in free 3Ca2+ cTnC. In contrast, only one N-domain drug binding site exists in 3Ca2+ cTnC·cTnI complex. The methyl groups of Met 45, 60 and 80, which are grouped in a hydrophobic patch near site II in cTnC, showed the greatest change upon titration with bepridil or TFP, suggesting that this is a critical site of drug binding in both free cTnC and when associated with cTnI. The strongest NOEs were seen for Met-60 and -80, which are located on helices C and D, respectively, of Ca2+ binding site II. These results support the conclusion that the small hydrophobic patch which includes Met-45, -60, and -80 constitutes a drug binding site, and that binding drugs to this site will lead to an increase in Ca2+ binding affinity of site II while preserving maximal cTnC activity. Thus, the subregion in cTnC makes a likely target against which to design new and selective Ca2+-sensitizing compounds. ^