Faculty Bibliography

BACKGROUND: Panel Management can expand prevention and chronic illness management beyond the office visit, but there is limited evidence for its effectiveness or guidance on how best to incorporate it into practice. OBJECTIVE: We aimed to test the effectiveness of incorporating panel management into clinical practice by incorporating Panel Management Assistants (PMAs) into primary care teams with and without panel management education. DESIGN: We conducted an 8-month cluster-randomized controlled trial of panel management for improving hypertension and smoking cessation outcomes among veterans. PATRICIPANTS: Twenty primary care teams from the Veterans Affairs New York Harbor were randomized to control, panel management support, or panel management support plus education groups. Teams included 69 clinical staff serving 8,153 hypertensive and/or smoking veterans. INTERVENTIONS: Teams assigned to the intervention groups worked with non-clinical Panel Management Assistants (PMAs) who monitored care gaps and conducted proactive patient outreach, including referrals, mail reminders and motivational interviewing by telephone. MAIN MEASURES: Measurements included mean systolic and diastolic blood pressure, proportion of patients with controlled blood pressure, self-reported quit attempts, nicotine replacement therapy (NRT) prescriptions, and referrals to disease management services. KEY RESULTS: Change in mean blood pressure, blood pressure control, and smoking quit rates were similar across study groups. Patients on intervention teams were more likely to receive NRT (OR = 1.4; 95 % CI 1.2-1.6) and enroll in the disease management services MOVE! (OR = 1.2; 95 % CI 1.1-1.6) and Telehealth (OR = 1.7, 95 % CI 1.4-2.1) than patients on control teams. CONCLUSIONS: Panel Management support for primary care teams improved process, but not outcome variables among veterans with hypertension and smoking. Incorporating PMAs into teams was feasible and highly valued by the clinical staff, but clinical impact may require a longer intervention.

PURPOSE: The authors performed a review of 30 Accelerating Change in Medical Education full grant submissions and an analysis of the health systems science (HSS)-related curricula at the 11 grant recipient schools to develop a potential comprehensive HSS curricular framework with domains and subcategories. METHOD: In phase 1, to identify domains, grant submissions were analyzed and coded using constant comparative analysis. In phase 2, a detailed review of all existing and planned syllabi and curriculum documents at the grantee schools was performed, and content in the core curricular domains was coded into subcategories. The lead investigators reviewed and discussed drafts of the categorization scheme, collapsed and combined domains and subcategories, and resolved disagreements via group discussion. RESULTS: Analysis yielded three types of domains: core, cross-cutting, and linking. Core domains included health care structures and processes; health care policy, economics, and management; clinical informatics and health information technology; population and public health; value-based care; and health system improvement. Cross-cutting domains included leadership and change agency; teamwork and interprofessional education; evidence-based medicine and practice; professionalism and ethics; and scholarship. One linking domain was identified: systems thinking. CONCLUSIONS: This broad framework aims to build on the traditional definition of systems-based practice and highlight the need for medical and other health professions schools to better align education programs with the anticipated needs of the systems in which students will practice. HSS will require a critical investigation into existing curricula to determine the most efficient methods for integration with the basic and clinical sciences.

Panel management, a set of tools and processes for proactively caring for patient populations, has potential to reduce morbidity and improve outcomes between office visits. We examined primary care staff's self-efficacy in implementing panel management, its correlates, and an intervention's impact on this self-efficacy. Primary care teams at two Veterans Health Administration (VA) hospitals were assigned to control or intervention conditions. Staff were surveyed at baseline and post-intervention, with a random subset interviewed post-intervention. Panel management self-efficacy was higher among staff participating in the panel management intervention. Self-efficacy was significantly correlated with sufficient training, aspects of team member interaction, and frequency of panel management use. Panel management self-efficacy was modest among primary care staff at two VA hospitals. Team level interventions may improve primary care staff's confidence in practicing panel management, with this greater confidence related to greater team involvement with, and use of panel management.

The Graduate Medical Education (GME) system in the United States (US) has garnered worldwide respect, graduating over 25,000 new physicians from over 8,000 residency and fellowship programs annually. GME is the portal of entry to medical practice and licensure in the US, and the pathway through which resident physicians develop the competence to practice independently and further develop their career plans. The number and specialty distribution of available GME positions shapes the overall composition of our national workforce; however, GME is failing to provide appropriate programs that support the delivery of our society's system of healthcare. This paper, prepared by the Health Policy Education Subcommittee of the Society of General Internal Medicine (SGIM) and unanimously endorsed by SGIM's Council, outlines a set of recommendations on how to reform the GME system to best prepare a physician workforce that can provide high quality, high value, population-based, and patient-centered health care, aligned with the dynamic needs of our nation's healthcare delivery system. These recommendations include: accurate workforce needs assessment, broadened GME funding sources, increased transparency of the use of GME dollars, and implementation of incentives to increase the accountability of GME-funded programs for the preparation and specialty selection of their program graduates.

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: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.

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.

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.