A proposed application of the attributable risk technique in the assessment of unmet need among newly organized populations

In the midst of health care reform, and as health care organizations reorganize to provide more cost-effective healthcare, the population is being shifted into new healthcare delivery systems such as health insurance purchasing alliances, and health maintenance organizations. These new models of delivery are usually organized within resource restricted and data limited environments. Health care planners are faced with the challenge of identifying priorities for preventive and primary care services within these newly organized populations (Medicare HMO, Medicaid HMO, etc.). The author proposes a technique usually employed in epidemiology--attributable risk estimation--as a planning methodology to establish preventive health priorities within newly organized populations. Illustrations of the methodology are provided utilizing the Texas 1992 population. ^

Development of a Bayesian Joint Logistic Model to Better Study the Association between Haplotypes and Disease

In 2011, there will be an estimated 1,596,670 new cancer cases and 571,950 cancer-related deaths in the US. With the ever-increasing applications of cancer genetics in epidemiology, there is great potential to identify genetic risk factors that would help identify individuals with increased genetic susceptibility to cancer, which could be used to develop interventions or targeted therapies that could hopefully reduce cancer risk and mortality. In this dissertation, I propose to develop a new statistical method to evaluate the role of haplotypes in cancer susceptibility and development. This model will be flexible enough to handle not only haplotypes of any size, but also a variety of covariates. I will then apply this method to three cancer-related data sets (Hodgkin Disease, Glioma, and Lung Cancer). I hypothesize that there is substantial improvement in the estimation of association between haplotypes and disease, with the use of a Bayesian mathematical method to infer haplotypes that uses prior information from known genetics sources. Analysis based on haplotypes using information from publically available genetic sources generally show increased odds ratios and smaller p-values in both the Hodgkin, Glioma, and Lung data sets. For instance, the Bayesian Joint Logistic Model (BJLM) inferred haplotype TC had a substantially higher estimated effect size (OR=12.16, 95% CI = 2.47-90.1 vs. 9.24, 95% CI = 1.81-47.2) and more significant p-value (0.00044 vs. 0.008) for Hodgkin Disease compared to a traditional logistic regression approach. Also, the effect sizes of haplotypes modeled with recessive genetic effects were higher (and had more significant p-values) when analyzed with the BJLM. Full genetic models with haplotype information developed with the BJLM resulted in significantly higher discriminatory power and a significantly higher Net Reclassification Index compared to those developed with haplo.stats for lung cancer. Future analysis for this work could be to incorporate the 1000 Genomes project, which offers a larger selection of SNPs can be incorporated into the information from known genetic sources as well. Other future analysis include testing non-binary outcomes, like the levels of biomarkers that are present in lung cancer (NNK), and extending this analysis to full GWAS studies.

Mathematical and empirical examinations of some epidemiological procedures

In epidemiology literature, it is often required to investigate the relationships between means where the levels of experiment are actually monotone sets forming a partition on the range of sampling values. With this need, the analysis of these group means is generally performed using classical analysis of variance (ANOVA). However, this method has never been challenged. In this dissertation, we will formulate and present our examination of its validity. First, the classical assumptions of normality and constant variance are not always true. Second, under the null hypothesis of equal means, the test statistic for the classical ANOVA technique is still valid. Third, when the hypothesis of equal means is rejected, the classical analysis techniques for hypotheses of contrasts are not valid. Fourth, under the alternative hypothesis, we can show that the monotone property of levels leads to the conclusion that the means are monotone. Fifth, we propose an appropriate method for handing the data in this situation. ^

Is depression associated with insulin resistance? A systematic review

Studies suggest that depression affects glucose metabolism, and therefore is a risk factor for insulin resistance. The association between depression and insulin resistance has been investigated in a number of studies, but there is no agreement on the results. The objective of this study is to survey the epidemiological studies, identify the ones that measured the association of depression (as exposure) with insulin resistance (as outcome), and perform a systematic review to assess the reliability and strength of the association. For high quality reporting, and assessment, this systematic review used the outlined procedures, guidelines and recommendations for reviews in health care, suggested by the Centre for Reviews and Dissemination, along with recommendations from the STROBE group (Strengthening the Reporting of Observational Studies in Epidemiology). Ovid MEDLINE 1996 to April Week 1 2010, was used to identify the relevant epidemiological studies. To identify the most relevant set of articles for this systematic review, a set of inclusion and exclusion criteria were applied. Six studies that met the specific criteria were selected. Key information from identified studies was tabulated, and the methodological quality, internal and external validity, and the strength of the evidence of the selected studies were assessed. The result from the tabulated data of the reviewed studies indicates that the studies either did not apply a case definition for insulin resistance in their investigation, or did not state a specific value for the index used to define insulin resistance. The quality assessment of the reviewed studies indicates that to assess the association between insulin resistance and depression, specifying a case definition for insulin resistance is important. The case definition for insulin resistance is defined by the World Health Organization and the European Group for the Study of Insulin Resistance as the insulin sensitivity index of the lowest quartile or lowest decile of a general population, respectively. Three studies defined the percentile cut-off point for insulin resistance, but did not give the insulin sensitivity index value. In these cases, it is not possible to compare the results. Three other studies did not define the cut-off point for insulin resistance. In these cases, it is hard to confirm the existence of insulin resistance. In conclusion, to convincingly answer our question, future studies need to adopt a clear case definition, define a percentile cut-off point and reference population, and give value of the insulin resistance measure at the specified percentile.^