Single nucleotide polymorphism (SNP) selection for genotype-phenotype association studies

With hundreds of single nucleotide polymorphisms (SNPs) in a candidate gene and millions of SNPs across the genome, selecting an informative subset of SNPs to maximize the ability to detect genotype-phenotype association is of great interest and importance. In addition, with a large number of SNPs, analytic methods are needed that allow investigators to control the false positive rate resulting from large numbers of SNP genotype-phenotype analyses. This dissertation uses simulated data to explore methods for selecting SNPs for genotype-phenotype association studies. I examined the pattern of linkage disequilibrium (LD) across a candidate gene region and used this pattern to aid in localizing a disease-influencing mutation. The results indicate that the r2 measure of linkage disequilibrium is preferred over the common D′ measure for use in genotype-phenotype association studies. Using step-wise linear regression, the best predictor of the quantitative trait was not usually the single functional mutation. Rather it was a SNP that was in high linkage disequilibrium with the functional mutation. Next, I compared three strategies for selecting SNPs for application to phenotype association studies: based on measures of linkage disequilibrium, based on a measure of haplotype diversity, and random selection. The results demonstrate that SNPs selected based on maximum haplotype diversity are more informative and yield higher power than randomly selected SNPs or SNPs selected based on low pair-wise LD. The data also indicate that for genes with small contribution to the phenotype, it is more prudent for investigators to increase their sample size than to continuously increase the number of SNPs in order to improve statistical power. When typing large numbers of SNPs, researchers are faced with the challenge of utilizing an appropriate statistical method that controls the type I error rate while maintaining adequate power. We show that an empirical genotype based multi-locus global test that uses permutation testing to investigate the null distribution of the maximum test statistic maintains a desired overall type I error rate while not overly sacrificing statistical power. The results also show that when the penetrance model is simple the multi-locus global test does as well or better than the haplotype analysis. However, for more complex models, haplotype analyses offer advantages. The results of this dissertation will be of utility to human geneticists designing large-scale multi-locus genotype-phenotype association studies. ^

Adiposity and the perilipin gene: The role of single locus and multilocus variation, obesity-related quantitative traits, and disease-related phenotypes in the Atherosclerosis Risk in Communities (ARIC) study

Obesity is a complex multifactorial disease and is a public health priority. Perilipin coats the surface of lipid droplets in adipocytes and is believed to stabilize these lipid bodies by protecting triglyceride from early lipolysis. This research project evaluated the association between genetic variation within the human perilipin (PLIN) gene and obesity-related quantitative traits and disease-related phenotypes in Non-Hispanic White (NHW) and African American (AA) participants from the Atherosclerosis Risk in Communities (ARIC) Study. ^ Multivariate linear regression, multivariate logistic regression, and Cox proportional hazards models evaluated the association between single gene variants (rs2304794, rs894160, rs8179071, and rs2304795) and multilocus variation (rs894160 and rs2304795) within the PLIN gene and both obesity-related quantitative traits (body weight, body mass index [BMI], waist girth, waist-to-hip ratio [WHR], estimated percent body fat, and plasma total triglycerides) and disease-related phenotypes (prevalent obesity, metabolic syndrome [MetS], prevalent coronary heart disease [CHD], and incident CHD). Single variant analyses were stratified by race and gender within race while multilocus analyses were stratified by race. ^ Single variant analyses revealed that rs2304794 and rs894160 were significantly related to plasma triglyceride levels in all NHWs and NHW women. Among AA women, variant rs8179071 was associated with triglyceride levels and rs2304794 was associated with risk-raising waist circumference (>0.8 in women). The multilocus effects of variants rs894160 and rs2304795 were significantly associated with body weight, waist girth, WHR, estimated percent body fat, class II obesity (BMI ≥ 35 kg/m2), class III obesity (BMI ≥ 35 kg/m2), and risk-raising WHR (>0.9 in men and >0.8 in women) in AAs. Variant rs2304795 was significantly related to prevalent MetS among AA males and prevalent CHD in NHW women; multilocus effects of the PLIN gene were associated with prevalent CHD among NHWs. Rs2304794 was associated with incident CHD in the absence of the MetS among AAs. These findings support the hypothesis that variation within the PLIN gene influences obesity-related traits and disease-related phenotypes. ^ Understanding these effects of the PLIN genotype on the development of obesity can potentially lead to tailored health promotion interventions that are more effective. ^

The role of self-efficacy in a low fat high fiber intervention to reduce breast cancer risk among African American women

Purpose. To determine if self-efficacy (SE) changes predicted total fat (TF) and total fiber (TFB) intake and the relationship between SE changes and the two dietary outcomes. ^ Design. This is a secondary analysis, utilizing baseline and first follow up (FFU) data from the NULIFE, a randomized trial. ^ Setting. Nutrition classes were taught in the Texas Medical Center in Houston, Texas. ^ Participants. 79 pre-menopausal, 25--45 year old African American women with an 85% response rate at FFU. ^ Method. Dietary intake was assessed with the Arizona Food Frequency Questionnaire and SE with the Self Efficacy for Dietary Change Questionnaire. Analysis was done using Stata version 9. Linear and logistic regression was used with adjustment for confounders. ^ Results. Linear regression analyses showed that SE changes for eating fruits and vegetables predicted total fiber intake in the control group for both the univariate (P = 0.001) and multivariate (P = 0.01) models while SE for eating fruits and vegetables at first follow-up predicted total fiber intake in the intervention for both models (P = 0.000). Logistic regression analyses of low fat SE changes and 30% or less for total fat intake, showed an adjusted OR of 0.22 (95% CI = 0.03, 1.48; P = 0.12) in the intervention group. The logistic regression analyses of SE changes in fruits and vegetables and 10g or more for total fiber intake, showed an adjusted OR of 6.25 (95% CI = 0.53, 72.78; P = 0.14) in the control group. ^ Conclusion. SE for eating fruits and vegetables at first follow-up predicted intervention groups' TFB intake and intervention women that increased their SE for eating a low fat diet were more likely to achieve the study goal of 30% or less calories from TF. SE changes for eating fruits and vegetables predicted the control's TFB intake and control women that increased their SE for eating fruits and vegetables were more likely to achieve the study goal of 10 g or more from TFB. Limitations are use of self-report measures, small sample size, and possible control group contamination.^

A space-time analysis of reported shigellosis and salmonellosis cases in Texas from 2000 to 2004

Background. The purpose of this study was to describe the risk factors and demographics of persons with salmonellosis and shigellosis and to investigate both seasonal and spatial variations in the occurrence of these infections in Texas from 2000 to 2004, utilizing time series analyses and the geographic information system digital mapping methods. ^ Methods. Spatial Analysis: MapInfo software was used to map the distribution of age-adjusted rates of reported shigellosis and salmonellosis in Texas from 2000–2004 by zip codes. Census data on above or below poverty level, household income, highest level of educational attainment, race, ethnicity, and urban/rural community status was obtained from the 2000 Decennial Census for each zip code. The zip codes with the upper 10% and lower 10% were compared using t-tests and logistic regression to determine whether there were any potential risk factors. ^ Temporal analysis. Seasonal patterns in the prevalence of infections in Texas from 2000 to 2003 were determined by performing time-series analysis on the numbers of cases of salmonellosis and shigellosis. A linear regression was also performed to assess for trends in the incidence of each disease, along with auto-correlation and multi-component cosinor analysis. ^ Results. Spatial analysis: Analysis by general linear model showed a significant association between infection rates and age, with young children aged less than 5 and those aged 5–9 years having increased risk of infection for both disease conditions. The data demonstrated that those populations with high percentages of people who attained a higher than high school education were less likely to be represented in zip codes with high rates of shigellosis. However, for salmonellosis, logistic regression models indicated that when compared to populations with high percentages of non-high school graduates, having a high school diploma or equivalent increased the odds of having a high rate of infection. ^ Temporal analysis. For shigellosis, multi-component cosinor analyses were used to determine the approximated cosine curve which represented a statistically significant representation of the time series data for all age groups by sex. The shigellosis results show 2 peaks, with a major peak occurring in June and a secondary peak appearing around October. Salmonellosis results showed a single peak and trough in all age groups with the peak occurring in August and the trough occurring in February. ^ Conclusion. The results from this study can be used by public health agencies to determine the timing of public health awareness programs and interventions in order to prevent salmonellosis and shigellosis from occurring. Because young children depend on adults for their meals, it is important to increase the awareness of day-care workers and new parents about modes of transmission and hygienic methods of food preparation and storage. ^

Relationship between dietary patterns and body mass index in Hispanic children ages 4--19 years of age

Background. In over 30 years, the prevalence of overweight for children and adolescents has increased across the United States (Barlow et al., 2007; Ogden, Flegal, Carroll, & Johnson, 2002). Childhood obesity is linked with adverse physiological and psychological issues in youth and affects ethnic/minority populations in disproportionate rates (Barlow et al., 2007; Butte et al., 2006; Butte, Cai, Cole, Wilson, Fisher, Zakeri, Ellis, & Comuzzie, 2007). More importantly, overweight in children and youth tends to track into adulthood (McNaughton, Ball, Mishra, & Crawford, 2008; Ogden et al., 2002). Childhood obesity affects body functions such as the cardiovascular, respiratory, gastrointestinal, and endocrine systems, including emotional health (Barlow et al., 2007, Ogden et al., 2002). Several dietary factors have been associated with the development of obesity in children; however, these factors have not been fully elucidated, especially in ethnic/minority children. In particular, few studies have been done to determine the effects of different meal patterns on the development of obesity in children. Purpose. The purpose of the study is to examine the relationships between daily proportions of energy consumed and energy derived from fat across breakfast, lunch, dinner, and snack, and obesity among Hispanic children and adolescents. Methods. A cross-sectional design was used to evaluate the relationship between dietary patterns and overweight status in Hispanic children and adolescents 4-19 years of age who participated in the Viva La Familia Study. The goal of the Viva La Familia Study was to evaluate genetic and environmental factors affecting childhood obesity and its co-morbidities in the Hispanic population (Butte et al., 2006, 2007). The study enrolled 1030 Hispanic children and adolescents from 319 families and examined factors related to increased body weight by focusing on a multilevel analysis of extensive sociodemographic, genetic, metabolic, and behavioral data. Baseline dietary intakes of the children were collected using 24-hour recalls, and body mass index was calculated from measured height and weight, and classified using the CDC standards. Dietary data were analyzed using a GEE population-averaged panel-data model with a cluster variable family identifier to include possible correlations within related data sets. A linear regression model was used to analyze associations of dietary patterns using possible covariates, and to examine the percentage of daily energy coming from breakfast, lunch, dinner, and snack while adjusting for age, sex, and BMI z-score. Random-effects logistic regression models were used to determine the relationship of the dietary variables with obesity status and to understand if the percent energy intake (%EI) derived from fat from all meals (breakfast, lunch, dinner, and snacks) affected obesity. Results. Older children (age 4-19 years) consumed a higher percent of energy at lunch and dinner and less percent energy from snacks compared to younger children. Age was significantly associated with percentage of total energy intake (%TEI) for lunch, as well as dinner, while no association was found by gender. Percent of energy consumed from dinner significantly differed by obesity status, with obese children consuming more energy at dinner (p = 0.03), but no associations were found between percent energy from fat and obesity across all meals. Conclusions. Information from this study can be used to develop interventions that target dietary intake patterns in obesity prevention programs for Hispanic children and adolescents. In particular, intervention programs for children should target dietary patterns with energy intake that is spread throughout the day and earlier in the day. These results indicate that a longitudinal study should be used to further explore the relationship of dietary patterns and BMI in this and other populations (Dubois et al., 2008; Rodriquez & Moreno, 2006; Thompson et al., 2005; Wilson et al., in review, 2008). ^

Statistical and methodological challenges for disaster preparedness and medical needs assessment in Rio Grande Valley of Texas

In recent years, disaster preparedness through assessment of medical and special needs persons (MSNP) has taken a center place in public eye in effect of frequent natural disasters such as hurricanes, storm surge or tsunami due to climate change and increased human activity on our planet. Statistical methods complex survey design and analysis have equally gained significance as a consequence. However, there exist many challenges still, to infer such assessments over the target population for policy level advocacy and implementation. ^ Objective. This study discusses the use of some of the statistical methods for disaster preparedness and medical needs assessment to facilitate local and state governments for its policy level decision making and logistic support to avoid any loss of life and property in future calamities. ^ Methods. In order to obtain precise and unbiased estimates for Medical Special Needs Persons (MSNP) and disaster preparedness for evacuation in Rio Grande Valley (RGV) of Texas, a stratified and cluster-randomized multi-stage sampling design was implemented. US School of Public Health, Brownsville surveyed 3088 households in three counties namely Cameron, Hidalgo, and Willacy. Multiple statistical methods were implemented and estimates were obtained taking into count probability of selection and clustering effects. Statistical methods for data analysis discussed were Multivariate Linear Regression (MLR), Survey Linear Regression (Svy-Reg), Generalized Estimation Equation (GEE) and Multilevel Mixed Models (MLM) all with and without sampling weights. ^ Results. Estimated population for RGV was 1,146,796. There were 51.5% female, 90% Hispanic, 73% married, 56% unemployed and 37% with their personal transport. 40% people attained education up to elementary school, another 42% reaching high school and only 18% went to college. Median household income is less than $15,000/year. MSNP estimated to be 44,196 (3.98%) [95% CI: 39,029; 51,123]. All statistical models are in concordance with MSNP estimates ranging from 44,000 to 48,000. MSNP estimates for statistical methods are: MLR (47,707; 95% CI: 42,462; 52,999), MLR with weights (45,882; 95% CI: 39,792; 51,972), Bootstrap Regression (47,730; 95% CI: 41,629; 53,785), GEE (47,649; 95% CI: 41,629; 53,670), GEE with weights (45,076; 95% CI: 39,029; 51,123), Svy-Reg (44,196; 95% CI: 40,004; 48,390) and MLM (46,513; 95% CI: 39,869; 53,157). ^ Conclusion. RGV is a flood zone, most susceptible to hurricanes and other natural disasters. People in the region are mostly Hispanic, under-educated with least income levels in the U.S. In case of any disaster people in large are incapacitated with only 37% have their personal transport to take care of MSNP. Local and state government’s intervention in terms of planning, preparation and support for evacuation is necessary in any such disaster to avoid loss of precious human life. ^ Key words: Complex Surveys, statistical methods, multilevel models, cluster randomized, sampling weights, raking, survey regression, generalized estimation equations (GEE), random effects, Intracluster correlation coefficient (ICC).^

Identfying predictors of St. Louis encephalitis activity in Houston Texas

Generalized linear Poisson and logistic regression models were utilized to examine the relationship between temperature and precipitation and cases of Saint Louis encephalitis virus spread in the Houston metropolitan area. The models were investigated with and without repeated measures, with a first order autoregressive (AR1) correlation structure used for the repeated measures model. The two types of Poisson regression models, with and without correlation structure, showed that a unit increase in temperature measured in degrees Fahrenheit increases the occurrence of the virus 1.7 times and a unit increase in precipitation measured in inches increases the occurrence of the virus 1.5 times. Logistic regression did not show these covariates to be significant as predictors for encephalitis activity in Houston for either correlation structure. This discrepancy for the logistic model could be attributed to the small data set.^ Keywords: Saint Louis Encephalitis; Generalized Linear Model; Poisson; Logistic; First Order Autoregressive; Temperature; Precipitation. ^

A novel method for evaluating an interaction effect of correlated continuous covariates in a linear model

Interaction effect is an important scientific interest for many areas of research. Common approach for investigating the interaction effect of two continuous covariates on a response variable is through a cross-product term in multiple linear regression. In epidemiological studies, the two-way analysis of variance (ANOVA) type of method has also been utilized to examine the interaction effect by replacing the continuous covariates with their discretized levels. However, the implications of model assumptions of either approach have not been examined and the statistical validation has only focused on the general method, not specifically for the interaction effect.^ In this dissertation, we investigated the validity of both approaches based on the mathematical assumptions for non-skewed data. We showed that linear regression may not be an appropriate model when the interaction effect exists because it implies a highly skewed distribution for the response variable. We also showed that the normality and constant variance assumptions required by ANOVA are not satisfied in the model where the continuous covariates are replaced with their discretized levels. Therefore, naïve application of ANOVA method may lead to an incorrect conclusion. ^ Given the problems identified above, we proposed a novel method modifying from the traditional ANOVA approach to rigorously evaluate the interaction effect. The analytical expression of the interaction effect was derived based on the conditional distribution of the response variable given the discretized continuous covariates. A testing procedure that combines the p-values from each level of the discretized covariates was developed to test the overall significance of the interaction effect. According to the simulation study, the proposed method is more powerful then the least squares regression and the ANOVA method in detecting the interaction effect when data comes from a trivariate normal distribution. The proposed method was applied to a dataset from the National Institute of Neurological Disorders and Stroke (NINDS) tissue plasminogen activator (t-PA) stroke trial, and baseline age-by-weight interaction effect was found significant in predicting the change from baseline in NIHSS at Month-3 among patients received t-PA therapy.^

Micronutrient intakes and their associations with markers of inflammation, subclinical atherosclerosis, cardiovascular disease, type II diabetes and metabolic syndrome

We investigated cross-sectional associations between intakes of zinc, magnesium, heme- and non heme iron, beta-carotene, vitamin C and vitamin E and inflammation and subclinical atherosclerosis in the Multi-Ethnic Study of Atherosclerosis (MESA). We also investigated prospective associations between those micronutrients and incident MetS, T2D and CVD. Participants between 45-84 years of age at baseline were followed between 2000 and 2007. Dietary intake was assessed at baseline using a 120-item food frequency questionnaire. Multivariable linear regression and Cox proportional hazard regression models were used to evaluate associations of interest. Dietary intakes of non-heme iron and Mg were inversely associated with tHcy concentrations (geometric means across quintiles: 9.11, 8.86, 8.74, 8.71, and 8.50 µmol/L for non-heme iron, and 9.20, 9.00, 8.65, 8.76, and 8.33 µmol/L for Mg; ptrends <0.001). Mg intake was inversely associated with high CC-IMT; odds ratio (95% CI) for extreme quintiles 0.76 (0.58, 1.01), ptrend: 0.002. Dietary Zn and heme-iron were positively associated with CRP (geometric means: 1.73, 1.75, 1.78, 1.88, and 1.96 mg/L for Zn and 1.72, 1.76, 1.83, 1.86, and 1.94 mg/L for heme-iron). In the prospective analysis, dietary vitamin E intake was inversely associated with incident MetS and with incident CVD (HR [CI] for extreme quintiles - MetS: 0.78 [0.62-0.97] ptrend=0.01; CVD: 0.69 [0.46-1.03]; ptrend =0.04). Intake of heme-iron from red meat and Zn from red meat, but not from other sources, were each positively associated with risk of CVD (HR [CI] - heme-iron from red meat: 1.65 [1.10-2.47] ptrend = 0.01; Zn from red meat: 1.51 [1.02 - 2.24] ptrend =0.01) and MetS (HR [CI] - heme-iron from red meat: 1.25 [0.99-1.56] ptrend =0.03; Zn from red meat: 1.29 [1.03-1.61]; ptrend = 0.04). All associations evaluated were similar across different strata of gender, race-ethnicity and alcohol intake. Most of the micronutrients investigated were not associated with the outcomes of interest in this multi-ethnic cohort. These observations do not provide consistent support for the hypothesized association of individual nutrients with inflammatory markers, MetS, T2D, or CVD. However, nutrients consumed in red meat, or consumption of red meat as a whole, may increase risk of MetS and CVD.^

FEMALE SEXUAL MATURATION AND BLOOD PRESSURE (SECONDARY-SEX CHARACTERISTICS, GROWTH, DEVELOPMENT)

This study described the relationship of sexual maturation and blood pressure in a sample (n = 361) of white females, ages seven through 18, attending public schools in a defined area of Central Texas during October through December, 1984. Other correlates of blood pressure were also described for this sample.^ A survey was performed to obtain the data on height, weight, body mass, pulse rate, upper arm circumference and length, and blood pressure. Each subject self-assessed her secondary sex characteristics (breast and pubic hair) according to drawings of the Tanner stages of maturation. The subjects were interviewed to obtain data on personal health habits and menstrual status. Student age, ethnic group and place of residence were abstracted from school records. Parents or guardians of the subjects responded to a questionnaire pertaining to parental and subject health history and parents' occupation and educational attainment.^ In the simple linear regression analysis, sexual maturation and variables of body size were significantly (p < 0.001) and positively associated with systolic and fourth- and fifth-phase diastolic blood pressure. The demographic and socioeconomic variables were not sufficiently variant in this population to have differential effects on the relation between blood pressure and maturation. Stepwise multiple regression was used to assess the contribution of sexual maturation to the variance of blood pressure after accounting for the variables of body size. Sexual maturation (breast stage) along with weight, height and body mass remained in the multiple regression models for fourth- and fifth-phase diastolic blood pressure. Only height and body mass remained in the regression model for systolic blood pressure; sexual maturation did not contribute more to the explanation of the systolic blood pressure variance.^ The association of sexual maturation with blood pressure level was established in this sample of young white females. More research is needed first, to determine if this relationship prevails in other populations of young females, and second, to determine the relationship of sexual maturation sequence and change with the change of blood pressure during childhood and adolescence. ^

Identification of micro-RNAs targeting genes of PI3K/AKT pathway in ovarian cancer

Ovarian cancer is the leading cause of cancer-related death for females due to lack of specific early detection method. It is of great interest to find molecular-based biomarkers which are sensitive and specific to ovarian cancer for early diagnosis, prognosis and therapeutics. miRNAs have been proposed to be potential biomarkers that could be used in cancer prevention and therapeutics. The current study analyzed the miRNA and mRNA expression data extracted from the Cancer Genome Atlas (TCGA) database. Using simple linear regression and multiple regression models, we found 71 miRNA-mRNA pairs which were negatively associated between 56 miRNAs and 24 genes of PI3K/AKT pathway. Among these miRNA and mRNA target pairs, 9 of them were in agreement with the predictions from the most commonly used target prediction programs including miRGen, miRDB, miRTarbase and miR2Disease. These shared miRNA-mRNA pairs were considered to be the most potential genes that were involved in ovarian cancer. Furthermore, 4 of the 9 target genes encode cell cycle or apoptosis related proteins including Cyclin D1, p21, FOXO1 and Bcl2, suggesting that their regulator miRNAs including miR-16, miR-96 and miR-21 most likely played important roles in promoting tumor growth through dysregulated cell cycle or apoptosis. miR-96 was also found to directly target IRS-1. In addition, the results showed that miR-17 and miR-9 may be involved in ovarian cancer through targeting JAK1. This study might provide evidence for using miRNA or miRNA profile as biomarker.^

Attitudes of dental students to hand hygiene practices at a dental school

Objectives. The purpose of this study was to elucidate behavioral determinants (prevailing attitudes and beliefs) of hand hygiene practices among undergraduate dental students in a dental school. ^ Methods. Statistical modeling using the Integrative Behavioral Model (IBM) prediction was utilized to develop a questionnaire for evaluating behavioral perceptions of hand hygiene practices by dental school students. Self-report questionnaires were given to second, third and fourth year undergraduate dental students. Models representing two distinct hand hygiene practices, termed "elective in-dental school hand hygiene practice" and "inherent in-dental school hand hygiene practice" were tested using linear regression analysis. ^ Results. 58 responses were received (24.5%); the sample mean age was 26.6 years old and females comprised 51%. In our models, elective in-dental school hand hygiene practice and inherent in-dental school hand hygiene practice, explained 40% and 28%, respectively, of the variance in behavioral intention. Translation of community hand hygiene practice to the dental school setting is the predominant driver of elective hand hygiene practice. Intended elective in-school hand hygiene practice is further significantly predicted by students' self-efficacy. Students' attitudes, peer pressure of other dental students and clinic administrators, and role modeling had minimal effects. Inherent hand hygiene intent was strongly predicted by students' beliefs in the benefits of the activity and, to a lesser extent, role modeling. Inherent and elective community behaviors were insignificant. ^ Conclusions. This study provided significant insights into dental student's hand hygiene behavior and can form the basis for an effective behavioral intervention program designed to improve hand hygiene compliance.^

New methods for quantification and analysis of quantitative real-time polymerase chain reaction data

Quantitative real-time polymerase chain reaction (qPCR) is a sensitive gene quantitation method that has been widely used in the biological and biomedical fields. The currently used methods for PCR data analysis, including the threshold cycle (CT) method, linear and non-linear model fitting methods, all require subtracting background fluorescence. However, the removal of background fluorescence is usually inaccurate, and therefore can distort results. Here, we propose a new method, the taking-difference linear regression method, to overcome this limitation. Briefly, for each two consecutive PCR cycles, we subtracted the fluorescence in the former cycle from that in the later cycle, transforming the n cycle raw data into n-1 cycle data. Then linear regression was applied to the natural logarithm of the transformed data. Finally, amplification efficiencies and the initial DNA molecular numbers were calculated for each PCR run. To evaluate this new method, we compared it in terms of accuracy and precision with the original linear regression method with three background corrections, being the mean of cycles 1-3, the mean of cycles 3-7, and the minimum. Three criteria, including threshold identification, max R2, and max slope, were employed to search for target data points. Considering that PCR data are time series data, we also applied linear mixed models. Collectively, when the threshold identification criterion was applied and when the linear mixed model was adopted, the taking-difference linear regression method was superior as it gave an accurate estimation of initial DNA amount and a reasonable estimation of PCR amplification efficiencies. When the criteria of max R2 and max slope were used, the original linear regression method gave an accurate estimation of initial DNA amount. Overall, the taking-difference linear regression method avoids the error in subtracting an unknown background and thus it is theoretically more accurate and reliable. This method is easy to perform and the taking-difference strategy can be extended to all current methods for qPCR data analysis.^

Health outcomes, quality improvement and cost-effectiveness of the Community Diabetes Education (CoDE) program: Results of a randomized controlled trial

The objectives of this dissertation were to evaluate health outcomes, quality improvement measures, and the long-term cost-effectiveness and impact on diabetes-related microvascular and macrovascular complications of a community health worker-led culturally tailored diabetes education and management intervention provided to uninsured Mexican Americans in an urban faith-based clinic. A prospective, randomized controlled repeated measures design was employed to compare the intervention effects between: (1) an intervention group (n=90) that participated in the Community Diabetes Education (CoDE) program along with usual medical care; and (2) a wait-listed comparison group (n=90) that received only usual medical care. Changes in hemoglobin A1c (HbA1c) and secondary outcomes (lipid status, blood pressure and body mass index) were assessed using linear mixed-models and an intention-to-treat approach. The CoDE group experienced greater reduction in HbA1c (-1.6%, p<.001) than the control group (-.9%, p<.001) over the 12 month study period. After adjusting for group-by-time interaction, antidiabetic medication use at baseline, changes made to the antidiabetic regime over the study period, duration of diabetes and baseline HbA1c, a statistically significant intervention effect on HbA1c (-.7%, p=.02) was observed for CoDE participants. Process and outcome quality measures were evaluated using multiple mixed-effects logistic regression models. Assessment of quality indicators revealed that the CoDE intervention group was significantly more likely to have received a dilated retinal examination than the control group, and 53% achieved a HbA1c below 7% compared with 38% of control group subjects. Long-term cost-effectiveness and impact on diabetes-related health outcomes were estimated through simulation modeling using the rigorously validated Archimedes Model. Over a 20 year time horizon, CoDE participants were forecasted to have less proliferative diabetic retinopathy, fewer foot ulcers, and reduced numbers of foot amputations than control group subjects who received usual medical care. An incremental cost-effectiveness ratio of $355 per quality-adjusted life-year gained was estimated for CoDE intervention participants over the same time period. The results from the three areas of program evaluation: impact on short-term health outcomes, quantification of improvement in quality of diabetes care, and projection of long-term cost-effectiveness and impact on diabetes-related health outcomes provide evidence that a community health worker can be a valuable resource to reduce diabetes disparities for uninsured Mexican Americans. This evidence supports formal integration of community health workers as members of the diabetes care team.^