Faculty Bibliography

BACKGROUND:While Type 2 Diabetes Mellitus (T2DM) prevalence is increasing among younger individuals, few studies have examined how age at T2DM diagnosis relates to dementia risk in diabetic populations. We aimed to investigate the association between age at T2DM diagnosis and subsequent dementia risk, and to determine whether obesity moderates this relationship. METHODS:We conducted a prospective cohort study using data from the Health and Retirement Study (2002-2016) matched with its 2003 Diabetes Mail-Out Survey. The study included 1,213 dementia-free adults aged ≥50 with diagnosed T2DM. Primary exposures were age at T2DM diagnosis (categorized as <50, 50-59, 60-69, and ≥70 years) and obesity status (BMI ≥30 kg/m2). The outcome was incident dementia, assessed using the Telephone Interview for Cognitive Status. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs), adjusting for sociodemographic factors, health behaviors, health status, and diabetes medication use. RESULTS:Over a median follow-up of 10 (interquartile range, 6-14) years, 216 (17.8%) participants developed dementia. Compared to participants diagnosed with T2DM at age ≥70 years, those diagnosed at younger ages had increased dementia risk: HR 1.70 (95% CI, 1.03-2.80) for 60-69 years, 1.72 (95% CI, 1.06-2.79) for 50-59 years, and 1.90 (95% CI, 1.14-3.18) for <50 years. Obesity significantly moderated this relationship, with obese individuals diagnosed with T2DM before age 50 showing the highest dementia risk (HR 3.05; 95% CI 1.23-7.56) compared to non-obese individuals diagnosed at ≥50 years. CONCLUSIONS:Younger age at diagnosis of T2DM was significantly associated with a higher risk of dementia, particularly among individuals with obesity. Interventions specifically targeting obesity may be more effective in preventing dementia for adults with a younger onset of T2DM.

OBJECTIVES:This study evaluated whether a range of demographic, social and geographic factors had an influence on glycaemic control longitudinally after an initial diagnosis of diabetes. DESIGN, SETTING AND PARTICIPANTS:We used the US Veterans Administration Diabetes Risk national cohort to track glycaemic control among patients 20-79-year old with a new diagnosis of type 2 diabetes. PRIMARY OUTCOME AND METHODS:We modelled associations between glycaemic control at follow-up clinical assessments and geographic factors including neighbourhood race/ethnicity, socioeconomic, land use and food environment measures. We also adjusted for individual demographics, comorbidities, haemoglobin A1c (HbA1c) at diagnosis and duration of follow-up. These factors were analysed within strata of community type: high-density urban, low-density urban, suburban/small town and rural areas. RESULTS:We analysed 246 079 Veterans who developed a new type 2 diabetes diagnosis in 2008-2018 and had at least 2 years of follow-up data available. Across all community types, we found that lower baseline HbA1c and female sex were strongly associated with a higher likelihood of within-range HbA1c at follow-up. Surprisingly, patients who were older or had more documented comorbidities were more likely to have within-range follow-up HbA1c results. While there was variation by community type, none of the geographic measures analysed consistently demonstrated significant associations with glycaemic control across all community types.

Background A number of studies have shown an association between social determinants of health and the emergence of obesity and diabetes, but whether the relationship is causal is not clear. Objective To test whether social, environmental, and medical determinants directly or indirectly affect population-level diabetes prevalence after controlling for mediator-mediator interactions. Methods Data were obtained from the CDC and supplemented with nine other data sources for 3,109 US counties. The dependent variable was the prevalence of diabetes in 2017. Independent variables were a given county's 30 social, environmental, and medical characteristics in 2015 and 2016. A network multiple mediation analysis was conducted. First, we used Least Absolute Shrinkage and Selection Operator (LASSO) regression to relate the 2017 diabetes rate in each county to 30 predictors measured in 2016, identifying statistically significant and robust predictors as the mediators within the network model and as direct determinants of 2017 diabetes. Second, each of the direct causes of diabetes was taken as a new response variable and LASSO-regressed on the same 30 independent variables measured in 2015, identifying the indirect (mediated) causes of diabetes. Subsequently, these direct and indirect predictors were used to construct a network model. The completed network was then employed to estimate the direct and mediated impact of variables on diabetes. Results For 2017 diabetes rates, 63% of the variation was explained by five variables measured in 2016: the percentage of residents who were (1) obese, (2) African American, (3) physically inactive, (4) in poor health condition, and (5) had a history of diabetes. These five direct predictors, measured in 2016, mediated the effect of indirect variables measured in 2015, including the percentage of residents who were (1) Hispanic, (2) physically distressed, (3) smokers, (4) living with children in poverty, (5) experiencing limited access to healthy foods, and (6) had low income. Conclusion All of the direct predictors of diabetes prevalence, except the percentage of residents who were African American, were medical conditions potentially influenced by lifestyles. Counties characterized by higher levels of obesity, inactivity, and poor health conditions exhibited increased diabetes rates in the following year. The impact of social determinants of illness, such as low income, children in poverty, and limited access to healthy foods, had an indirect effect on the health of residents and, consequently, increased the prevalence of diabetes.

BACKGROUND:It is critical to assess implementation fidelity of evidence-based interventions and factors moderating fidelity, to understand the reasons for their success or failure. However, fidelity and fidelity moderators are seldom systematically reported. The study objective was to conduct a concurrent implementation fidelity evaluation and examine fidelity moderators of CHORD (Community Health Outreach to Reduce Diabetes), a pragmatic, cluster-randomized, controlled trial to test the impact of a Community Health Workers (CHW)-led health coaching intervention to prevent incident type 2 Diabetes Mellitus in New York (NY). METHODS:We applied the Conceptual Framework for Implementation Fidelity to assess implementation fidelity and factors moderating it across the four core intervention components: patient goal setting, education topic coaching, primary care (PC) visits, and referrals to address social determinants of health (SDH), using descriptive statistics and regression models. PC patients with prediabetes receiving care from safety-net patient-centered medical homes (PCMHs) at either, VA NY Harbor or at Bellevue Hospital (BH) were eligible to be randomized into the CHW-led CHORD intervention or usual care. Among 559 patients randomized and enrolled in the intervention group, 79.4% completed the intake survey and were included in the analytic sample for fidelity assessment. Fidelity was measured as coverage, content adherence and frequency of each core component, and the moderators assessed were implementation site and patient activation measure. RESULTS:Content adherence was high for three components with nearly 80.0% of patients setting ≥ 1 goal, having ≥ 1 PC visit and receiving ≥ 1 education session. Only 45.0% patients received ≥ 1 SDH referral. After adjusting for patient gender, language, race, ethnicity, and age, the implementation site moderated adherence to goal setting (77.4% BH vs. 87.7% VA), educational coaching (78.9% BH vs. 88.3% VA), number of successful CHW-patient encounters (6 BH vs 4 VA) and percent of patients receiving all four components (41.1% BH vs. 25.7% VA). CONCLUSIONS:The fidelity to the four CHORD intervention components differed between the two implementation sites, demonstrating the challenges in implementing complex evidence-based interventions in different settings. Our findings underscore the importance of measuring implementation fidelity in contextualizing the outcomes of randomized trials of complex multi-site behavioral interventions. TRIAL REGISTRATION:The trial was registered with ClinicalTrials.gov on 30/12/2016 and the registration number is NCT03006666 .

INTRODUCTION:Inequitable access to leisure-time physical activity (LTPA) resources may explain geographic disparities in type 2 diabetes (T2D). We evaluated whether the neighborhood socioeconomic environment (NSEE) affects T2D through the LTPA environment. RESEARCH DESIGN AND METHODS:We conducted analyses in three study samples: the national Veterans Administration Diabetes Risk (VADR) cohort comprising electronic health records (EHR) of 4.1 million T2D-free veterans, the national prospective cohort REasons for Geographic and Racial Differences in Stroke (REGARDS) (11 208 T2D free), and a case-control study of Geisinger EHR in Pennsylvania (15 888 T2D cases). New-onset T2D was defined using diagnoses, laboratory and medication data. We harmonized neighborhood-level variables, including exposure, confounders, and effect modifiers. We measured NSEE with a summary index of six census tract indicators. The LTPA environment was measured by physical activity (PA) facility (gyms and other commercial facilities) density within street network buffers and population-weighted distance to parks. We estimated natural direct and indirect effects for each mediator stratified by community type. RESULTS:The magnitudes of the indirect effects were generally small, and the direction of the indirect effects differed by community type and study sample. The most consistent findings were for mediation via PA facility density in rural communities, where we observed positive indirect effects (differences in T2D incidence rates (95% CI) comparing the highest versus lowest quartiles of NSEE, multiplied by 100) of 1.53 (0.25, 3.05) in REGARDS and 0.0066 (0.0038, 0.0099) in VADR. No mediation was evident in Geisinger. CONCLUSIONS:PA facility density and distance to parks did not substantially mediate the relation between NSEE and T2D. Our heterogeneous results suggest that approaches to reduce T2D through changes to the LTPA environment require local tailoring.

While every disease could affect a patient's prognosis, published studies continue to use indices that include a selective list of diseases to predict prognosis, which may limit its accuracy. This paper compares 6-month mortality predicted by a multimorbidity index (MMI) that relies on all diagnoses to the Deyo version of the Charlson index (DCI), a popular index that utilizes a selective set of diagnoses. In this retrospective cohort study, we used data from the Veterans Administration Diabetes Risk national cohort that included 6,082,018 diabetes-free veterans receiving primary care from January 1, 2008 to December 31, 2016. For the MMI, 7805 diagnoses were assigned into 19 body systems, using the likelihood that the disease will increase risk of mortality. The DCI used 17 categories of diseases, classified by clinicians as severe diseases. In predicting 6-month mortality, the cross-validated area under the receiver operating curve for the MMI was 0.828 (95% confidence interval of 0.826-0.829) and for the DCI was 0.749 (95% confidence interval of 0.748-0.750). Using all available diagnoses (MMI) led to a large improvement in accuracy of predicting prognosis of patients than using a selected list of diagnosis (DCI).

BACKGROUND:While emerging studies suggest that the COVID-19 pandemic caused disruptions in routine healthcare utilization, the full impact of the pandemic on healthcare utilization among diverse group of patients with type 2 diabetes is unclear. The purpose of this study is to examine trends in healthcare utilization, including in-person and telehealth visits, among U.S. veterans with type 2 diabetes before, during and after the onset of the COVID-19 pandemic, by demographics, pre-pandemic glycemic control, and geographic region. METHODS:We longitudinally examined healthcare utilization in a large national cohort of veterans with new diabetes diagnoses between January 1, 2008 and December 31, 2018. The analytic sample was 733,006 veterans with recently-diagnosed diabetes, at least 1 encounter with veterans administration between March 2018-2020, and followed through March 2021. Monthly rates of glycohemoglobin (HbA1c) measurements, in-person and telehealth outpatient visits, and prescription fills for diabetes and hypertension medications were compared before and after March 2020 using interrupted time-series design. Log-linear regression model was used for statistical analysis. Secular trends were modeled with penalized cubic splines. RESULTS:In the initial 3 months after the pandemic onset, we observed large reductions in monthly rates of HbA1c measurements, from 130 (95%CI,110-140) to 50 (95%CI,30-80) per 1000 veterans, and in-person outpatient visits, from 1830 (95%CI,1640-2040) to 810 (95%CI,710-930) per 1000 veterans. However, monthly rates of telehealth visits doubled between March 2020-2021 from 330 (95%CI,310-350) to 770 (95%CI,720-820) per 1000 veterans. This pattern of increases in telehealth utilization varied by community type, with lowest increase in rural areas, and by race/ethnicity, with highest increase among non-hispanic Black veterans. Combined in-person and telehealth outpatient visits rebounded to pre-pandemic levels after 3 months. Despite notable changes in HbA1c measurements and visits during that initial window, we observed no changes in prescription fills rates. CONCLUSIONS:Healthcare utilization among veterans with diabetes was substantially disrupted at the onset of the pandemic, but rebounded after 3 months. There was disparity in uptake of telehealth visits by geography and race/ethnicity.

The objective of this study was to compare unannounced standardized patient (USP) and patient reports of care. Patient satisfaction surveys and USP checklist results collected at an urban, public hospital were compared to identify items included in both surveys. Qualitative commentary was reviewed to better understand USP and patient satisfaction survey data. Analyses included χ² and Mann-Whitney U test. Patients provided significantly higher ratings on 10 of the 11 items when compared to USPs. USPs may provide a more objective perspective on a clinical encounter than a real patient, reinforcing the notion that real patients skew overly positive or negative.

COVID-19 symptom definitions rarely include symptom severity. We collected daily nasal swabs and symptom diaries from contacts of SARS-CoV-2 cases. Requiring ≥1 moderate or severe symptom reduced sensitivity to predict SARS-CoV-2 shedding from 60.0% (CI: 52.9-66.7%) to 31.5% (CI: 25.7-38.0%), but increased specificity from 77.5% (CI:75.3-79.5%) to 93.8% (CI: 92.7-94.8%).

INTRODUCTION/BACKGROUND:The aim of this research was to assess the association between inflammation and oral health and diabetes, as well as the mediating role of oral hygiene practice in this association. METHODS:Data were from the 2009-2010 National Health and Nutrition Examination Survey. The analytical sample consisted of 2,191 respondents aged 50 and older. Poor oral health was clinically defined by significant tooth loss (STL) and periodontal disease (PD). Diabetes mellitus (DM) was determined by glycemic levels. The outcome variable was serum C-reactive protein (CRP) level, dichotomised as ≥1 mg/dL (elevated CRP) vs <1 mg/dL (not elevated CRP). Two path models, one using STL and DM as the independent variable, the other using PD and DM as the independent variable, were estimated to assess the direct effects of having poor oral health and DM on elevated CRP and the mediating effects of dental flossing. RESULTS:In path model 1, individuals having both STL and DM (adjusted odds ratio [AOR], 1.92; 95% confidence interval [CI], 1.30-2.82) or having STL alone (AOR, 2.30; 95% CI, 1.68-3.15) were more likely to have elevated CRP than those with neither STL nor DM; dental flossing (AOR, 0.92, 95% CI, 0.88-0.96) was associated with lower risk of elevated CRP. In path model 2, no significant association was found between having both PD and DM and elevated CRP; dental flossing (AOR, 0.91; 95% CI:, 0.86-0.94) was associated with lower risk of elevated CRP. CONCLUSIONS:Findings from this study highlight the importance of improving oral health and oral hygiene practice to mitigate inflammation. Further research is needed to assess the longer-term effects of reducing inflammation.