Racial/Ethnic Differences in Glycemic Control in Older Adults with Type 2 Diabetes: United States 2003-2014

The aim of this study was to determine whether racial differences in HbA1c persist in older adults (≥65 years) living with type 2 diabetes. Data from The National Health and Nutrition Examination Survey (NHANES) 2003-2014 were used to examine the association between HbA1c and older adults (≥65 years) over time. Compared to non-Hispanic Whites, Mexican Americans had the greatest difference in average HbA1c among minority groups, followed by those with unspecified/mixed ethnicities and non-Hispanic Blacks. In the adjusted linear model, racial minorities had a statistically significant relationship with HbA1c. There was no relationship between HbA1c and older age and insulin use. Trends in mean HbA1c over time increased for non-Hispanic Blacks and Mexican Americans and decreased for non-Hispanic Whites. The findings suggest that racial differences in HbA1c persist into older age and compared to non-Hispanic Whites, non-Hispanic Blacks and Mexican Americans are at an increased risk of morbidity, mortality, and disability due to high HbA1c. Furthermore, alternate measures of glycemic control may be needed to screen and manage T2DM in racial minorities

Use of Social Adaptability Index to Explain Self-Care and Diabetes Outcomes

Background: To examine whether the social adaptability index (SAI) alone or components of the index provide a better explanatory model for self-care and diabetes outcomes. Methods: Six hundred fifteen patients were recruited from two primary care settings. A series of multiple linear regression models were run to assess (1) associations between the SAI and diabetes self-care/outcomes, and (2) associations between individual SAI indicator variables and diabetes self-care/outcomes. Separate models were run for each self-care behavior and outcome. Two models were run for each dependent variable to compare associations with the SAI and components of the index. Results: The SAI has a significant association with the mental component of quality of life (0.23, p \u3c 0.01). In adjusted analyses, the SAI score did not have a significant association with any of the self-care behaviors. Individual components from the index had significant associations between self-care and multiple SAI indicator variables. Significant associations also exist between outcomes and the individual SAI indicators for education and employment. Conclusions: In this population, the SAI has low explanatory power and few significant associations with diabetes self-care/outcomes. While the use of a composite index to predict outcomes within a diabetes population would have high utility, particularly for clinical settings, this SAI lacks statistical and clinical significance in a representative diabetes population. Based on these results, the index does not provide a good model fit and masks the relationship of individual components to diabetes self-care and outcomes. These findings suggest that five items alone are not adequate to explain or predict outcomes for patients with type 2 diabetes

The Association between Food Insecurity, Glycemic Control, Self-Care, and Quality of Life in Adults with Type 2 Diabetes

Food insecurity is the inability to obtain adequate nutritious food. Therefore, the study assessed the relationship between food insecurity, glycemic control, self-care behaviors, and quality of life in adults with type 2 diabetes (T2DM). Cross sectional study of 356 adults with T2DM recruited from an academic medical center and a veterans affairs medical center. The independent predictor was food insecurity, and the outcomes were glycosylated hemoglobin A1c, self-care behaviors, and quality of life (QOL). Logistic regression was used to assess the independent factors associated with food insecurity. Multiple linear regression was used to assess the association between food insecurity and outcomes. Stata was used for the analyses. The majority (88%) was ≥50 years old, male (70%), and non-Hispanic black (55%). Thirty-five percent were food insecure. Compared to those who had 16 years of education were less likely to be food insecure (Odds ratio (OR) 0.25; 95% Confidence Interval (CI) 0.07, 0.92). Compared to those making \u3c$10,000, those with income levels of$20,000-$34,999 (OR 0.31; 95$35,000 (OR 0.15, 95% 0.06, 0.38) were less likely to be food insecure. In adjusted modeling, food insecurity was marginally associated with glycemic control (βeta coefficient =-0.41; 95% CI -0.85, 0.02), and not significantly associated with self-care behaviors or QOL. In this sample of adults with T2DM, food insecurity was significantly associated with education and income and marginally associated with glycemic control. Further research is needed to assess the relationship between these factors

Fitting parametric random effects models in very large data sets with application to VHA national data

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Data</p> <p>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.</p> <p>Methods and results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p

Prevalence and Correlates of Diagnosed and Undiagnosed Hypertension in the Indigenous Kuna Population of Panamá

Background: To determine the prevalence of hypertension and investigate sociodemographic correlates in an indigenous Kuna community living on the San Blas islands of Panama. Methods: Data was collected from adults using a paper-based survey using a cross sectional study design. Blood pressure was measured, and hypertension defined at two cut-points: 130/80 mmHg and 140/90 mmHg. Individuals with undiagnosed hypertension had a blood pressure measurement that indicated hypertension, however, the individual had not been told by a doctor they had hypertension. Whereas individuals with diagnosed hypertension had been told by a healthcare provider that they had hypertension. Univariate tests compared diagnosed and undiagnosed hypertension by sociodemographic categories and logistic regression models tested individual correlates adjusting for all sociodemographic factors. Results: Two hundred and eleven adult indigenous Kuna participated in the study. Overall prevalence of hypertension was 6.2% (95%CI:3.32–10.30) as defined by 140/90 mmHg, and 16.6% (95%CI:11.83–22.31) as defined by 130/80 mmHg. Hypertension was significantly higher in men (31.6, 95% CI:19.90–45.24, compared to 11.0, 95% CI:6.56–17.09). Individuals with low income were 3 times more likely to be hypertensive (OR = 3.13, 95% CI:1.02–9.60) and 3.5 times more likely to have undiagnosed hypertension (OR = 3.42, 95% CI:1.01–11.52); while those with moderate income were 6 times more likely to be hypertensive (OR = 7.37, 95% CI:1.76–30.90) compared to those who were poor. Conclusion: The prevalence of diagnosed and undiagnosed hypertension is higher in men and those with higher income. Investigating these factors remains vitally important in helping improve the health of the Kuna through targeted interventions to address chronic disease

Rationale and design: telepsychology service delivery for depressed elderly veterans

BACKGROUND: Older adults who live in rural areas experience significant disparities in health status and access to mental health care. "Telepsychology," (also referred to as "telepsychiatry," or "telemental health") represents a potential strategy towards addressing this longstanding problem. Older adults may benefit from telepsychology due to its: (1) utility to address existing problematic access to care for rural residents; (2) capacity to reduce stigma associated with traditional mental health care; and (3) utility to overcome significant age-related problems in ambulation and transportation. Moreover, preliminary evidence indicates that telepsychiatry programs are often less expensive for patients, and reduce travel time, travel costs, and time off from work. Thus, telepsychology may provide a cost-efficient solution to access-to-care problems in rural areas. METHODS: 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 inhome Telepsychology 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). DISCUSSION: 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. TRIAL REGISTRATION: National Institutes of Health Clinical Trials Registry (ClinicalTrials.gov identifier# NCT00324701)

MTPmle: A SAS Macro and Stata Programs for Marginalized Inference in Semi-Continuous Data

We develop a SAS macro and equivalent Stata programs that provide marginalized inference for semi-continuous data using a maximum likelihood approach. These software extensions are based on recently developed methods for marginalized two-part (MTP) models. Both the SAS and Stata extensions can fit simple MTP models for cross-sectional semi-continuous data. In addition, the SAS macro can fit random intercept models for longitudinal or clustered data, whereas the Stata programs can fit MTP models that account for subject level heteroscedasticity and for a complex survey design. Differences and similarities between the two software extensions are highlighted to provide a comparative picture of the available options for estimation, inclusion of random effects, convergence diagnosis, and graphical display. We provide detailed programming syntax, simulated and real data examples to facilitate the implementation of the MTP models for both SAS and Stata software users

Results from the first culturally tailored, multidisciplinary diabetes education in Lebanese adults with type 2 diabetes: effects on self-care and metabolic outcomes

Objective: Diabetes self-management education (DSME) is an essential component of lifestyle management needed for diabetes care. This pilot-study tested the effect of culturally-tailored education targeting diabetes selfcare on glycemia and cardiovascular risk factors of Lebanese with type 2 diabetes mellitus (T2DM) (n = 27; Age: 61 ± 10 yrs, 59% males, HbA1c: 8.98 ± 1.38%). Results: Diabetes self-care (Diet, Self-Monitoring Blood Glucose and foot care) improved after 6 months, which was reflected in a significant drop in glycemic levels (HbA1c:-0.5%; FPG: − 38 mg/dl), and cholesterol/HDL ratio (4.45 ± 1.39 vs. 4.06 ± 1.29). Waist circumference decreased at 6 months compared to 3 months (p < 0.05). This is the first effective culturally-tailored intervention that improved self-care, glycemic control, body adiposity and lipid profile of Lebanese with T2DM. Larger scale implementation with representative sample is warranted.This work was supported by the Lebanese American University-intramural fund granted to the first author. Roche Diagnostics Middle East provided screening tools for HbA1c levels and LifeScan MEA donated the glucometers and test strips. The companies did not have any role in the design of the study, data analyses, or decision to publish