THE RELATIONSHIP BETWEEN OBESITY AND PSYCHOLOGICAL GENERAL WELL-BEING IN UNITED STATES WOMEN

While clinical studies have shown a negative relationship between obesity and mental health in women, population studies have not shown a consistent association. However, many of these studies can be criticized regarding fatness level criteria, lack of control variables, and validity of the psychological variables.^ The purpose of this research was to elucidate the relationship between fatness level and mental health in United States women using data from the First National Health and Nutrition Examination Survey (NHANES I), which was conducted on a national probability sample from 1971 to 1974. Mental health was measured by the General Well-Being Schedule (GWB), and fatness level was determined by the sum of the triceps and subscapular skinfolds. Women were categorized as lean (15th percentile or less), normal (16th to 84th percentiles), or obese (85th percentile or greater).^ A conceptual framework was developed which identified the variables of age, race, marital status, socioeconomic status (education), employment status, number of births, physical health, weight history, and perception of body image as important to the fatness level-GWB relationship. Multiple regression analyses were performed separately for whites and blacks with GWB as the response variable, and fatness level, age, education, employment status, number of births, marital status, and health perception as predictor variables. In addition, 2- and 3-way interaction terms for leanness, obesity and age were included as predictor variables. Variables related to weight history and perception of body image were not collected in NHANES I, and thus were not included in this study.^ The results indicated that obesity was a statistically significant predictor of lower GWB in white women even when the other predictor variables were controlled. The full regression model identified the young, more educated, obese female as a subgroup with lower GWB, especially in blacks. These findings were not consistent with the previous non-clinical studies which found that obesity was associated with better mental health. The social stigma of being obese and the preoccupation of women with being lean may have contributed to the lower GWB in these women. ^

Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

An examination of the relationship between maternal and neighborhood-level characteristics and intrauterine growth retardation in Harris County, Texas, 1999--2001

The persistence of low birth weight and intrauterine growth retardation (IUGR) in the United States has puzzled researchers for decades. Much of the work that has been conducted on adverse birth outcomes has focused on low birth weight in general and not on IUGR. Studies that have examined IUGR specifically thus far have focused primarily on individual-level maternal risk factors. These risk factors have only been able to explain a small portion of the variance in IUGR. Therefore, recent work has begun to focus on community-level risk factors in addition to the individual-level maternal characteristics. This study uses Social Ecology to examine the relationship of individual and community-level risk factors and IUGR. Logistic regression was used to establish an individual-level model based on 155, 856 births recorded in Harris County, TX during 1999-2001. IUGR was characterized using a fetal growth ratio method with race/ethnic and sex specific mean birth weights calculated from national vital records. The spatial distributions of 114,460 birth records spatially located within the City of Houston were examined using choropleth, probability and density maps. Census tracts with higher than expected rates of IUGR and high levels of neighborhood disadvantage were highlighted. Neighborhood disadvantage was constructed using socioeconomic variables from the 2000 U.S. Census. Factor analysis was used to create a unified single measure. Lastly, a random coefficients model was used to examine the relationship between varying levels of community disadvantage, given the set of individual-level risk factors for 152,997 birth records spatially located within Harris County, TX. Neighborhood disadvantage was measured using three different indices adapted from previous work. The findings show that pregnancy-induced hypertension, previous preterm infant, tobacco use and insufficient weight gain have the highest association with IUGR. Neighborhood disadvantage only slightly further increases the risk of IUGR (OR 1.12 to 1.23). Although community level disadvantage only helped to explain a small proportion of the variance of IUGR, it did have a significant impact. This finding suggests that community level risk factors should be included in future work with IUGR and that more work needs to be conducted. ^

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.^

A systematic review of literature to compare the fertility desires among HIV positive women and cancer survivors

Background. Decision-making on reproductive issues is influenced by an interplay of individual, familial, medical, religious and socio-cultural factors. Women with chronic medical illnesses such an HIV infection and cancers are often fraught with decisional conflicts about child-bearing. With increase in the incidence of these illnesses as well as improvement in survival rates, there is a need to pay due attention to the issue of reproductive decision-making. Examining the prevalence and determinants of fertility desires in the two groups in a comparative manner would help bring to light perception of the medical community and the society in general on the two illnesses and the issue of motherhood. ^ Methods. Systematic literature search was undertaken using databases such as MEDLINE (PubMED), MEDLINE (Ovid), PsycInfo and Web of Science. Articles published in English and English language abstracts for foreign articles were included. Studies that explore ‘fertility desires’ as the outcome variable were included. Quantitative studies which have assessed the prevalence of fertility desires as well as qualitative studies which have provided a descriptive understanding of factors governing reproductive desires were included in the review. ^ Results. A total of 34 articles (29 studies examining HIV and 5 studies examining cancer in relation to fertility desires). Variables such as age, stage of illness, support of spouse and family, perception of the medical community and one’s own view of motherhood were key determinants among both groups. ^ Conclusion. There is a need for uniform, systematic research in this field. It is important that health care workers acknowledge these decisional conflicts, include them as part of the medical care of these patients and provide guidance with the right balance of information, practicality and compassion.^

THE DEVELOPMENT OF COMMUNITY COMPETENCE THROUGH A NEIGHBORHOOD ORGANIZATION (LOS ANGELES, MEXICAN-AMERICAN)

This investigation focused on how people cope with the demands of their environment in a competent manner. It sought to assess the effects of learning competent coping behaviors on self-reported well-being. The study chose a community-evolved, organized effort on the part of a group of neighborhoods to build competence in the Mexican-American community of East Los Angeles. This network was a citizen-action organization called the United Neighborhoods Organization. UNO was selected because it concentrated on developing community leaders by using spiritual beliefs and family values as shared community resources. Neighborhood leaders were encouraged to engage in risk-taking and confrontation maneuvers. They were also taught problem-solving skills and provided with social support.^ A survey instrument was developed to assess sociodemographic characteristics, acculturation history and status, willingness to engage in risk-taking and confrontation and self-perceived general well-being. The study relied on eight months of daily participant-observation of the organization, the East Los Angeles environment and the interaction between the two. At the end of the observation period, a sample of 150 UNO participants were given the survey questionnaire as was a matched group of 150 non-UNO participants who were ELA residents.^ The study sample was mostly women, in their middle age years who had lived in the area from 5 to more than 30 years. Significantly more single persons were found in the UNO group. The sample was almost equally divided into English and Spanish speaking respondents. Acculturatively almost all the sample fell in the Very Mexican and Mostly Mexican types. The survey found a trend of association between participating in UNO and reporting feeling well. A statistically significant association was found among UNO participants between taking risks and reporting feeling well, regardless of a tendency for all the sample to minimize risk. A trend was seen for married UNO participants to report feeling well. Slightly more UNO participants were willing to engage in confrontation and a substantial proportion of the participants who were confronters reported feeling well in comparison to their counterparts. Ethnic pride was positively associated with participation in UNO and showed a trend in the expected direction with reported self-perceived well-being. ^

Economic evaluation of latent tuberculosis infection screening among health care workers in Houston

Since interferon-gamma release assays (IGRAs) were introduced in the 2000's, tuberculin skin testing (TST) and IGRAs have been used in various latent tuberculosis infection (LTBI) screening settings. IGRAs are laboratory-based tests and are considered not to be affected by previous Bacille de Calmette et Guérin (BCG) vaccination; however, they are more costly when compared directly with TST, which does not require specimen processing in a laboratory. This study aimed to examine TST and two types of IGRAs, QuantiFERON-TB Gold in Tube (QFT-GIT) and T-SPOT. TB (TSPOT), from an economic viewpoint. Firstly, a systematic literature review was conducted to identify cost related analyses of LTBI screening. Secondly, specific cost information detailing each test's items and labor was collected from an LTBI screening program of health care workers in Houston, and the cost of each test was computed. Thirdly, using the computed cost estimate of each test, cost-effectiveness analyses were conducted to compare TST and IGRAs.^ A literature search showed that a limited number of studies have been conducted, but the IGRA's economic advantages were common among studies. Cost analyses showed that IGRAs were much more costly than TST. The results were consistent with previous studies. In cost-effectiveness analyses, where test cost and consequential TB-related cost were considered, IGRAs showed variable advantages over TST depending on the targeted population. When only non BCG-vaccinated people were considered, TST was the least costly option among the three tests. On the other hand, when only BCG-vaccinated people were considered, IGRAs were less costly options. These results were mostly consistent even with varying assumption parameters.^ IGRAs can be more costly than TST, but their economic disadvantages are alleviated when the target population was BCG-vaccinated. Based on current knowledge, IGRAs may be recommended in a population where the BCG history is mixed. Additional studies are needed to better understand IGRA's reliability among low-incidence and low-risk populations in which background TB prevalence is low.^

Instrumental variable method for the causal relationship between soluble receptor for advanced glycation end-products (sRAGE) and risk of pancreatic cancer

This thesis project is motivated by the potential problem of using observational data to draw inferences about a causal relationship in observational epidemiology research when controlled randomization is not applicable. Instrumental variable (IV) method is one of the statistical tools to overcome this problem. Mendelian randomization study uses genetic variants as IVs in genetic association study. In this thesis, the IV method, as well as standard logistic and linear regression models, is used to investigate the causal association between risk of pancreatic cancer and the circulating levels of soluble receptor for advanced glycation end-products (sRAGE). Higher levels of serum sRAGE were found to be associated with a lower risk of pancreatic cancer in a previous observational study (255 cases and 485 controls). However, such a novel association may be biased by unknown confounding factors. In a case-control study, we aimed to use the IV approach to confirm or refute this observation in a subset of study subjects for whom the genotyping data were available (178 cases and 177 controls). Two-stage IV method using generalized method of moments-structural mean models (GMM-SMM) was conducted and the relative risk (RR) was calculated. In the first stage analysis, we found that the single nucleotide polymorphism (SNP) rs2070600 of the receptor for advanced glycation end-products (AGER) gene meets all three general assumptions for a genetic IV in examining the causal association between sRAGE and risk of pancreatic cancer. The variant allele of SNP rs2070600 of the AGER gene was associated with lower levels of sRAGE, and it was neither associated with risk of pancreatic cancer, nor with the confounding factors. It was a potential strong IV (F statistic = 29.2). However, in the second stage analysis, the GMM-SMM model failed to converge due to non- concaveness probably because of the small sample size. Therefore, the IV analysis could not support the causality of the association between serum sRAGE levels and risk of pancreatic cancer. Nevertheless, these analyses suggest that rs2070600 was a potentially good genetic IV for testing the causality between the risk of pancreatic cancer and sRAGE levels. A larger sample size is required to conduct a credible IV analysis.^