Use of empowerment evaluation to determine a sustainability plan for a community-based preconception care program

Women With IMPACT (WWI) is a community-based preconception care educational intervention. WWI is being implemented by the Impacting Maternal and Prenatal Care Together (IMPACT) Collaborative and targets zip codes in Harris County, Texas at high risk for infant mortality, low birthweight, and preterm birth. WWI started March 2012 and continues through August 2013. Three workshop series are planned. This study was conducted with participants and facilitators from the first workshop series. This study aimed to 1) evaluate the WWI program using empowerment evaluation, 2) engage all WWI stakeholders in an empowerment evaluation so the method could be adopted as a participatory evaluation process for future IMPACT activities, and 3) develop recommendations for sustainability of the WWI intervention, based on empowerment evaluation findings and results from the pre/post program evaluation completed by WWI participants. Study participants included WWI participants and facilitators and IMPACT Collaborative Steering Committee members. WWI participants were female, 18-35 year-old, non-pregnant residents of zip codes at high risk of adverse birth outcomes. All other study participants were 18 years or older. A two-phased empowerment evaluation (EE) was utilized in this study. Sessions 1-4 were conducted independently of one another – 3 with participants at different sites and one with the facilitators. The fifth session included WWI participant and facilitator representatives, and IMPACT Steering Committee members. Session 5 built upon the work of the other sessions. Observation notes were recorded during each session. Thematic content analysis was conducted on all EE tables and observation notes. Mission statements drafted by each group focused on improvement of physical and mental health through behavior change and empowerment of all participants. The top 5 overall program components were: physical activity, nutrition, self-worth, in-class communication, and stress. Goals for program improvement were set by EE participants for each of these components. Through thematic content analysis of the tables and observation notes, social support emerged as an important theme of the program among all participant groups. Change to a healthy lifestyle emerged as an important theme in terms of program improvement. Two-phased EE provided an opportunity for all program stakeholders to provide feedback regarding important program components and provide suggestions for program improvement. EE, thematic content analysis, pre/post evaluation results, and inherent program knowledge were triangulated to make recommendations to sustain the program once the initial funding ends. ^

Molecular analysis of TNF sensitivity in normal and Ha-{\it ras\/} transformed murine fibroblasts

Tumor necrosis factor (TNF) is known to have antiproliferative effects on a wide variety of tumor cells but proliferative effects on normal cells. However, the molecular basis for such differences in the action of TNF are unknown. The overall objectives of my research are to investigate the role of oncogenes in TNF sensitivity and delineate some of the molecular mechanisms involved in TNF sensitivity and resistance. To accomplish these objectives, I transfected TNF-resistant C3H mouse embryo fibroblasts (10T1/2) with an activated Ha-ras oncogene and determined whether these cells exhibit altered sensitivity to TNF. The results indicated that 10T1/2 cells transfected with an activated Ha-ras oncogene (10T-EJ) not only produced tumors in nude mice but also exhibited extreme sensitivity to cytolysis by TNF. In contrast, 10T1/2 cells transfected with the pSV2-neo gene alone were resistant to the cytotoxic effects of TNF. I also found that TNF-induced cell death was mediated through apoptosis. The differential sensitivity of 10T1/2 and 10T-EJ cell lines to TNF was not due to differences in the number of TNF receptors on their cell surface. In addition, TNF-resistant revertants isolated from Ha-ras-transformed, TNF-sensitive cells still expressed the same amount of p21 as TNF-sensitive cells and were still tumorigenic, suggesting that Ha-ras-induced transformation and TNF sensitivity may follow different pathways. Interestingly, TNF-resistant but not sensitive cells expressed higher levels of bcl-2, c-myc, and manganese superoxide dismutase (MnSOD) mRNA following exposure to TNF. However, TNF treatment resulted in a marginal induction of p53 mRNA in both TNF-sensitive and resistant cells. Based on these results I can conclude that (i) Ha-ras oncogene induces both transformation and TNF sensitivity, (ii) TNF-induced cytotoxicity involves apoptosis, and (iii) TNF-induced upregulation of bcl-2, c-myc, and MnSOD genes is associated with TNF resistance in C3H mouse embryo fibroblasts. ^

The information bottleneck method for genome-wide association studies

In population studies, most current methods focus on identifying one outcome-related SNP at a time by testing for differences of genotype frequencies between disease and healthy groups or among different population groups. However, testing a great number of SNPs simultaneously has a problem of multiple testing and will give false-positive results. Although, this problem can be effectively dealt with through several approaches such as Bonferroni correction, permutation testing and false discovery rates, patterns of the joint effects by several genes, each with weak effect, might not be able to be determined. With the availability of high-throughput genotyping technology, searching for multiple scattered SNPs over the whole genome and modeling their joint effect on the target variable has become possible. Exhaustive search of all SNP subsets is computationally infeasible for millions of SNPs in a genome-wide study. Several effective feature selection methods combined with classification functions have been proposed to search for an optimal SNP subset among big data sets where the number of feature SNPs far exceeds the number of observations. ^ In this study, we take two steps to achieve the goal. First we selected 1000 SNPs through an effective filter method and then we performed a feature selection wrapped around a classifier to identify an optimal SNP subset for predicting disease. And also we developed a novel classification method-sequential information bottleneck method wrapped inside different search algorithms to identify an optimal subset of SNPs for classifying the outcome variable. This new method was compared with the classical linear discriminant analysis in terms of classification performance. Finally, we performed chi-square test to look at the relationship between each SNP and disease from another point of view. ^ In general, our results show that filtering features using harmononic mean of sensitivity and specificity(HMSS) through linear discriminant analysis (LDA) is better than using LDA training accuracy or mutual information in our study. Our results also demonstrate that exhaustive search of a small subset with one SNP, two SNPs or 3 SNP subset based on best 100 composite 2-SNPs can find an optimal subset and further inclusion of more SNPs through heuristic algorithm doesn't always increase the performance of SNP subsets. Although sequential forward floating selection can be applied to prevent from the nesting effect of forward selection, it does not always out-perform the latter due to overfitting from observing more complex subset states. ^ Our results also indicate that HMSS as a criterion to evaluate the classification ability of a function can be used in imbalanced data without modifying the original dataset as against classification accuracy. Our four studies suggest that Sequential Information Bottleneck(sIB), a new unsupervised technique, can be adopted to predict the outcome and its ability to detect the target status is superior to the traditional LDA in the study. ^ From our results we can see that the best test probability-HMSS for predicting CVD, stroke,CAD and psoriasis through sIB is 0.59406, 0.641815, 0.645315 and 0.678658, respectively. In terms of group prediction accuracy, the highest test accuracy of sIB for diagnosing a normal status among controls can reach 0.708999, 0.863216, 0.639918 and 0.850275 respectively in the four studies if the test accuracy among cases is required to be not less than 0.4. On the other hand, the highest test accuracy of sIB for diagnosing a disease among cases can reach 0.748644, 0.789916, 0.705701 and 0.749436 respectively in the four studies if the test accuracy among controls is required to be at least 0.4. ^ A further genome-wide association study through Chi square test shows that there are no significant SNPs detected at the cut-off level 9.09451E-08 in the Framingham heart study of CVD. Study results in WTCCC can only detect two significant SNPs that are associated with CAD. In the genome-wide study of psoriasis most of top 20 SNP markers with impressive classification accuracy are also significantly associated with the disease through chi-square test at the cut-off value 1.11E-07. ^ Although our classification methods can achieve high accuracy in the study, complete descriptions of those classification results(95% confidence interval or statistical test of differences) require more cost-effective methods or efficient computing system, both of which can't be accomplished currently in our genome-wide study. We should also note that the purpose of this study is to identify subsets of SNPs with high prediction ability and those SNPs with good discriminant power are not necessary to be causal markers for the disease.^

Genome-wide gene-gene interaction analysis for cardiovascular disease

Numerous studies have been carried out to try to better understand the genetic predisposition for cardiovascular disease. Although it is widely believed that multifactorial diseases such as cardiovascular disease is the result from effects of many genes which working alone or interact with other genes, most genetic studies have been focused on identifying of cardiovascular disease susceptibility genes and usually ignore the effects of gene-gene interactions in the analysis. The current study applies a novel linkage disequilibrium based statistic for testing interactions between two linked loci using data from a genome-wide study of cardiovascular disease. A total of 53,394 single nucleotide polymorphisms (SNPs) are tested for pair-wise interactions, and 8,644 interactions are found to be significant with p-values less than 3.5×10-11. Results indicate that known cardiovascular disease susceptibility genes tend not to have many significantly interactions. One SNP in the CACNG1 (calcium channel, voltage-dependent, gamma subunit 1) gene and one SNP in the IL3RA (interleukin 3 receptor, alpha) gene are found to have the most significant pair-wise interactions. Findings from the current study should be replicated in other independent cohort to eliminate potential false positive results.^

A review of economic analysis in the vaccination program; decision making and governance: Adivisory Committee on Immunization Practices (ACIP)

The Advisory Committee on Immunization Practices (ACIP) develops written recommendations for the routine administration of vaccines to children and adults in the U.S. civilian population. The ACIP is the only entity in the federal government that makes such recommendations. ACIP elaborates on selection of its members and rules out concerns regarding its integrity, but fails to provide information about the importance of economic analysis in vaccine selection. ACIP recommendations can have large health and economic consequences. Emphasis on economic evaluation in health is a likely response to severe pressures of the federal and state health budget. This study describes the economic aspects considered by the ACIP while sanctioning a vaccine, and reviews the economic evaluations (our economic data) provided for vaccine deliberations. A five year study period from 2004 to 2009 is adopted. Publicly available data from ACIP web database is used. Drummond et al. (2005) checklist serves as a guide to assess the quality of economic evaluations presented. Drummond et al.'s checklist is a comprehensive hence it is unrealistic to expect every ACIP deliberation to meet all of their criteria. For practical purposes we have selected seven criteria that we judge to be significant criteria provided by Drummond et al. Twenty-four data points were obtained in a five year period. Our results show that out of the total twenty-four data point‘s (economic evaluations) only five data points received a score of six; that is six items on the list of seven were met. None of the data points received a perfect score of seven. Seven of the twenty-four data points received a score of five. A minimum of a two score was received by only one of the economic analyses. The type of economic evaluation along with the model criteria and ICER/QALY criteria met at 0.875 (87.5%). These three criteria were met at the highest rate among the seven criteria studied. Our study findings demonstrate that the perspective criteria met at 0.583 (58.3%) followed by source and sensitivity analysis criteria both tied at 0.541 (54.1%). The discount factor was met at 0.250 (25.0%).^ Economic analysis is not a novel concept to the ACIP. It has been practiced and presented at these meetings on a regular basis for more than five years. ACIP‘s stated goal is to utilize good quality epidemiologic, clinical and economic analyses to help policy makers choose among alternatives presented and thus achieve a better informed decision. As seen in our study the economic analyses over the years are inconsistent. The large variability coupled with lack of a standardized format may compromise the utility of the economic information for decision-making. While making recommendations, the ACIP takes into account all available information about a vaccine. Thus it is vital that standardized high quality economic information is provided at the ACIP meetings. Our study may provide a call for the ACIP to further investigate deficiencies within the system and thereby to improve economic evaluation data presented. ^

Bayesian and survival model for tumor relapse status and disease-specific survival, with applications to breast cancer

Breast cancer is the most common non-skin cancer and the second leading cause of cancer-related death in women in the United States. Studies on ipsilateral breast tumor relapse (IBTR) status and disease-specific survival will help guide clinic treatment and predict patient prognosis.^ After breast conservation therapy, patients with breast cancer may experience breast tumor relapse. This relapse is classified into two distinct types: true local recurrence (TR) and new ipsilateral primary tumor (NP). However, the methods used to classify the relapse types are imperfect and are prone to misclassification. In addition, some observed survival data (e.g., time to relapse and time from relapse to death)are strongly correlated with relapse types. The first part of this dissertation presents a Bayesian approach to (1) modeling the potentially misclassified relapse status and the correlated survival information, (2) estimating the sensitivity and specificity of the diagnostic methods, and (3) quantify the covariate effects on event probabilities. A shared frailty was used to account for the within-subject correlation between survival times. The inference was conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in softwareWinBUGS. Simulation was used to validate the Bayesian method and assess its frequentist properties. The new model has two important innovations: (1) it utilizes the additional survival times correlated with the relapse status to improve the parameter estimation, and (2) it provides tools to address the correlation between the two diagnostic methods conditional to the true relapse types.^ Prediction of patients at highest risk for IBTR after local excision of ductal carcinoma in situ (DCIS) remains a clinical concern. The goals of the second part of this dissertation were to evaluate a published nomogram from Memorial Sloan-Kettering Cancer Center, to determine the risk of IBTR in patients with DCIS treated with local excision, and to determine whether there is a subset of patients at low risk of IBTR. Patients who had undergone local excision from 1990 through 2007 at MD Anderson Cancer Center with a final diagnosis of DCIS (n=794) were included in this part. Clinicopathologic factors and the performance of the Memorial Sloan-Kettering Cancer Center nomogram for prediction of IBTR were assessed for 734 patients with complete data. Nomogram for prediction of 5- and 10-year IBTR probabilities were found to demonstrate imperfect calibration and discrimination, with an area under the receiver operating characteristic curve of .63 and a concordance index of .63. In conclusion, predictive models for IBTR in DCIS patients treated with local excision are imperfect. Our current ability to accurately predict recurrence based on clinical parameters is limited.^ The American Joint Committee on Cancer (AJCC) staging of breast cancer is widely used to determine prognosis, yet survival within each AJCC stage shows wide variation and remains unpredictable. For the third part of this dissertation, biologic markers were hypothesized to be responsible for some of this variation, and the addition of biologic markers to current AJCC staging were examined for possibly provide improved prognostication. The initial cohort included patients treated with surgery as first intervention at MDACC from 1997 to 2006. Cox proportional hazards models were used to create prognostic scoring systems. AJCC pathologic staging parameters and biologic tumor markers were investigated to devise the scoring systems. Surveillance Epidemiology and End Results (SEER) data was used as the external cohort to validate the scoring systems. Binary indicators for pathologic stage (PS), estrogen receptor status (E), and tumor grade (G) were summed to create PS+EG scoring systems devised to predict 5-year patient outcomes. These scoring systems facilitated separation of the study population into more refined subgroups than the current AJCC staging system. The ability of the PS+EG score to stratify outcomes was confirmed in both internal and external validation cohorts. The current study proposes and validates a new staging system by incorporating tumor grade and ER status into current AJCC staging. We recommend that biologic markers be incorporating into revised versions of the AJCC staging system for patients receiving surgery as the first intervention.^ Chapter 1 focuses on developing a Bayesian method to solve misclassified relapse status and application to breast cancer data. Chapter 2 focuses on evaluation of a breast cancer nomogram for predicting risk of IBTR in patients with DCIS after local excision gives the statement of the problem in the clinical research. Chapter 3 focuses on validation of a novel staging system for disease-specific survival in patients with breast cancer treated with surgery as the first intervention. ^

Genetic underpinnings of variation in brain ventricular volume: A genome-wide association study

The ventricular system is a critical component of the central nervous system (CNS) that is formed early in the developmental stages and remains functional through the lifetime. Changes in the ventricular system can be easily discerned via neuroimaging procedures and most of the time it reflects changes in the physiology of the CNS. In this study we attempted to identify specific genes associated with variation in ventricular volume in humans. Methods. We conducted a genome wide association (GWA) analysis of the volume of the lateral ventricles among 1605 individuals of European ancestry from two community based cohorts, the Genetics of Microangiopathic Brain Injury (GMBI; N=814) and Atherosclerosis Risk in Communities (ARIC; N=791). Significant findings from the analysis were tested for replication in both the cohorts and then meta-analyzed to get an estimate of overall significance. Results. In our GWA analyses, no single nucleotide polymorphism (SNP) reached a genome-wide significance of p<10−8. There were 25 SNPs in GMBI and 9 SNPs in ARIC that reached a threshold of p<10 −5. However, none of the top SNPs from each cohort were replicated in the other. In the meta-analysis, no SNP reached the genome-wide threshold of 5×10−8, but we identified five novel SNPs associated with variation in ventricular volume at the p<10 −5 level. Strongest association was for rs2112536 in an intergenic region on chromosome 5q33 (Pmeta= 8.46×10−7 ). The remaining four SNPs were located on chromosome 3q23 encompassing the gene for Calsyntenin-2 (CLSTN2). The SNPs with strongest association in this region were rs17338555 (Pmeta= 5.28×10 −6), rs9812091 (Pmeta= 5.89×10−6 ), rs9812283 (Pmeta= 5.97×10−6) and rs9833213 (Pmeta= 6.96×10−6). Conclusions. This GWA study of ventricular volumes in the community-based cohorts of European descent identifies potential locus on chromosomes 3 and 5. Further characterization of these loci may provide insights into pathophysiology of ventricular involvement in various neurological diseases.^

Nonlinear dimension reduction in pathway-based genome-wide association analysis

Pathway based genome wide association study evolves from pathway analysis for microarray gene expression and is under rapid development as a complementary for single-SNP based genome wide association study. However, it faces new challenges, such as the summarization of SNP statistics to pathway statistics. The current study applies the ridge regularized Kernel Sliced Inverse Regression (KSIR) to achieve dimension reduction and compared this method to the other two widely used methods, the minimal-p-value (minP) approach of assigning the best test statistics of all SNPs in each pathway as the statistics of the pathway and the principal component analysis (PCA) method of utilizing PCA to calculate the principal components of each pathway. Comparison of the three methods using simulated datasets consisting of 500 cases, 500 controls and100 SNPs demonstrated that KSIR method outperformed the other two methods in terms of causal pathway ranking and the statistical power. PCA method showed similar performance as the minP method. KSIR method also showed a better performance over the other two methods in analyzing a real dataset, the WTCCC Ulcerative Colitis dataset consisting of 1762 cases, 3773 controls as the discovery cohort and 591 cases, 1639 controls as the replication cohort. Several immune and non-immune pathways relevant to ulcerative colitis were identified by these methods. Results from the current study provided a reference for further methodology development and identified novel pathways that may be of importance to the development of ulcerative colitis.^

Network-based analysis of genome-wide scans of natural selection

The genomic era brought by recent advances in the next-generation sequencing technology makes the genome-wide scans of natural selection a reality. Currently, almost all the statistical tests and analytical methods for identifying genes under selection was performed on the individual gene basis. Although these methods have the power of identifying gene subject to strong selection, they have limited power in discovering genes targeted by moderate or weak selection forces, which are crucial for understanding the molecular mechanisms of complex phenotypes and diseases. Recent availability and rapid completeness of many gene network and protein-protein interaction databases accompanying the genomic era open the avenues of exploring the possibility of enhancing the power of discovering genes under natural selection. The aim of the thesis is to explore and develop normal mixture model based methods for leveraging gene network information to enhance the power of natural selection target gene discovery. The results show that the developed statistical method, which combines the posterior log odds of the standard normal mixture model and the Guilt-By-Association score of the gene network in a naïve Bayes framework, has the power to discover moderate/weak selection gene which bridges the genes under strong selection and it helps our understanding the biology under complex diseases and related natural selection phenotypes.^

Genome -wide and locus -specific instability

The discovery of expanded simple repeated sequences causing or associated with human disease has lead to a new area of research involved in the elucidation of how the expanded repeat causes disease and how the repeat becomes unstable. ^ To study the genetic basis of the (CTG)n repeat instability in the DMPK gene in myotonic dystrophy (DM1) patients, somatic cell hybrids were constructed between the lymphocytes of DM1 patients and a variety of Chinese hamster ovary (CHO) cell DNA repair gene deficient mutants. By using small pool PCR (SP-PCR), the instability of the (CTG)n can be quantitated for both the frequency and sizes of length change mutations. ^ Additional SP-PCR analysis on 2/11 subclones generated from this original hybrid showed a marked increase in large repeat deletions, ∼50%. A bimodal distribution of repeats was seen around the progenitor allele and at a large deleted product (within the normal range) with no intermediate products present. ^ To determine if the repair capacity of the CHO cell led to a mutator phenotype in the hamster and hybrid clones, SP-PCR was also done on 3 hamster microsatellites in a variety of hamster cell backgrounds. No variant alleles were seen in over 2500 genome equivalents screened. ^ Human-hamster hybrids have long been shown to be chromosomally unstable, yet information about the stability of repeated sequences was not known. To test if repeat instability was associated with either intact or non-intact human chromosomes, more than 300 microsatellite repeats on 13 human chromosomes (intact and non-intact) were analyzed in eight hybrid cells. No variants were seen between the hybrid and patient alleles in the hybrids. ^ To identify whether DM1 patients have a previously undetected level of genome wide instability or if the instability is truly locus specific, SP-PCR was done on 6 human microsatellites within the patient used to make the hybrid cells. No variants were seen in over 1000 genomes screened. ^ These studies show that the somatic cell hybrid approach is a genetically stable system that allows for the determination of factors that could lead to changes in microsatellite instability. It also shows that there is something inherent about the DM1 expanded (CTG)n repeat that it is solely targeted by, as of yet, and unknown mechanism that causes the repeat to be unstable. (Abstract shortened by UMI.)^