Faculty Bibliography

OBJECTIVES/OBJECTIVE:Edentulism and diabetes mellitus (DM) are frequently seen among older adults. However, the joint effect of edentulism and DM on mortality was understudied. We aim to examine the joint effect of edentulism and DM on all-cause mortality and to what extent the joint effect varies by race/ethnicity. METHODS:Analysis of US Health and Retirement Study (HRS) data (2006-2018) included 11,813 non-Hispanic Whites, 2216 non-Hispanic Blacks, and 1337 Hispanics aged ≥ 50 years old. Mortality data came from the National Death Index or HRS surveys. Edentulism was self-reported and DM was determined by self-reported diagnosis, medication use, or glycosylated hemoglobin. Cox proportional-hazard models with inverse probability treatment weighting were applied. RESULTS:During mean follow-up of 9.6 years, 2874 Whites, 703 Blacks, and 441 Hispanics died. DM was associated with higher mortality across all groups (Whites: HR = 1.43, 95% CI = 1.25-1.64; Blacks: HR = 1.62, 95% CI = 1.28-2.04; Hispanics: HR = 1.46, 95% CI = 1.07-1.99). However, edentulism predicted higher mortality only in Whites (HR = 1.65, 95% CI = 1.51-1.80). Having both conditions showed highest mortality risk in all groups (Whites: HR = 2.31, 95% CI = 1.56-3.42; Blacks: HR = 1.94, 95% CI = 1.45-2.59; Hispanics: HR = 1.77, 95% CI = 1.16-2.70), with a significant additive interaction observed only in Whites (relative excess risk due to interaction = 0.22, p < 0.05). CONCLUSIONS:DM and edentulism pose an additive risk for mortality in Whites, and there are racial/ethnic differences in edentulism-related mortality.

OBJECTIVE:To investigate associations of age at type 2 diabetes (T2D) detection with HbA1c. RESEARCH DESIGN AND METHODS/METHODS:We examined associations of early-onset T2D (age of detection 20-39 years) and age at T2D detection with HbA1c in Veterans with incident T2D (1/1/2008-12/31/2016; n = 851,302). RESULTS:About 3 % (n = 22,649) of men and 11 % (n = 5,117) of women had early-onset T2D. Compared to men with later-onset T2D, men with early-onset T2D had a 0.71 (95 %CI 0.69,0.73) greater HbA1c after detection, a 0.78 (95 %CI 0.77,0.80) greater mean HbA1c, and a 0.05 (95 %CI 0.04,0.05) greater coefficient of variation (CV). Each 10-year decrease in age was associated with greater HbA1c after detection, mean HbA1c, and HbA1c CV (β 0.21 (95 %CI 0.21,0.21), 0.25 (95 %CI 0.24,0.25), 0.02 (95 %CI 0.02,0.02), respectively). In women, early-onset T2D was associated with a 0.14 (95 %CI -0.18,-0.10) lower HbA1c after detection and a 0.01 greater CV (95 %CI 0.01,0.01). Each ten-year decrease in age was associated with greater mean HbA1c and CV (β 0.09 (95 %CI 0.08,0.10); 0.01 (95 %CI 0.01,0.01), respectively). CONCLUSION/CONCLUSIONS:Among male Veterans, early-onset T2D was associated with greater HbA1c and greater HbA1c variability. Similar results were seen among women but of lesser magnitude. Longitudinal research examining sex-specific impacts of early-onset T2D on clinical course is needed.

BACKGROUND:The United States Preventive Services Task Force (USPSTF) provides age-based recommendations for prostate cancer screening. Such a single-criterion strategy can not only miss aggressive cancers that occur before the designated cut-off age, but also over-screen men whose cancers occur at a much older age. A multi-factorial model incorporating a wide spectrum of medical history may offer more accurate predictions. Such cancer prediction models may excel by incorporating diverse medical histories, including causal and non-causal conditions, as well as their chronological relationship with the onset of cancer. OBJECTIVE: This study aims to develop an AI (machine learning)-driven predictive model for prostate cancer based on patients' medical history and compare its performance with traditional age-based criteria. METHODS: database. A binary indicator for a prostate cancer diagnosis was established using SNOMED codes. Subsequently, a Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression model was employed to examine the relationship between all prior health conditions and the cancer indicator, thereby identifying the most predictive features. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUROC) and McFadden's R². RESULTS:The EHR-based model achieved a 10-fold cross-validated McFadden's R² of 0.36, significantly outperforming a model based on USPSTF eligibility criteria, which had an R² of 0.20. Validation using AUROC further demonstrated that the proposed model outperformed current screening criteria in terms of both sensitivity and specificity. CONCLUSION/CONCLUSIONS:This study highlights the potential of personalized screening strategies and demonstrates that AI-driven prediction models based on EHR data can predict prostate cancer with better accuracy than existing age-based guidelines through non-invasive means. Such approaches may help reduce invasive diagnostic procedures due to unnecessary screening and improve early detection by focusing diagnostic efforts on those most at risk.

OBJECTIVE:This study examined associations between access to leisure-time physical activity (LTPA) facilities and parks and repeated measures of glycated hemoglobin (A1C) over time, using follow-up tests among United States Veterans with newly diagnosed type-2 diabetes (T2D). METHODS:Data were analyzed from 274,463 patients in the Veterans Administration Diabetes Risk cohort who were newly diagnosed with T2D between 2008 and 2018 and followed through 2023. Generalized estimating equations with a logit link function and binomial logistic regression were used to examine associations. RESULTS:Patients were on average 60.5 years of age, predominantly male (95.0 %) and white (66.9 %), and had an average of 11.7 A1C tests during the study follow-up period. In high- and low-density urban communities, a one-unit higher LTPA facility density score was associated with 1 % and 3 % greater likelihood of in-range A1C tests during follow-up, respectively, but no association was observed among patients living in suburban/small town and rural communities. Across community types, closer park distance was not associated with subsequent greater odds of in-range A1C tests. Unexpectedly, in low-density urban areas, the likelihood of in-range A1C tests was 1 % lower at farther park distances. CONCLUSIONS:These results suggest that broader access to LTPA facilities, but not park proximity, may contribute in small ways to maintaining glycemic control after T2D diagnosis in urban communities. Tailored interventions may be needed to promote patients' effective use of LTPA facilities and parks.

Prior studies suggest that diabetes is associated with severe outcomes following COVID-19. However, most research has focused on early phases of the COVID-19 pandemic, and less is known about changing diabetes-associated risks over time. We constructed a retrospective cohort of U.S. Veterans with documented COVID-19 between March 2020 and August 2023 (N = 426,170). We used Poisson regression models to estimate relative risks of 60-day mortality following COVID-19 among Veterans with and without diabetes, incorporating demographic and clinical covariates, as well as weights to address unequal probabilities of selection into the sample. We then incorporated interaction terms representing six-month time windows and plotted predicted mortality risks over time. To contextualize risk estimates, we repeated the analysis among a cohort of Veterans without documented COVID-19. Diabetes was associated with overall higher risk of 60-day mortality following COVID-19 (RR = 1.21, 95% CI = 1.17-1.26). Mortality risks attenuated over time and converged with risks observed among Veterans without COVID-19 by March-August 2022. Results suggest that post-COVID-19 mortality risks associated with diabetes may have attenuated over time. Mechanisms underlying the attenuation of mortality risks were beyond the scope of the paper, however, future studies can potentially shed light on the contributions of population immunity (driven by previous infection or vaccination status), changing treatment patterns, and other factors to time-varying mortality risks following COVID-19 among individuals with diabetes.

BACKGROUND:Hypertension (HTN) is a critical public health concern that disproportionately impacts racial/ethnic minorities. The recent COVID-19 pandemic spurred rapid adoption of virtual HTN treatment programs such as remote patient monitoring programs (RPM), including among minority populations. However, it is unclear how utilization patterns differ across racial/ethnic groups and what the implications are for HTN outcomes. OBJECTIVE:The present study examines whether the association between RPM utilization and uncontrolled BP differs by race/ethnicity among hypertensive patients enrolled in an RPM program. METHODS:This study includes an urban sample of HTN patients who were 18 ≥ years old who have been in their RPM programs for three consecutive months or longer. Our primary exposure measures are three widely used dichotomized RPM engagement metrics and uncontrolled BP outcomes were dichotomized as BP ≥ 140/90 and ≥ 130/80. We tested for effect modification by race/ethnicity across RPM utilization variables using multivariable logistic regression models. RESULTS:Of 2920 participants, 59% were females, 37% were ≥ 65 years old, and Hispanic patients were the most represented race/ethnicity group (39%). Percentage-uncontrolled was 25% non-Hispanic Black, 21% Hispanic, and 20% among non-Hispanic White patients. Compared to non-Hispanic White patients with high RPM utilization, patients with no BP transmission had higher odds of uncontrolled BP: White (OR=1.72; 95% CI: 1.07-2.75), Black (OR=2.11; 95% CI: 1.32-3.39), and Other race (OR=2.36; 95% CI: 1.41-3.96). Similar patterns were observed for low clinician interactions and low portal use. CONCLUSION/CONCLUSIONS:Disparities in RPM utilization and BP outcomes in our study parallel reported inequities in digital technology utilization and uncontrolled BP in the U.S. Future studies should aim to understand how utilization trends among various vulnerable populations influence HTN outcomes. Such findings may help inform efforts aimed at streamlining access and utilization of RPM to reduce utilization disparities and promote better BP control.

OBJECTIVE:Racial/ethnic disparities in glycemic control among non-Hispanic Black (NHB) and non-Hispanic White (NHW) veterans with type 2 diabetes (T2D) have been reported. This study examined trends in early glycemic control by race/ethnicity to understand how disparities soon after T2D diagnosis have changed between 2008 and 2019 among cohorts of U.S. veterans with newly diagnosed T2D. RESEARCH DESIGN AND METHODS/METHODS:We estimated the annual percentage of early glycemic control (average A1C <7%) in the first 5 years after diagnosis among 837,023 veterans (95% male) with newly diagnosed T2D in primary care. We compared early glycemic control by racial/ethnic group among cohorts defined by diagnosis year (2008-2010, 2011-2013, 2014-2016, and 2017-2018) using mixed-effects models with random intercepts. We estimated odds ratios of early glycemic control comparing racial/ethnic groups with NHW, adjusting for age, sex, and years since diagnosis. RESULTS:The average annual percentage of veterans who achieved early glycemic control during follow-up was 73%, 72%, 72%, and 76% across the four cohorts, respectively. All racial/ethnic groups were less likely to achieve early glycemic control compared with NHW veterans in the 2008-2010 cohort. In later cohorts, NHB and Hispanic veterans were more likely to achieve early glycemic control; however, Hispanic veterans were also more likely to have an A1C ≥9% within 5 years in all cohorts. Early glycemic control disparities for non-Hispanic Asian, Native Hawaiian/Pacific Islander, and American Indian/Alaska Native veterans persisted in cohorts until the 2017-2018 cohort. CONCLUSIONS:Overall early glycemic control trends among veterans with newly diagnosed T2D have been stable since 2008, but trends differed by racial/ethnic groups and disparities in very poor glycemic control were still observed. Efforts should continue to minimize disparities among racial/ethnic groups.

BACKGROUND:Over a third of US adults carry a diagnosis of prediabetes, 70% of whom may progress to type 2 diabetes mellitus ("diabetes"). Community health workers (CHWs) can help patients undertake healthy behavior to prevent diabetes. However, there is limited guidance to integrate CHWs in primary care, specifically to address CHWs' dual clinic-based and community-oriented role. OBJECTIVE:Using evidence from CHWs' adaptations of a diabetes-prevention intervention in safety-net hospitals in New York City, we examine the nature, intent, and possible consequences of CHWs' actions on program fidelity. We propose strategies for integrating CHWs in primary care. DESIGN/METHODS:Case study drawing on the Model for Adaptation Design and Impact (MADI) to analyze CHWs' actions during implementation of CHORD (Community Health Outreach to Reduce Diabetes), a cluster-randomized pragmatic trial (2017-2022) at Manhattan VA and Bellevue Hospital. PARTICIPANTS/METHODS:CHWs and clinicians in the CHORD study, with a focus in this analysis on CHWs. APPROACH/METHODS:Semi-structured interviews and focus group discussion with CHWs (n=4); semi-structured interviews with clinicians (n=17). Interpretivist approach to explain CHWs' adaptations using a mix of inductive and deductive analysis. KEY RESULTS/RESULTS:CHWs' adaptations extended the intervention in three ways: by extending social assistance, healthcare access, and operational tasks. The adaptations were intended to improve fit, reach, and retention, but likely had ripple effects on implementation outcomes. CHWs' focus on patients' complex social needs could divert them from judiciously managing their caseload. CONCLUSIONS:CHWs' community knowledge can support patient engagement, but overextension of social assistance may detract from protocolized health-coaching goals. CHW programs in primary care should explicitly delineate CHWs' non-health support to patients, include multiprofessional teams or partnerships with community-based organizations, establish formal communication between CHWs and clinicians, and institute mechanisms to review and iterate CHWs' work to resolve challenges in their community-oriented role.