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Search Results: 1 - 10 of 168935 matches for " Leonard E Egede "
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Association of Health Literacy with Complementary and Alternative Medicine Use: A Cross-Sectional Study in Adult Primary Care Patients
Sujeev S Bains, Leonard E Egede
BMC Complementary and Alternative Medicine , 2011, DOI: 10.1186/1472-6882-11-138
Abstract: 351 patients were recruited from an outpatient primary care clinic. Validated surveys assessed CAM use (I-CAM-Q), health literacy (REALM-R), and demographic information. We compared demographics by health literacy (adequate vs. inadequate) and overall and individual CAM categories by health literacy using chi square statistics. We found a race by health literacy interaction and ran sequential logistic regression models stratified by race to test the association between health literacy and overall CAM use (Model 1), Model 1 + education (Model 2), and Model 2 + other demographic characteristics (Model 3). We reported the adjusted effect of health literacy on CAM use for both whites and African Americans separately.75% of the participants had adequate literacy and 80% used CAM. CAM use differed by CAM category. Among whites, adequate health literacy was significantly associated with increased CAM use in both unadjusted (Model 1, OR 7.68; p = 0.001) and models adjusted for education (Model 2, OR 7.70; p = 0.002) and other sociodemographics (Model 3, OR 9.42; p = 0.01). Among African Americans, adequate health literacy was not associated with CAM use in any of the models.We found a race by literacy interaction suggesting that the relationship between health literacy and CAM use differed significantly by race. Adequate health literacy among whites is associated with increased CAM use, but not associated with CAM use in African Americans.Complementary and Alternative Medicine (CAM) is defined by the United States National Center for Complementary and Alternative Medicine (NCCAM) as "a group of diverse medical and healthcare systems, practices, and products that are not presently considered to be part of conventional medicine" [1]. In the United States, it is estimated that 83 million adults [2,3] utilize CAM with total out-of pocket expenditures approaching $33.9 billion yearly [4].CAM use is common among patients with medical illnesses [5-9] and has been associated with a
Fitting parametric random effects models in very large data sets with application to VHA national data
Gebregziabher Mulugeta,Egede Leonard,Gilbert Gregory E,Hunt Kelly
BMC Medical Research Methodology , 2012, DOI: 10.1186/1471-2288-12-163
Abstract: Background With the current focus on personalized medicine, patient/subject level inference is often of key interest in translational research. As a result, random effects models (REM) are becoming popular for patient level inference. However, for very large data sets that are characterized by large sample size, it can be difficult to fit REM using commonly available statistical software such as SAS since they require inordinate amounts of computer time and memory allocations beyond what are available preventing model convergence. For example, in a retrospective cohort study of over 800,000 Veterans with type 2 diabetes with longitudinal data over 5 years, fitting REM via generalized linear mixed modeling using currently available standard procedures in SAS (e.g. PROC GLIMMIX) was very difficult and same problems exist in Stata’s gllamm or R’s lme packages. Thus, this study proposes and assesses the performance of a meta regression approach and makes comparison with methods based on sampling of the full data. Data We use both simulated and real data from a national cohort of Veterans with type 2 diabetes (n=890,394) which was created by linking multiple patient and administrative files resulting in a cohort with longitudinal data collected over 5 years. Methods and results The outcome of interest was mean annual HbA1c measured over a 5 years period. Using this outcome, we compared parameter estimates from the proposed random effects meta regression (REMR) with estimates based on simple random sampling and VISN (Veterans Integrated Service Networks) based stratified sampling of the full data. Our results indicate that REMR provides parameter estimates that are less likely to be biased with tighter confidence intervals when the VISN level estimates are homogenous. Conclusion When the interest is to fit REM in repeated measures data with very large sample size, REMR can be used as a good alternative. It leads to reasonable inference for both Gaussian and non-Gaussian responses if parameter estimates are homogeneous across VISNs.
Effectiveness of technology-assisted case management in low income adults with type 2 diabetes (TACM-DM): study protocol for a randomized controlled trial
Leonard E Egede, Joni L Strom, Jyotika Fernandes, Rebecca G Knapp, Adebola Rojugbokan
Trials , 2011, DOI: 10.1186/1745-6215-12-231
Abstract: We describe a four-year prospective, randomized clinical trial, which will test the effectiveness of technology-assisted case management in low income rural adults with T2DM. Two-hundred (200) male and female participants, 18 years of age or older and with an HbA1c ≥ 8%, will be randomized into one of two groups: (1) an intervention arm employing the innovative FORA system coupled with nurse case management or (2) a usual care group. Participants will be followed for 6-months to ascertain the effect of the interventions on glycemic control. Our primary hypothesis is that among indigent, rural adult patients with T2DM treated in FQHC's, participants randomized to the technology-assisted case management intervention will have significantly greater reduction in HbA1c at 6 months of follow-up compared to usual care.Results from this study will provide important insight into the effectiveness of technology-assisted case management intervention (TACM) for optimizing diabetes care in indigent, rural adult patients with T2DM treated in FQHC's.National Institutes of Health Clinical Trials Registry (http://ClinicalTrials.gov webcite identifier# NCT01373489According to the Centers for Disease Control and Prevention, as many as 1 in 3 United States adults will have diabetes by 2050 [1]. As of 2007, approximately 23.6 million Americans or 7.8% of the population have diabetes; nearly 6 million of which have not been diagnosed [2]. In 2007, 1.6 million new cases of diabetes were diagnosed in individuals 20 years of age and older [2]. Currently, 10.7% of all people in this age group have diabetes [2]. In adults, type 2 diabetes accounts for about 90-95% of all diagnosed cases of diabetes [2].South Carolina ranks 10th in cases of diagnosed diabetes compared to other states [3]. The prevalence of diabetes in SC is presently 9.6%, with an estimated 300,000-350,000 people in SC living with diabetes. As observed nationally, more women and non-white individuals residing in SC are affecte
Rationale and design: telephone-delivered behavioral skills interventions for African Americans with type 2 diabetes
Leonard E Egede, Joni L Strom, Valerie L Durkalski, Patrick D Mauldin, William P Moran
Trials , 2010, DOI: 10.1186/1745-6215-11-35
Abstract: We describe an ongoing four-year randomized clinical trial, using a 2 × 2 factorial design, which will test the efficacy of separate and combined telephone-delivered, diabetes knowledge/information and motivation/behavioral skills training interventions in high risk African Americans with poorly controlled T2DM (HbA1c ≥ 9%). Two-hundred thirty-two (232) male and female African-American participants, 18 years of age or older and with an HbA1c ≥ 9%, will be randomized into one of four groups for 12-weeks of phone interventions: (1) an education group, (2) a motivation/skills group, (3) a combined group or (4) a usual care/general health education group. Participants will be followed for 12-months to ascertain the effect of the interventions on glycemic control. Our primary hypothesis is that among African Americans with poorly controlled T2DM, patients randomized to the combined diabetes knowledge/information and motivation/behavioral skills training intervention will have significantly greater reduction in HbA1c at 12 months of follow-up compared to the usual care/general health education group.Results from this study will provide important insight into how best to deliver diabetes education and skills training in ethnic minorities and whether combined knowledge/information and motivation/behavioral skills training is superior to the usual method of delivering diabetes education for African Americans with poorly controlled T2DM.National Institutes of Health Clinical Trials Registry (ClinicalTrials.gov identifier# NCT00929838).Diabetes affects approximately 23.6 million people or 7.8% of the United States population [1]. Diabetes is associated with significant morbidity, mortality, increased health care utilization, and increased health care costs [1]. Diabetes is the leading cause of cardiovascular disease (CVD), strokes, blindness, and lower limb amputations [2]. It was the seventh leading cause of death listed on U.S. death certificates in 2006, and individuals wit
Marginalized Two Part Models for Generalized Gamma Family of Distributions
Delia C. Voronca,Mulugeta Gebregziabher,Valerie L. Durkalski,Lei Liu,Leonard E. Egede
Statistics , 2015,
Abstract: Positive continuous outcomes with a point mass at zero are prevalent in biomedical research. To model the point mass at zero and to provide marginalized covariate effect estimates, marginalized two part models (MTP) have been developed for outcomes with lognormal and log skew normal distributions. In this paper, we propose MTP models for outcomes from a generalized gamma (GG) family of distributions. In the proposed MTP-GG model, the conditional mean from a two-part model with a three-parameter GG distribution is parameterized to provide regression coefficients that have marginal interpretation. MTP-gamma and MTP-Weibull are developed as special cases of MTP-GG. We derive marginal covariate effect estimators from each model and through simulations assess their finite sample operating characteristics in terms of bias, standard errors, 95% coverage, and rate of convergence. We illustrate the models using data sets from The Medical Expenditure Survey (MEPS) and from a randomized trial of addictive disorders and we provide SAS code for implementation. The simulation results show that when the response distribution is unknown or mis-specified, which is usually the case in real data sets, the MTP-GG is preferable over other models.
Using quantile regression to investigate racial disparities in medication non-adherence
Mulugeta Gebregziabher, Cheryl P Lynch, Martina Mueller, Gregory E Gilbert, Carrae Echols, Yumin Zhao, Leonard E Egede
BMC Medical Research Methodology , 2011, DOI: 10.1186/1471-2288-11-88
Abstract: A retrospective cohort of 11,272 veterans with type 2 diabetes was assembled from Veterans Administration datasets from April 1996 to May 2006. The main outcome measure was MPR with quantile cutoffs Q1-Q4 taking values of 0.4, 0.6, 0.8 and 0.9. Quantile-regression (QReg) was used to model the association between MPR and race/ethnicity after adjusting for covariates. Comparison was made with commonly used ordinary-least-squares (OLS) and generalized linear mixed models (GLMM).Quantile-regression showed that Non-Hispanic-Black (NHB) had statistically significantly lower MPR compared to Non-Hispanic-White (NHW) holding all other variables constant across all quantiles with estimates and p-values given as -3.4% (p = 0.11), -5.4% (p = 0.01), -3.1% (p = 0.001), and -2.00% (p = 0.001) for Q1 to Q4, respectively. Other racial/ethnic groups had lower adherence than NHW only in the lowest quantile (Q1) of about -6.3% (p = 0.003). In contrast, OLS and GLMM only showed differences in mean MPR between NHB and NHW while the mean MPR difference between other racial groups and NHW was not significant.Quantile regression is recommended for analysis of data that are heterogeneous such that the tails and the central location of the conditional distributions vary differently with the covariates. QReg provides a comprehensive view of the relationships between independent and dependent variables (i.e. not just centrally but also in the tails of the conditional distribution of the dependent variable). Indeed, without performing QReg at different quantiles, an investigator would have no way of assessing whether a difference in these relationships might exist.Diabetes is a chronic debilitating illness that affects approximately 24 million people in the United States [1]. Medication adherence is an important component of good diabetes care and medication non-adherence is associated with poor glycemic control [2,3], increased health utilization [4,5], increased health care costs [6,7], and in
Rationale and design: telepsychology service delivery for depressed elderly veterans
Leonard E Egede, Christopher B Frueh, Lisa K Richardson, Ronald Acierno, Patrick D Mauldin, Rebecca G Knapp, Carl Lejuez
Trials , 2009, DOI: 10.1186/1745-6215-10-22
Abstract: We describe an ongoing four-year prospective, randomized clinical trial comparing the effectiveness of an empirically supported treatment for major depressive disorder, Behavioral Activation, delivered either via in-home videoconferencing technology ("Telepsychology") or traditional face-to-face services ("Same-Room"). Our hypothesis is that in-homeTelepsychology service delivery will be equally effective as the traditional mode (Same-Room). Two-hundred twenty-four (224) male and female elderly participants will be administered protocol-driven individual Behavioral Activation therapy for depression over an 8-week period; and subjects will be followed for 12-months to ascertain longer-term effects of the treatment on three outcomes domains: (1) clinical outcomes (symptom severity, social functioning); (2) process variables (patient satisfaction, treatment credibility, attendance, adherence, dropout); and (3) economic outcomes (cost and resource use).Results from the proposed study will provide important insight into whether telepsychology service delivery is as effective as the traditional mode of service delivery, defined in terms of clinical, process, and economic outcomes, for elderly patients with depression residing in rural areas without adequate access to mental health services.National Institutes of Health Clinical Trials Registry (ClinicalTrials.gov identifier# NCT00324701).People who live in rural areas experience significant disparities in health status and access to care compared to their urban counterparts [1,2]. Access to appropriate mental health care services represents a significant problem in many rural and remote areas and as the ageing population expands, this problem will intensify over the next several decades without innovative solutions [1-4]. There is growing awareness that specifically tailored treatment and service delivery strategies are frequently needed for different populations of adult consumers of mental health services [3,5]. Access
CKM Reach at Hadronic Colliders
Ulrik Egede
Physics , 2003,
Abstract: The analysis of the CKM parameters will take a leap forward when the hadronic B factories receive their first data. I describe the challenges faced by B-physics at hadronic colliders and the expected reach in specific channels for the LHCb, BTeV, ATLAS and CMS experiments.
Determination of the wrong sign decay rate D0 -> K+pi- and the sensitivity to D0-D0bar mixing
Ulrik Egede
Physics , 2001,
Abstract: The D0 meson can decay to the wrong sign K+pi- state either through a doubly Cabibbo suppressed decay or via mixing to the D0bar state followed by the Cabibbo favoured decay D0bar -> K+ pi-. We measure the rate of wrong sign decays relative to the Cabibbo favoured decay to (0.383 +- 0.044 +- 0.022)% and give our sensitivity to a mixing signal.
How important is psychoneuroimmunology?
Brian E. Leonard
Salud mental , 2008,
Abstract:
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