Clinical subtypes of older adults starting long-term care in Japan and their association with prognoses: a data-driven cluster analysis

Abstract

Abstract We aimed to identify the clinical subtypes in individuals starting long-term care in Japan and examined their association with prognoses. Using linked medical insurance claims data and survey data for care-need certification in a large city, we identified participants who started long-term care. Grouping them based on 22 diseases recorded in the past 6 months using fuzzy c-means clustering, we examined the longitudinal association between clusters and death or care-need level deterioration within 2 years. We analyzed 4,648 participants (median age 83 [interquartile range 78–88] years, female 60.4%) between October 2014 and March 2019 and categorized them into (i) musculoskeletal and sensory, (ii) cardiac, (iii) neurological, (iv) respiratory and cancer, (v) insulin-dependent diabetes, and (vi) unspecified subtypes. The results of clustering were replicated in another city. Compared with the musculoskeletal and sensory subtype, the adjusted hazard ratio (95% confidence interval) for death was 1.22 (1.05–1.42), 1.81 (1.54–2.13), and 1.21 (1.00–1.46) for the cardiac, respiratory and cancer, and insulin-dependent diabetes subtypes, respectively. The care-need levels more likely worsened in the cardiac, respiratory and cancer, and unspecified subtypes than in the musculoskeletal and sensory subtype. In conclusion, distinct clinical subtypes exist among individuals initiating long-term care.

Keywords

• Long-term care
• Multimorbidity
• Machine learning
• Clustering