Racial and ethnic differences in predictors of participation in an intergenerational social connectedness intervention for older adults.

BACKGROUND: Social connectedness is a key determinant of health and interventions have been developed to prevent social isolation in older adults. However, these interventions have historically had a low participation rate amongst minority populations. Given the sustained isolation caused by the COVID-19 pandemic, it is even more important to understand what factors are associated with an individual's decision to participate in a social intervention. To achieve this, we used machine learning techniques to model the racial and ethnic differences in participation in social connectedness interventions. METHODS: Data were obtained from a social connectedness intervention that paired college students with Houston-area community-dwelling older adults (> 65 yo) enrolled in Medicare Advantage plans. Eligible participants were contacted telephonically and asked to complete the 3-item UCLA Loneliness Scale. We used the following machine-learning methods to identify significant predictors of participation in the program: k-nearest neighbors, logistic regression, decision tree, gradient-boosted decision tree, and random forest. RESULTS: The gradient-boosted decision tree models yielded the best parameters for all race/ethnicity groups (96.1% test accuracy, 0.739 AUROC). Among non-Hispanic White older adults, key features of the predictive model included Functional Comorbidity Index (FCI) score, Medicare prescription risk score, Medicare risk score, and depression and anxiety indicators within the FCI. Among non-Hispanic Black older adults, key features included disability, Medicare prescription risk score, FCI and Medicare risk scores. Among Hispanic older adults, key features included depression, FCI and Medicare risk scores. CONCLUSIONS: These findings offer a substantial opportunity for the design of interventions that maximize engagement among minority groups at greater risk for adverse health outcomes.

Health Care Utilization and Medical Cost Outcomes from a Digital Diabetes Prevention Program in a Medicare Advantage Population.

This study examined the effects of a digital diabetes prevention program (DPP) on health care costs and utilization among Medicare Advantage participants. Patients (n = 501) received access to a plan-sponsored, digitally-delivered DPP accessible through computer, tablet, or smartphone. Prior research demonstrated a 7.5% reduction in body weight at 12 months. A comparison group who did not participate in the DPP was constructed by matching on demographic, health plan, health status, and health care costs and utilization. The authors assessed effects on cost and utilization outcomes using difference-in-differences regressions, controlling for propensities to participate and engage in the DPP, in the 12 months prior to DPP enrollment and 24 months after. Though post-enrollment data showed trends in decreased drug spending and emergency department use, increased inpatient utilization, and no change in total nondrug costs or outpatient utilization, the findings did not reach statistical significance, potentially because of sample size. The population had low costs and utilization at baseline, which may be responsible for the lack of observed effects in the short time frame. This study demonstrates the challenges of studying the effectiveness of preventive programs in a population with low baseline costs and the importance of using a large enough sample and follow-up period, but remains an important contribution to exploring the effects of digital DPPs in a real-world sample of individuals who were eligible and willing to participate.