Combined impact of physical frailty and social isolation on use of long-term care insurance in Japan: A longitudinal observational study.

OBJECTIVES: The combined effect of physical frailty and social isolation on the need to make use of long-term care insurance (LTCI) among older adults remains unknown. Thus this study investigates the association between physical frailty, social isolation, and the use of LTCI among older adults in Japan. STUDY DESIGN: This is a prospective observational study. MAIN OUTCOME MEASURES: Physical frailty is defined as limitations in strength, mobility, and physical activity, as well as exhaustion and weight loss. People with one or two indicators were categorized as pre-frail. Participants with a score of 1 point or more on the social isolation scale were defined as being socially isolated. Participants were followed up monthly for two years to check whether incident certification of care had been required. RESULTS: Data on 4576 community-dwelling independent older adults (mean age, 73.9 ± 5.5 years, 2032 men, 2544 women) were analyzed. A time-dependent Cox proportional hazards regression model showed that individuals with pre-frailty without social isolation (hazard ratio [HR] 2.02, 95 % confidence interval [CI] 1.40-2.91), pre-frailty with social isolation (HR 2.36, 95 % CI 1.62-3.43), frailty without social isolation (HR 2.98, 95 % CI 1.83-4.85), and frailty with social isolation (HR 3.19, 95 % CI 2.07-4.91) had significantly higher risks of needing to make use of LTCI than those with no frailty and without social isolation. This higher risk was non-significant among individuals with no frailty and social isolation (HR 1.28, 95 % CI 0.78-2.10). CONCLUSION: Combined frailty and social isolation among older adults should be addressed to prevent adverse health outcomes, including use of LTCI.

Simplified Decision-Tree Algorithm to Predict Falls for Community-Dwelling Older Adults.

The present study developed a simplified decision-tree algorithm for fall prediction with easily measurable predictors using data from a longitudinal cohort study: 2520 community-dwelling older adults aged 65 years or older participated. Fall history, age, sex, fear of falling, prescribed medication, knee osteoarthritis, lower limb pain, gait speed, and timed up and go test were assessed in the baseline survey as fall predictors. Moreover, recent falls were assessed in the follow-up survey. We created a fall-prediction algorithm using decision-tree analysis (C5.0) that included 14 nodes with six predictors, and the model could stratify the probabilities of fall incidence ranging from 30.4% to 71.9%. Additionally, the decision-tree model outperformed a logistic regression model with respect to the area under the curve (0.70 vs. 0.64), accuracy (0.65 vs. 0.62), sensitivity (0.62 vs. 0.50), positive predictive value (0.66 vs. 0.65), and negative predictive value (0.64 vs. 0.59). Our decision-tree model consists of common and easily measurable fall predictors, and its white-box algorithm can explain the reasons for risk stratification; therefore, it can be implemented in clinical practices. Our findings provide useful information for the early screening of fall risk and the promotion of timely strategies for fall prevention in community and clinical settings.