HIV prevalence and contextual risk-factors among injection drug users in Harris County, Texas

Injection drug use is the third most frequent risk factor for new HIV infections in the United States. A dual mode of exposure: unsafe drug using practices and risky sexual behaviors underlies injection drug users' (IDUs) risk for HIV infection. This research study aims to characterize patterns of drug use and sexual behaviors and to examine the social contexts associated with risk behaviors among a sample of injection drug users. ^ This cross-sectional study includes 523 eligible injection drug users from Houston, Texas, recruited into the 2009 National HIV Behavioral Surveillance project. Three separate set of analyses were carried out. First, using latent class analysis (LCA) and maximum likelihood we identified classes of behavior describing levels of HIV risk, from nine drug and sexual behaviors. Second, eight separate multivariable regression models were built to examine the odds of reporting a given risk behavior. We constructed the most parsimonious multivariable model using a manual backward stepwise process. Third, we examined whether HIV serostatus knowledge (self-reported positive, negative, or unknown serostatus) is associated with drug use and sexual HIV risk behaviors. ^ Participants were mostly male, older, and non-Hispanic Black. Forty-two percent of our sample had behaviors putting them at high risk, 25% at moderate risk, and 33% at low risk for HIV infection. Individuals in the High-risk group had the highest probability of risky behaviors, categorized as almost always sharing needles (0.93), seldom using condoms (0.10), reporting recent exchange sex partners (0.90), and practicing anal sex (0.34). We observed that unsafe injecting practices were associated with high risk sexual behaviors. IDUs who shared needles had higher odds of having anal sex (OR=2.89, 95%CI: 1.69-4.92) and unprotected sex (OR=2.66, 95%CI: 1.38-5.10) at last sex. Additionally, homelessness was associated with needle sharing (OR=2.24, 95% CI: 1.34-3.76) and cocaine use was associated with multiple sex partners (OR=1.82, 95% CI: 1.07-3.11). Furthermore, twenty-one percent of the sample was unaware of their HIV serostatus. The three groups were not different from each other in terms of drug-use behaviors: always using a new sterile needle, or in sharing needles or drug preparation equipment. However, IDUs unaware of their HIV serostatus were 33% more likely to report having more than three sexual partners in the past 12 months; 45% more likely to report to have unprotected sex and 85% more likely to use drug and or alcohol during or before at last sex compared to HIV-positive IDUs. ^ This analysis underscores the merit of LCA approach to empirically categorize injection drug users into distinct classes and identify their risk pattern using multiple indicators and our results show considerable overlap of high risk sexual and drug use behaviors among the high-risk class members. The observed clustering pattern of drug and sexual risk behavior among this population confirms that injection drug users do not represent a homogeneous population in terms of HIV risk. These findings will help develop tailored prevention programs.^

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^

The response of Texas public health agencies and workers to the emergency preparedness initiatives after 9/11

The purpose of this research was to better understand the impact of the terrorist attacks in 2001 on public health, particularly for Texas public health. This study employed mixed methods to examine changes to public health culture within Texas local public health agencies, important attitudes of public health workers toward responding to a disaster, and the funding policies that might ensure our investment in public health emergency preparedness is protected. ^ A qualitative analysis of interviews conducted with a large sample of public health officials in Texas found that all the constituent parts of a peculiar culture for public health preparedness existed that spanned the state's local health departments regardless of size, or funding level. The new preparedness culture in Texas had the hallmarks necessary for a robust public health preparedness and emergency response system. ^ The willingness of public health workers, necessary to make these kinds of changes and mount a disaster response was examined in one of Texas' most experienced disaster response teams—the public health workers for the City of Houston. A hypothesized latent variable model showed that willingness mediated all other factors in the model (self-efficacy, knowledge, barriers, and risk perception) for self-reported likelihood of reporting to work for a disaster. The RMSEA for the final model was 0.042 with a confidence interval of 0.036—0.049 and the chi-squared difference test was P=0.08, indicating a well-fitted model that suggests willingness is an important factor for consideration by preparedness planners and researchers alike. ^ Finally, with disasters on the rise and federal funding for preparedness dwindling, a review of states' policies for the distribution of these funds and their advantages and disadvantages were examined through a review of current literature and public documents, and a survey of state-level public health officials, emergency management professionals and researchers. Although the base plus per-capita method is the most common, it is not necessarily perceived to be the most effective. No clear "optimal" method emerged from the study, but recommendations for a strategic combination of three methods were made that has the potential to maximize the benefits of each method, while minimizing the weaknesses.^

Metastatic lung cancer in a new mouse model inheriting p53 and latent K-ras mutations

Lung cancer is a devastating disease with very poor prognosis. The design of better treatments for patients would be greatly aided by mouse models that closely resemble the human disease. The most common type of human lung cancer is adenocarcinoma with frequent metastasis. Unfortunately, current models for this tumor are inadequate due to the absence of metastasis. Based on the molecular findings in human lung cancer and metastatic potential of osteosarcomas in mutant p53 mouse models, I hypothesized that mice with both K-ras and p53 missense mutations might develop metastatic lung adenocarcinomas. Therefore, I incorporated both K-rasLA1 and p53RI72HΔg alleles into mouse lung cells to establish a more faithful model for human lung adenocarcinoma and for translational and mechanistic studies. Mice with both mutations ( K-rasLA1/+ p53R172HΔg/+) developed advanced lung adenocarcinomas with similar histopathology to human tumors. These lung adenocarcinomas were highly aggressive and metastasized to multiple intrathoracic and extrathoracic sites in a pattern similar to that seen in lung cancer patients. This mouse model also showed gender differences in cancer related death and developed pleural mesotheliomas in 23.2% of them. In a preclinical study, the new drug Erlotinib (Tarceva) decreased the number and size of lung lesions in this model. These data demonstrate that this mouse model most closely mimics human metastatic lung adenocarcinoma and provides an invaluable system for translational studies. ^ To screen for important genes for metastasis, gene expression profiles of primary lung adenocarcinomas and metastases were analyzed. Microarray data showed that these two groups were segregated in gene expression and had 79 highly differentially expressed genes (more than 2.5 fold changes and p<0.001). Microarray data of Bub1b, Vimentin and CCAM1 were validated in tumors by quantitative real-time PCR (QPCR). Bub1b , a mitotic checkpoint gene, was overexpressed in metastases and this correlated with more chromosomal abnormalities in metastatic cells. Vimentin, a marker of epithelial-mesenchymal transition (EMT), was also highly expressed in metastases. Interestingly, Twist, a key EMT inducer, was also highly upregulated in metastases by QPCR, and this significantly correlated with the overexpression of Vimentin in the same tumors. These data suggest EMT occurs in lung adenocarcinomas and is a key mechanism for the development of metastasis in K-ras LA1/+ p53R172HΔg/+ mice. Thus, this mouse model provides a unique system to further probe the molecular basis of metastatic lung cancer.^

Involvement of heparin-like glycosaminoglycans and expression of a novel heparan sulfate binding protein (p24) during human placentation and in a model for human implantation

Previous studies in our laboratory have indicated that heparan sulfate proteoglycans (HSPGs) play an important role in murine embryo implantation. To investigate the potential function of HSPGs in human implantation, two human cell lines (RL95 and JAR) were selected to model uterine epithelium and embryonal trophectoderm, respectively. A heterologous cell-cell adhesion assay showed that initial binding between JAR and RL95 cells is mediated by cell surface glycosaminoglycans (GAG) with heparin-like properties, i.e., heparan sulfate and dermatan sulfate. Furthermore, a single class of highly specific, protease-sensitive heparin/heparan sulfate binding sites exist on the surface of RL95 cells. Three heparin binding, tryptic peptide fragments were isolated from RL95 cell surfaces and their amino termini partially sequenced. Reverse transcription-polymerase chain reaction (RT-PCR) generated 1 to 4 PCR products per tryptic peptide. Northern blot analysis of RNA from RL95 cells using one of these RT-PCR products identified a 1.2 Kb mRNA species (p24). The amino acid sequence predicted from the cDNA sequence contains a putative heparin-binding domain. A synthetic peptide representing this putative heparin binding domain was used to generate a rabbit polyclonal antibody (anti-p24). Indirect immunofluorescence studies on RL95 and JAR cells as well as binding studies of anti-p24 to intact RL95 cells demonstrate that p24 is distributed on the cell surface. Western blots of RL95 membrane preparations identify a 24 kDa protein (p24) highly enriched in the 100,000 g pellet plasma membrane-enriched fraction. p24 eluted from membranes with 0.8 M NaCl, but not 0.6 M NaCl, suggesting that it is a peripheral membrane component. Solubilized p24 binds heparin by heparin affinity chromatography and $\sp{125}$I-heparin binding assays. Furthermore, indirect immunofluorescence studies indicate that cytotrophoblast of floating and attached villi of the human fetal-maternal interface are recognized by anti-p24. The study also indicates that the HSPG, perlecan, accumulates where chorionic villi are attached to uterine stroma and where p24-expressing cytotrophoblast penetrate the stroma. Collectively, these data indicate that p24 is a cell surface membrane-associated heparin/heparan sulfate binding protein found in cytotrophoblast, but not many other cell types of the fetal-maternal interface. Furthermore, p24 colocalizes with HSPGs in regions of cytotrophoblast invasion. These observations are consistent with a role for HSPGs and HSPG binding proteins in human trophoblast-uterine cell interactions. ^

Characterization of recombinant class I MHC alloantigen action upon the survival of disparate heart allografts

Previous studies have led to the development of allochimeric class I MHC proteins as agents that effectively induce donor-specific transplantation tolerance in a rat system with or without additional immunosuppression. Within the α1-helical region of RT1.Au, an epitope that conferred immunologic tolerance was discovered. Studies presented herein were designed to test our central hypothesis that allochimeric proteins onfer tolerance in a mouse model. To test this hypothesis, portal vein (PV) injection of wild-type H2Kd and H2Dd proteins were produced in a bacterial expression system and found to specifically prolong the survival of BALB/c (H2d) heart allografts in C57BL/10 (H2b) recipients. Although a single PV injection of 50 μg α1–α 3 H2Kd alone was ineffective, 50 μg α1 –α3 alone slightly prolonged BALB/c heart allograft survivals. In contrast, the combination of 25 μg α1–α 3 H2Kd and 25 μg α1–α 3 H2Dd proteins prolonged BALB/c graft survivals to 20.2 ± 6.4 days (p < 0.004). The effect was donor-specific, since a combination of 25 μg α1–α3 H2Kd and 25 μg α1–α3 H2Dd proteins failed to affect survivals of third-party C3H (H2k k) heart allografts, namely 9.0 ± 0.0 days in treated versus 7.8 ± 0.5 days in untreated hosts. Thus, the combination of two H2K d and H2Dd proteins is more effective in prolonging allograft survival than a single protein produced in a bacterial expression system. A single PV injection (day 0) of 25 μg α1–α 2 H2Kd and 25 μg α1–α 2 H2Dd proteins to C57BL/10 mice prolonged the survival of BALB/c heart allografts to 22.4 ± 4.5 days. Within a WF to ACI rat heart allograft system, a single PV injection of 20 μg 70–77 u-RT1.Aa induced specific tolerance of allografts. This therapy could be combined with CsA to induce transplantation tolerance. However, combination of 70–77u-RT1.Aa with CTLA4Ig, rapamycin, or AG-490 effectively blocked the induction of transplantation tolerance. Tolerance generated by allochimeric protein could be adoptively transferred to naive recipients. Intragraft cytokine mRNA levels showed a bias towards a Th2-type phenotype. Additionally, studies of cytokine signaling and activation of transcription factors revealed a requirement that these pathways remain available for signaling in order for transplantation tolerance to occur. These studies suggest that the generation of regulatory cells are required for the induction of transplantation tolerance through the use of allochimeric proteins. ^

Adherence to a lifestyle physical activity program: Trends over time and associations with processes of change and physical activity

Research on lifestyle physical activity interventions suggests that they help individuals meet the new recommendations for physical activity made by the Centers for Disease Control and Prevention (CDC) and the American College of Sports Medicine (ACSM). The purpose of this research was to describe the rates of adherence to two lifestyle physical activity intervention arms and to examine the association between adherence and outcome variables, using data from Project PRIME, a lifestyle physical activity intervention based on the transtheoretical model and conducted by the Cooper Institute of Aerobics Research, Dallas, Texas. Participants were 250 sedentary healthy adults, aged 35 to 70 years, primarily non-Hispanic White, and in the contemplation and preparation stages of readiness to change. They were randomized to a group (PRIME G) or a mail- and telephone-delivered condition (PRIME C). Adherence measures included attending class (PRIME G), completing a monthly telephone call with a health educator (PRIME C), and completing homework assignments and self-monitoring minutes of moderate- to vigorous physical activity (both groups). In the first results paper, adherence over time and between conditions was examined: Attendance in group, completing the monthly telephone call, and homework completion decreased over time, and participants in PRIME G were more likely to complete homework than those in PRIME C. Paper 2 aimed to determine whether the adherence measures predicted achievement of the CDC/ACSM physical activity guideline. In separate models for the two conditions, a latent variable measuring adherence was found to predict achievement of the guideline. Paper 3 examined the association between adherence measures and the transtheoretical model's processes of change within each condition. For both, participants who completed at least two thirds of the homework assignments improved their use of the processes of change more than those who completed less than that amount. These results suggest that encouraging adherence to a lifestyle physical activity intervention, at least among already motivated volunteers, may increase the likelihood of beneficial changes in the outcomes. ^

Effect of thiazolidinedione, a class of anti-diabetic drugs, on the mouse skin tumorigenesis

Thiazolidinediones (TZDs), a novel class of anti-diabetic drugs, have been known as ligands of peroxisome proliferator-activated receptor γ (PPARγ), a transcription factor that belongs to the nuclear receptor superfamily. These synthetic compounds improve insulin sensitivity in patients with type II diabetes likely through activating PAPRγ. Interestingly, they were also shown to inhibit cell growth and proliferation in a wide variety of tumor cell lines. The aim of this study is to assess the potential use of TZDs in the prevention of carcinogenesis using mouse skin as a model. ^ We found that troglitazone, one of TZD drugs, strongly inhibited cultured mouse skin keratinocyte proliferation as demonstrated by [3H]thymidine incorporation assay. It also induced a cell cycle G1 phase arrest and inhibited expression of cell cycle proteins, including cyclin D1, cdk2 and cdk4. Further experiments showed that PPARγ expression in keratinocytes was surprisingly undetectable in vitro or in vivo. Consistent with this, no endogenous PPARγ function in keratinocytes was found, suggesting that the inhibition of troglitazone on keratinocyte proliferation and cell cycle was PPARγ-independent. We further found that troglitazone inhibited insulin/insulin growth factor I (IGF-1) mitogenic signaling, which may explains, at least partly, its inhibitory effect on keratinocyte proliferation. We showed that troglitazone rapidly inhibited IGF-1 induced phosphorylation of p70S6K by mammalian target of rapamycin (mTOR). However, troglitazone did not directly inhibit mTOR kinase activity as shown by in vitro kinase assay. The inhibition of p70S6K is likely to be the result of strong activation of AMP activated protein kinase (AMPK) by TZDs. Stable expression of a dominant negative AMPK in keratinocytes blocked the inhibitory effect of troglitazone on IGF-1 induced phosphorylation of p70S6K. ^ Finally, we found that dietary TZDs inhibited by up to 73% mouse skin tumor development promoted by elevated IGF-1 signaling in BK5-IGF-1 transgenic mice, while they had no or little effect on skin tumor development promoted by 12-O-tetradecanoylphorbol-13-acetate (TPA) or ultraviolet (UV). Since IGF-1 signaling is frequently found to be elevated in patients with insulin resistance and in many human tumors, our data suggest that TZDs may provide tumor preventive benefit particularly to these patients. ^

Comparing the factorial structure, invariance, and predictive validity of transtheoretical model constructs for alcohol use across restricted and unrestricted settings

With substance abuse treatment expanding in prisons and jails, understanding how behavior change interacts with a restricted setting becomes more essential. The Transtheoretical Model (TTM) has been used to understand intentional behavior change in unrestricted settings, however, evidence indicates restrictive settings can affect the measurement and structure of the TTM constructs. The present study examined data from problem drinkers at baseline and end-of-treatment from three studies: (1) Project CARE (n = 187) recruited inmates from a large county jail; (2) Project Check-In (n = 116) recruited inmates from a state prison; (3) Project MATCH, a large multi-site alcohol study had two recruitment arms, aftercare (n = 724 pre-treatment and 650 post-treatment) and outpatient (n = 912 pre-treatment and 844 post-treatment). The analyses were conducted using cross-sectional data to test for non-invariance of measures of the TTM constructs: readiness, confidence, temptation, and processes of change (Structural Equation Modeling, SEM) across restricted and unrestricted settings. Two restricted (jail and aftercare) and one unrestricted group (outpatient) entering treatment and one restricted (prison) and two unrestricted groups (aftercare and outpatient) at end-of-treatment were contrasted. In addition TTM end-of-treatment profiles were tested as predictors of 12 month drinking outcomes (Profile Analysis). Although SEM did not indicate structural differences in the overall TTM construct model across setting types, there were factor structure differences on the confidence and temptation constructs at pre-treatment and in the factor structure of the behavioral processes at the end-of-treatment. For pre-treatment temptation and confidence, differences were found in the social situations factor loadings and in the variance for the confidence and temptation latent factors. For the end-of-treatment behavioral processes, differences across the restricted and unrestricted settings were identified in the counter-conditioning and stimulus control factor loadings. The TTM end-of-treatment profiles were not predictive of drinking outcomes in the prison sample. Both pre and post-treatment differences in structure across setting types involved constructs operationalized with behaviors that are limited for those in restricted settings. These studies suggest the TTM is a viable model for explicating addictive behavior change in restricted settings but calls for modification of subscale items that refer to specific behaviors and caution in interpreting the mean differences across setting types for problem drinkers. ^

The application of latent variable models to the assessment of determinants of HIV risk behavior

Studies on the relationship between psychosocial determinants and HIV risk behaviors have produced little evidence to support hypotheses based on theoretical relationships. One limitation inherent in many articles in the literature is the method of measurement of the determinants and the analytic approach selected. ^ To reduce the misclassification associated with unit scaling of measures specific to internalized homonegativity, I evaluated the psychometric properties of the Reactions to Homosexuality scale in a confirmatory factor analytic framework. In addition, I assessed the measurement invariance of the scale across racial/ethnic classifications in a sample of men who have sex with men. The resulting measure contained eight items loading on three first-order factors. Invariance assessment identified metric and partial strong invariance between racial/ethnic groups in the sample. ^ Application of the updated measure to a structural model allowed for the exploration of direct and indirect effects of internalized homonegativity on unprotected anal intercourse. Pathways identified in the model show that drug and alcohol use at last sexual encounter, the number of sexual partners in the previous three months and sexual compulsivity all contribute directly to risk behavior. Internalized homonegativity reduced the likelihood of exposure to drugs, alcohol or higher numbers of partners. For men who developed compulsive sexual behavior as a coping strategy for internalized homonegativity, there was an increase in the prevalence odds of risk behavior. ^ In the final stage of the analysis, I conducted a latent profile analysis of the items in the updated Reactions to Homosexuality scale. This analysis identified five distinct profiles, which suggested that the construct was not homogeneous in samples of men who have sex with men. Lack of prior consideration of these distinct manifestations of internalized homonegativity may have contributed to the analytic difficulty in identifying a relationship between the trait and high-risk sexual practices. ^

Slope estimation in a parametric measurement error model: A simulation study

In regression analysis, covariate measurement error occurs in many applications. The error-prone covariates are often referred to as latent variables. In this proposed study, we extended the study of Chan et al. (2008) on recovering latent slope in a simple regression model to that in a multiple regression model. We presented an approach that applied the Monte Carlo method in the Bayesian framework to the parametric regression model with the measurement error in an explanatory variable. The proposed estimator applied the conditional expectation of latent slope given the observed outcome and surrogate variables in the multiple regression models. A simulation study was presented showing that the method produces estimator that is efficient in the multiple regression model, especially when the measurement error variance of surrogate variable is large.^

THE CYTOPLASMIC TAIL OF MHC CLASS I MOLECULES PLAYS A CRITICAL ROLE IN DENDRITIC CELL-INDUCED T CELL IMMUNITY

The presentation of MHC class I (MHC-I)/peptide complexes by dendritic cells (DCs) is critical for the maintenance of central tolerance to self and for the regulation of cytotoxic T lymphocytes (CTL)-mediated adaptive immune responses against pathogens and cancer cells. Interestingly, several findings have suggested that the cytoplasmic tail of MHC class I plays a functional role in the regulation of CTL immune responses. For example, our previous studies demonstrated that exon 7-deleted MHC-I molecules not only showed extended DC cell surface half-lives but also induced significantly increased CTL responses to viral challange invivo. Although exon 7-deleted variant of MHC-I does not occur naturally in humans, the animal studies prompted us to examine whether exon 7-deleted MHC-I molecules could generate augmented CTL responses in a therapeutic DC-based vaccine setting. To examine the stimulatory capacity of exon 7-deleted MHC-I molecules, we generated a lentivirus-mediated gene transfer system to induce the expression of different MHC-I cytoplasmic tail isoforms in both mouse and human DCs. These DCs were then used as vaccines in a melanoma mouse tumor model and in a human invitro co-culture system. In this thesis, we show that DCs expressing exon 7-deleted MHC-I molecules, stimulated remarkably higher levels of T-cell cytokine production and significantly increased the proliferation of meanoma-specific (Pmel-1) T cells compared with DCs expressing wild type MHC-I. We also demonstrate that, in combination with adoptive transfer of Pmel-1 T-cell, DCs expressing exon 7-deleted Db molecules induced greater anti-tumor responses against established B16 melanoma tumors, significantly extending mouse survival as compared to DCs expressing wild-type Db molecules. Moreover, we also observed that human DCs expressing exon 7-deleted HLA-A2 molecules showed similarly augmented CTL stimulatory ability. Mechanistic studies suggest that exon 7-deleted MHC-I molecules showed impaired lateral membrane movement and extended cell surface half-lives within the DC/T-cell interface, leading to increased spatial availability of MHC-I/peptide complexes for recognition by CD8+ T cells. Collectively, these results suggesr that targeting exon 7 within the cytoplasmic tail of MHC-I molecules in DC vaccines has the potential to enhance CD8+ T cell stimulatory capacity and improve clinical outcomes in patients with cancer or viral infections.

Mistreatment and self-neglect in community dwelling older adults: Studies of construct validity, measurement invariance and latent classes

Mistreatment and self-neglect significantly increase the risk of dying in older adults. It is estimated that 1 to 2 million older adults experience elder mistreatment and self-neglect every year in the United States. Currently, there are no elder mistreatment and self-neglect assessment tools with construct validity and measurement invariance testing and no studies have sought to identify underlying latent classes of elder self-neglect that may have differential mortality rates. Using data from 11,280 adults with Texas APS substantiated elder mistreatment and self-neglect 3 studies were conducted to: (1) test the construct validity and (2) the measurement invariance across gender and ethnicity of the Texas Adult Protective Services (APS) Client Assessment and Risk Evaluation (CARE) tool and (3) identify latent classes associated with elder self-neglect. Study 1 confirmed the construct validity of the CARE tool following adjustments to the initial hypothesized CARE tool. This resulted in the deletion of 14 assessment items and a final assessment with 5 original factors and 43 items. Cross-validation for this model was achieved. Study 2 provided empirical evidence for factor loading and item-threshold invariance of the CARE tool across gender and between African-Americans and Caucasians. The financial status domain of the CARE tool did not function properly for Hispanics and thus, had to be deleted. Subsequent analyses showed factor loading and item-threshold invariance across all 3 ethnic groups with the exception of some residual errors. Study 3 identified 4-latent classes associated with elder self-neglect behaviors which included individuals with evidence of problems in the areas of (1) their environment, (2) physical and medical status, (3) multiple domains and (4) finances. Overall, these studies provide evidence supporting the use of APS CARE tool for providing unbiased and valid investigations of mistreatment and neglect in older adults with different demographic characteristics. Furthermore, the findings support the underlying notion that elder self-neglect may not only occur along a continuum, but that differential types may exist. All of which, have very important potential implications for social and health services distributed to vulnerable mistreated and neglected older adults.^

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.