Identifying multi-dimensional health clusters associated with high hospitalization burden among middle-aged and older...

To identify and characterize health subtypes among middle-aged and older adults in China, and test if individuals in high-burden subtypes experience greater hospitalization over time.

This cohort study utilized data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey of adults aged ≥45 years, covering five waves (2011-2020). K-means clustering was applied to seven baseline indicators-functional disability, chronic conditions, neuropsychological vulnerability composite (NVC), unhealthy behaviors, social resources composite (SRC), and prior-year hospitalization-to identify distinct health subtypes. The optimal number of clusters was determined using the elbow method and average silhouette width, and cluster stability was assessed via bootstrap resampling with the adjusted Rand index. Associations between the identified subtypes and subsequent hospitalization burden were examined using generalized estimating equation (GEE) models.

The analysis included 16,710 participants (mean age, 59.4 years; 51.2 % female). Two health subtypes were identified: high-burden (27.1 %) and low-burden (72.9 %). The high-burden group showed worse health across all indicators and had significantly higher hospitalization rates. GEE models showed that the low-burden group had fewer hospitalization days (β = -0.34; P < .001) and lower costs (β = -2,336; P < .001). Age and education were significant factors (P < .001).

Greater functional disability, more chronic diseases, higher NVC, unhealthy behaviors, and lower SRC were significantly associated with a higher hospitalization burden. These findings underscore the importance of subtype-based stratification for designing targeted interventions in older adults.