Multimorbidity measures differentially predicted mortality among older Chinese adults

Area of Science:

• Gerontology
• Public Health
• Epidemiology

Background:

• Multimorbidity, the coexistence of multiple chronic conditions, is prevalent in older adults.
• Accurate assessment of multimorbidity is crucial for predicting health outcomes like mortality.
• Existing measures of multimorbidity vary in their predictive power.

Purpose of the Study:

• To compare the associations between different multimorbidity measures and mortality in older Chinese adults.
• To evaluate the performance of condition counts, multimorbidity patterns, and multimorbidity trajectories in predicting mortality.
• To determine which multimorbidity measurement approach offers superior predictive accuracy.

Main Methods:

• Utilized data from the Chinese Longitudinal Healthy Longevity Survey (2002-2018) with 13,144 participants aged ≥65 years.
• Assessed multimorbidity using condition counts, exploratory factor analysis for patterns, and group-based trajectory modeling.
• Employed Cox regression for mortality risk analysis and C-statistic, Integrated Discrimination Improvement (IDI), and Net Reclassification Index (NRI) for performance comparison.

Main Results:

• All multimorbidity measures were associated with a significantly higher risk of death compared to no multimorbidity.
• Incorporating multimorbidity measures improved mortality prediction models beyond age and sex alone (C-statistic > 0.77).
• Trajectory-based multimorbidity measures demonstrated superior predictive performance (IDI and NRI) compared to count- and pattern-based measures.

Conclusions:

• Multimorbidity measures significantly enhance the prediction of mortality in older adults.
• Trajectory-based assessment of multimorbidity provides a more accurate prediction of mortality risk than simpler count or pattern-based methods.
• These findings highlight the importance of dynamic and pattern-based approaches for understanding multimorbidity's impact on longevity.