Objective: to identify multimorbidity patterns among middle-aged and older adults in China and examine how these patterns are associated with incident disability and recovery of independence.
Methods: data were from The China Health and Retirement Longitudinal Study. We included 14,613 persons aged ≥45 years. Latent class analysis (LCA) was conducted to identify multimorbidity patterns with clinical meaningfulness. Multinomial logistic models were used to determine the adjusted association between multimorbidity patterns and incident disability and recovery of independence.
Results: we identified four multimorbidity patterns: 'low morbidity' (67.91% of the sample), 'pulmonary-digestive-rheumatic' (17.28%), 'cardiovascular-metabolic-neuro' (10.77%) and 'high morbidity' (4.04%). Compared to the 'low morbidity' group, 'high morbidity' (OR = 2.63, 95% CI = 1.97-3.51), 'pulmonary-digestive-rheumatic' (OR = 1.89, 95% CI = 1.63-2.21) and 'cardiovascular-metabolic-neuro' pattern (OR = 1.61, 95% CI = 1.31-1.97) had higher odds of incident disability in adjusted multinomial logistic models. The 'cardiovascular-metabolic-neuro' (OR = 0.60, 95% CI = 0.44-0.81), 'high morbidity' (OR = 0.68, 95% CI = 0.47-0.98) and 'pulmonary-digestive-rheumatic' group (OR = 0.75, 95% CI = 0.60-0.95) had lower odds of recovery from disability than the 'low morbidity' group. Among people without disability, the 'cardiovascular-endocrine-neuro' pattern was associated with the highest 2-year mortality (OR = 2.42, 95% CI = 1.56-3.72).
Conclusions: multimorbidity is complex and heterogeneous, but our study demonstrates that clinically meaningful patterns can be obtained using LCA. We highlight four multimorbidity patterns with differential effects on incident disability and recovery from disability. These studies suggest that targeted prevention and treatment approaches are needed for people with multimorbidity.
Keywords: disability; latent class analysis; middle-aged and older adults; multimorbidity; older people.
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