Willingness to take less medication for type 2 diabetes among older patients: The Diabetes & Aging Study.

BACKGROUND: To examine the willingness of older patients to take less diabetes medication (de-intensify) and to identify characteristics associated with willingness to de-intensify treatment. METHODS: Survey conducted in 2019 in an age-stratified, random sample of older (65-100 years) adults with diabetes on glucose-lowering medications in the Kaiser Permanente Northern California Diabetes Registry. We classified survey responses to the question: "I would be willing to take less medication for my diabetes" as willing, neutral, or unwilling to de-intensify. Willingness to de-intensify treatment was examined by several clinical characteristics, including American Diabetes Association (ADA) health status categories used for individualizing glycemic targets. Analyses were weighted to account for over-sampling of older individuals. RESULTS: A total of 1337 older adults on glucose-lowering medication(s) were included (age 74.2 ± 6.0 years, 44% female, 54.4% non-Hispanic white). The proportions of participants willing, neutral, or unwilling to take less medication were 51.2%, 27.3%, and 21.5%, respectively. Proportions of willing to take less medication varied by age (65-74 years: 54.2% vs. 85+ years: 38.5%) and duration of diabetes (0-4 years: 61.0% vs. 15+ years: 44.2%), both p < 0.001. Patients on 1-2 medications were more willing to take less medication(s) compared with patients on 10+ medications (62.1% vs. 46.6%, p = 0.03). Similar proportions of willingness to take less medications were seen across ADA health status, and HbA1c. Willingness to take less medication(s) was similar across survey responses to questions about patient-clinician relationships. CONCLUSIONS: Clinical guidelines suggest considering treatment de-intensification in older patients with longer duration of diabetes, yet patients with these characteristics are less likely to be willing to take less medication(s).

Associations of U.S. hospital closure (2007-2018) with area socioeconomic disadvantage and racial/ethnic composition.

PURPOSE: To examine whether hospital closure is associated with high levels of area socioeconomic disadvantage and racial/ethnic minority composition. METHODS: Pooled cross-sectional analysis (2007-2018) of 6467 U.S. hospitals from the American Hospital Association's Annual Survey, comparing hospital population characteristics of closed hospitals to all remaining open hospitals. We used multilevel mixed-effects logistic regression models to assess closure as a function of population characteristics, including area deprivation index ([ADI], a composite measure of socioeconomic disadvantage), racial/ethnic composition, and rural classification, nesting hospitals within hospital service areas (HSAs) and hospital referral regions. Secondary analyses examined public or private hospital type. RESULTS: Overall, 326 (5.0%) of 6467 U.S. hospitals closed during the study period. In multivariable models, hospitals in HSAs with a higher burden of socioeconomic disadvantage (per 10% above median ADI ZIP codes, AOR 1.05; 95% CI, 1.01-1.09) and Black Non-Hispanic composition (highest quartile, AOR 4.03; 95% CI, 2.62-6.21) had higher odds of closure. We did not observe disparities in closure by Hispanic/Latino composition or rurality. Disparities persisted for Black Non-Hispanic communities, even among HSAs with the lowest burden of disadvantage. CONCLUSIONS: Disproportionate hospital closure in communities with higher socioeconomic disadvantage and Black racial composition raises concerns about unequal loss of healthcare resources in the U.S.

Individualizing Care for Older Adults With Diabetes Amid the Revolution in Pharmacotherapy.

This JAMA Network Insights reassesses the approach to caring for older adults with diabetes in the context of newly available pharmacologic agents.

Impact of 2009 American Recovery and Reinvestment Act (ARRA) health center investments on disadvantaged neighborhoods after recession.

BACKGROUND: Federally qualified health centers (FQHCs) are integral to the U.S. healthcare safety net and uniquely situated in disadvantaged neighborhoods. The 2009 American Recovery and Reinvestment Act (ARRA) invested $2 billion in FQHC stimulus during the Great Recession; but it remains unknown whether this investment was associated with extended benefits for disadvantaged neighborhoods. METHODS: We used a propensity-score matched longitudinal design (2008-2012) to examine whether the 2009 ARRA FQHC investment was associated with local jobs and establishments recovery in FQHC neighborhoods. Job change data were obtained from the Longitudinal Employer-Household Dynamics (LEHD) survey and calculated as an annual rate per 1,000 population. Establishment change data were obtained from the National Neighborhood Data Archive (NaNDA) and calculated as an annual rate per 10,000 population. Establishment data included 4 establishment types: healthcare services, eating/drinking places, retail establishments, and grocery stores. Fixed effects were used to compare annual rates of jobs and establishments recovery between ARRA-funded FQHC census tracts and a matched control group. RESULTS: Of 50,381 tracts, 2,223 contained ≥ 1 FQHC that received ARRA funding. A higher proportion of FQHC tracts had an extreme poverty designation (11.6% vs. 5.4%), high unemployment rate (45.4% vs. 30.3%), and > 50% minority racial/ethnic composition (48.1% vs. 36.3%). On average, jobs grew at an annual rate of 3.84 jobs per 1,000 population (95% CI: 3.62,4.06). In propensity-score weighted models, jobs in ARRA-funded tracts grew at a higher annual rate of 4.34 per 1,000 (95% CI: 2.56,6.12) relative to those with similar social vulnerability. We observed persistent decline in non-healthcare establishments (-1.35 per 10,000; 95% CI: -1.68,-1.02); but did not observe decline in healthcare establishments. CONCLUSIONS: Direct funding to HCs may be an effective strategy to support healthcare establishments and some jobs recovery in disadvantaged neighborhoods during recession, reinforcing the important multidimensional roles HCs play in these communities. However, HCs may benefit from additional investments that target upstream determinants of health to mitigate uneven recovery and neighborhood decline.

Moderation of associations between weight discrimination and diabetes status by psychosocial factors.

Weight discrimination has adverse effects on health that include increasing the risk factors for developing type 2 diabetes. Preliminary evidence suggests a positive association between weight discrimination and diagnosed diabetes; however, it is unknown whether psychosocial resources may buffer this association. In logistic regressions stratified by gender, we examined links between weight discrimination and diabetes among a nationally representative sample of U.S. adults (the National Social Life, Health, and Aging Project; N = 2,794 adults age 50 and older in 2015-16). We also tested the extent to which trait-resilience and social support from a spouse/partner, family, and friends buffered any observed association. We adjusted for known predictors of diabetes (age, race/ethnicity, Body Mass Index) and conducted sensitivity analyses restricted to men and women with obesity. Net of covariates, in the overall sample, weight discrimination was associated with significantly greater odds of having ever had diabetes among women (OR = 2.00, 95% CI [1.15, 3.47]), but not men. Among women with obesity, weight discrimination was only significantly associated with greater odds of diabetes for those with low resilience (OR = 1.84, 95% CI [1.01, 3.35]). Among men overall, weight discrimination was associated with lower odds of diabetes for those with high family support (OR = 0.03, 95% CI [0.003, 0.25]) as well as those with high friend support (OR = 0.34, 95% CI [0.13, 0.91]); similar effects were observed in men with obesity. These novel findings evince a role for psychosocial resources in buffering associations between weight discrimination and diabetes.

Diabetes Life Expectancy Prediction Model Inputs and Results From Patient Surveys Compared With Electronic Health Record Abstraction: Survey Study.

Background: Prediction models are being increasingly used in clinical practice, with some requiring patient-reported outcomes (PROs). The optimal approach to collecting the needed inputs is unknown. Objective: Our objective was to compare mortality prediction model inputs and scores based on electronic health record (EHR) abstraction versus patient survey. Methods: Older patients aged ≥65 years with type 2 diabetes at an urban primary care practice in Chicago were recruited to participate in a care management trial. All participants completed a survey via an electronic portal that included items on the presence of comorbid conditions and functional status, which are needed to complete a mortality prediction model. We compared the individual data inputs and the overall model performance based on the data gathered from the survey compared to the chart review. Results: For individual data inputs, we found the largest differences in questions regarding functional status such as pushing/pulling, where 41.4% (31/75) of participants reported difficulties that were not captured in the chart with smaller differences for comorbid conditions. For the overall mortality score, we saw nonsignificant differences (P=.82) when comparing survey and chart-abstracted data. When allocating participants to life expectancy subgroups (<5 years, 5-10 years, >10 years), differences in survey and chart review data resulted in 20% having different subgroup assignments and, therefore, discordant glucose control recommendations. Conclusions: In this small exploratory study, we found that, despite differences in data inputs regarding functional status, the overall performance of a mortality prediction model was similar when using survey and chart-abstracted data. Larger studies comparing patient survey and chart data are needed to assess whether these findings are reproduceable and clinically important.

Severe hypoglycemia and falls in older adults with diabetes: The Diabetes & Aging Study.

Objective: To estimate rates of severe hypoglycemia and falls among older adults with diabetes and evaluate their association. Research Design and Methods: Survey in an age-stratified, random sample adults with diabetes age 65-100 years; respondents were asked about severe hypoglycemia (requiring assistance) and falls in the past 12 months. Prevalence ratios (adjusted for age, sex, race/ethnicity) estimated the increased risk of falls associated with severe hypoglycemia. Results: Among 2,158 survey respondents, 79 (3.7%) reported severe hypoglycemia, of whom 68 (86.1%) had no ED visit or hospitalization for hypoglycemia. Falls were reported by 847 (39.2%), of whom 745 (88.0%) had no fall documented in outpatient or inpatient records. Severe hypoglycemia was associated with a 70% greater prevalence of falls (adjusted prevalence ratio = 1.7 (95% CI, 1.3-2.2)). Conclusion: While clinical documentation of events likely reflects severity or care-seeking behavior, severe hypoglycemia and falls are common, under-reported life-threatening events.

CommunityRx, a social care assistance intervention for family and friend caregivers delivered at the point of care: two concurrent blinded randomized controlled trials.

BACKGROUND: CommunityRx is an evidence-based social care intervention delivered to family and friend caregivers ("caregivers") at the point of healthcare to address health-related social risks (HRSRs). Two CommunityRx randomized controlled trials (RCTs) are being fielded concurrently on Chicago's South Side, a predominantly African American/Black community. CommunityRx-Hunger is a double-blind RCT enrolling caregivers of hospitalized children. CommunityRx-Dementia is a single-blind RCT enrolling caregivers of community-residing people with dementia. RCTs with caregivers face recruitment barriers, including caregiver burden and lack of systematic strategies to identify caregivers in clinical settings. COVID-19 pandemic-related visitor restrictions exacerbated these barriers and prompted the need for iteration of the protocols from in-person to remote operations. This study describes these protocols and methods used for successful iteration to overcome barriers. METHODS AND FINDINGS: CommunityRx uses individual-level data to generate personalized, local community resource referrals for basic, health and caregiving needs. In early 2020, two in-person RCT protocols were pre-tested. In March 2020, when pandemic conditions prohibited face-to-face clinical enrollment, both protocols were iterated to efficient, caregiver-centered remote operations. Iterations were enabled in part by the Automated Randomized Controlled Trial Information-Communication System (ARCTICS), a trial management system innovation engineered to integrate the data collection database (REDCap) with community resource referral (NowPow) and SMS texting (Mosio) platforms. Enabled by engaged Community Advisory Boards and ARCTICS, both RCTs quickly adapted to remote operations. To accommodate these adaptations, launch was delayed until November (CommunityRx-Hunger) and December (CommunityRx-Dementia) 2020. Despite the delay, 65% of all planned participants (CommunityRx-Hunger n = 417/640; CommunityRx-Dementia n = 222/344) were enrolled by December 2021, halfway through our projected enrollment timeline. Both trials enrolled 13% more participants in the first 12 months than originally projected for in-person enrollment. DISCUSSION: Our asset-based, community-engaged approach combined with widely accessible institutional and commercial information technologies facilitated rapid migration of in-person trials to remote operations. Remote or hybrid RCT designs for social care interventions may be a viable, scalable alternative to in-person recruitment and intervention delivery protocols, particularly for caregivers and other groups that are under-represented in traditional health services research. TRIAL REGISTRATION: ClinicalTrials.gov: CommunityRx-Hunger (NCT04171999, 11/21/2019); CommunityRx for Caregivers (NCT04146545, 10/31/2019).

Costs and Health Care Utilization Analysis of Medical Group Visits for Adults With Type 2 Diabetes in Community Health Centers.

OBJECTIVE: We evaluated the economic impact of group visits (GVs) in adults with uncontrolled diabetes in community health centers (CHCs) in the United States. RESEARCH DESIGN AND METHODS: In this prospective controlled trial, we implemented 6 monthly GV sessions in 5 CHCs and compared intervention patients (n=49) to control patients (n=72) receiving usual care within the same CHCs. We conducted patient chart reviews to obtain health care utilization data for the prior 6 months at baseline, 6 months (during the GV implementation), and 12 months (after the implementation). We also collected monthly logs of CHC expenses and staff time spent on activities related to GVs. Per-patient total costs included CHCs' expenses and costs associated with staff time and patients' health care use. For group comparison, we used the Wilcoxon rank-sum test and the bootstrapping method that was to bootstrap generalized estimating equation models. RESULTS: The GV group had fewer 6-month hospitalizations (mean: GV: 0.06 vs. control: 0.24, rate: 6.1% vs. 19.4%) ( P ≤ 0.04) and similar emergency department visits at 12 months than the control group. Implementing GV incurred $1770 per-patient. The intervention cost $1597 more than the control at 6 months ($3021 vs. $1424) but saved $1855 at 12 months ($857 vs. $2712) ( P =0.002). CONCLUSIONS: The diabetes GV care model reduced hospitalizations and had cost savings at 12 months, while it improved patients' diabetes-related quality of life and glucose control. Future studies should assess its lifetime cost-effectiveness through a randomized controlled trial.

Glycemic control and diabetes complications across health status categories in older adults treated with insulin or insulin secretagogues: The Diabetes & Aging Study.

BACKGROUND: For older adults with type 2 diabetes (T2D) treated with insulin or sulfonylureas, Endocrine Society guideline recommends HbA1c between 7% to <7.5% for those in good health, 7.5% to <8% for those in intermediate health, and 8% to <8.5% for those in poor health. Our aim was to examine associations between attained HbA1c below, within (reference), or above recommended target range and risk of complication or mortality. METHODS: Retrospective cohort study of adults ≥65 years old with T2D treated with insulin or sulfonylureas from an integrated healthcare delivery system. Cox proportional hazards models of complications during 2019 were adjusted for sociodemographic and clinical variables. Primary outcome was a combined outcome of any microvascular or macrovascular event, severe hypoglycemia, or mortality during 12-month follow-up. RESULTS: Among 63,429 patients (mean age: 74.2 years, 46.8% women), 8773 (13.8%) experienced a complication. Complication risk was significantly elevated for patients in good health (n = 16,895) whose HbA1c was above (HR 1.97, 95% CI 1.62-2.41) or below (HR 1.29, 95% CI 1.02-1.63) compared to within recommended range. Among those in intermediate health (n = 30,129), complication risk was increased for those whose HbA1c was above (HR 1.45, 95% CI 1.30-1.60) but not those below the recommended range (HR 0.99, 95% CI 0.89-1.09). Among those in poor health (n = 16,405), complication risk was not significantly different for those whose HbA1c was below (HR 0.98, 95% CI 0.89-1.09) or above (HR 0.96, 95% CI 0.88-1.06) recommended range. CONCLUSIONS: For older adults with T2D in good health, HbA1c below or above the recommended range was associated with significantly elevated complication risk. However, for those in poor health, achieving specific HbA1c levels may not be helpful in reducing the risk of complications.

Expected Health Benefits of SGLT-2 Inhibitors and GLP-1 Receptor Agonists in Older Adults.

Background. Older and sicker adults with type 2 diabetes (T2D) were underrepresented in randomized trials of glucagon-like peptide 1 receptor-agonist (GLP1RA) and sodium-glucose cotransporter 2 inhibitors (SGLT2I), and thus, health benefits are uncertain in this population. Objective. To assess the impact of age, health status, and life expectancy in older adults with T2D on health benefits of GLP1RA and SGLT2I. Design. We used the United Kingdom Prospective Diabetes Study (UKPDS) model to simulate lifetime health outcomes. We calibrated the UKPDS model to improve mortality prediction in older adults using a common geriatric prognostic index. Participants. National Health and Nutrition Examination Survey 2013-2018 participants 65 y and older with T2D, eligible for GLP1RA or SGLT2I according to American Diabetes Association guidelines. Interventions. GLP1RA or SGLT2I use versus no additional medication. Main Measures. Lifetime complications and weighted life-years (LYs) and quality-adjusted life-years (QALYs) across overall treatment arms and life expectancies. Key Results. The overall older adult population was predicted to experience significant health benefits from GLP1RA (+0.29 LY [95% confidence interval: 0.27, 0.31], +0.15 QALYs [0.14, 0.16]) and SGLT2I (+0.26 LY [0.24, 0.28], +0.13 QALYs [0.12, 0.14]) as compared with no added medication. However, expected benefits declined in subgroups with shorter life expectancies. Participants with <4 y of life expectancy had minimal gains of <0.05 LY and <0.03 QALYs from added medication. Accounting for injection-related disutility, GLP1RA use reduced QALYs (-0.03 QALYs [-0.04, -0.02]). Conclusions. While GLP1RA and SGLT2I have substantial health benefits for many older adults with type 2 diabetes, benefits are not clinically significant in patients with <4 y of life expectancy. Life expectancy and patient preferences are important considerations when prescribing newer diabetes medications. Highlights: On average, older adults benefit significantly from SGLT2I and GLP1RA use. However, the benefits of these drugs are not clinically significant among older patients with life expectancy less than 4 y.There is potential harm in injectable GLP1RA use in the oldest categories of adults with type 2 diabetes.Heterogeneity in life expectancy and patient preferences for injectable versus oral medications are important to consider when prescribing newer diabetes medications.

The Growing Role of Technology in the Care of Older Adults With Diabetes.

The integration of technologies such as continuous glucose monitors, insulin pumps, and smart pens into diabetes management has the potential to support the transformation of health care services that provide a higher quality of diabetes care, lower costs and administrative burdens, and greater empowerment for people with diabetes and their caregivers. Among people with diabetes, older adults are a distinct subpopulation in terms of their clinical heterogeneity, care priorities, and technology integration. The scientific evidence and clinical experience with these technologies among older adults are growing but are still modest. In this review, we describe the current knowledge regarding the impact of technology in older adults with diabetes, identify major barriers to the use of existing and emerging technologies, describe areas of care that could be optimized by technology, and identify areas for future research to fulfill the potential promise of evidence-based technology integrated into care for this important population.

Use of Real-World Data in Population Science to Improve the Prevention and Care of Diabetes-Related Outcomes.

The past decade of population research for diabetes has seen a dramatic proliferation of the use of real-world data (RWD) and real-world evidence (RWE) generation from non-research settings, including both health and non-health sources, to influence decisions related to optimal diabetes care. A common attribute of these new data is that they were not collected for research purposes yet have the potential to enrich the information around the characteristics of individuals, risk factors, interventions, and health effects. This has expanded the role of subdisciplines like comparative effectiveness research and precision medicine, new quasi-experimental study designs, new research platforms like distributed data networks, and new analytic approaches for clinical prediction of prognosis or treatment response. The result of these developments is a greater potential to progress diabetes treatment and prevention through the increasing range of populations, interventions, outcomes, and settings that can be efficiently examined. However, this proliferation also carries an increased threat of bias and misleading findings. The level of evidence that may be derived from RWD is ultimately a function of the data quality and the rigorous application of study design and analysis. This report reviews the current landscape and applications of RWD in clinical effectiveness and population health research for diabetes and summarizes opportunities and best practices in the conduct, reporting, and dissemination of RWD to optimize its value and limit its drawbacks.

Development and Validation of the Life Expectancy Estimator for Older Adults with Diabetes (LEAD): the Diabetes and Aging Study.

BACKGROUND: Estimated life expectancy for older patients with diabetes informs decisions about treatment goals, cancer screening, long-term and advanced care, and inclusion in clinical trials. Easily implementable, evidence-based, diabetes-specific approaches for identifying patients with limited life expectancy are needed. OBJECTIVE: Develop and validate an electronic health record (EHR)-based tool to identify older adults with diabetes who have limited life expectancy. DESIGN: Predictive modeling based on survival analysis using Cox-Gompertz models in a retrospective cohort. PARTICIPANTS: Adults with diabetes aged ≥ 65 years from Kaiser Permanente Northern California: a 2015 cohort (N = 121,396) with follow-up through 12/31/2019, randomly split into training (N = 97,085) and test (N = 24,311) sets. Validation was conducted in the test set and two temporally distinct cohorts: a 2010 cohort (n = 89,563; 10-year follow-up through 2019) and a 2019 cohort (n = 152,357; 2-year follow-up through 2020). MAIN MEASURES: Demographics, diagnoses, utilization and procedures, medications, behaviors and vital signs; mortality. KEY RESULTS: In the training set (mean age 75 years; 49% women; 48% racial and ethnic minorities), 23% died during 5 years follow-up. A mortality prediction model was developed using 94 candidate variables, distilled into a life expectancy model with 11 input variables, and transformed into a risk-scoring tool, the Life Expectancy Estimator for Older Adults with Diabetes (LEAD). LEAD discriminated well in the test set (C-statistic = 0.78), 2010 cohort (C-statistic = 0.74), and 2019 cohort (C-statistic = 0.81); comparisons of observed and predicted survival curves indicated good calibration. CONCLUSIONS: LEAD estimates life expectancy in older adults with diabetes based on only 11 patient characteristics widely available in most EHRs and claims data. LEAD is simple and has potential application for shared decision-making, clinical trial inclusion, and resource allocation.

Two concurrent randomized controlled trials of CommunityRx, a social care intervention for family and friend caregivers delivered at the point of care.

Background: CommunityRx is an evidence-based social care intervention delivered to family and friend caregivers ("caregivers") at the point of healthcare to address health-related social risks (HRSRs). CommunityRx-Hunger is a double-blind randomized controlled trial (RCT) that enrolls caregivers of hospitalized children. CommunityRx-Dementia is a single-blind RCT that enrolls caregivers of community-residing people with dementia. Clinical trials that enroll caregivers face recruitment barriers, including caregiver burden and lack of systematic strategies to identify and track caregivers. COVID-19 pandemic-related visitor restrictions exacerbated these barriers and prompted the need for iteration of the CommunityRx protocols from in-person to remote operations. This study describes the novel methods used to iterate existing RCT protocols and factors contributing to their successful iteration. Methods: CommunityRx uses individual-level data to generate personalized community resource referrals for basic, health and caregiving needs. Our research program uses an asset-based, community-engaged approach including study-specific community advisory boards (CABs). In early 2020, both RCT protocols were pre-tested in-person. In March 2020, when pandemic conditions prohibited enrollment during clinical encounters, both protocols were iterated to efficient, caregiver-centered remote operations. Iterations were enabled in part by the Automated Randomized Controlled Trial Information-Communication System (ARCTICS), a trial management system innovation engineered to integrate the data collection database (REDCap) with community resource referral (NowPow) and SMS texting (Mosio) platforms. Results: Enabled by engaged CABs and ARCTICS, both RCTs quickly adapted to remote operations. Designed before the pandemic, we had planned to launch both trials by March 2020 and complete enrollment by December 2021. The pandemic postponed launch until November (CommunityRx-Hunger) and December (CommunityRx-Dementia) 2020. Despite the delay, 65% of all planned participants (CommunityRx-Hunger n = 417/640; CommunityRx-Dementia n = 222/344) were enrolled by December 2021, halfway through our projected enrollment timeline. Both trials enrolled 13% more participants in 12 months than originally projected in-person. Conclusions: Our asset-based, community-engaged approach combined with widely accessible institutional and commercial information technologies facilitated rapid migration to remote trial operations. Remote or hybrid RCT designs for social care interventions may be a viable, scalable alternative to in-person recruitment and intervention delivery protocols, particularly for caregivers and other groups that are under-represented in traditional health services research. Trial Status: Both studies are registered on ClinicalTrials.gov: CommunityRx-Hunger (NCT04171999); CommunityRx for Caregivers (NCT04146545); My Diabetes My Community (NCT04970810).

Validation of a Hypoglycemia Risk Stratification Tool Using Data From Continuous Glucose Monitors.

This cohort study uses data from continuous glucose monitoring to validate a hypoglycemia risk stratification tool.