Lagged effects of risk factors on the disability of older adults: A distributed lag non-linear model approach

This study aimed to explore the lagged and cumulative effects of risk factors on disability in older adults using distributed lag non-linear models (DLNMs).

We utilized data from the China Health and Retirement Longitudinal Study (CHARLS). After feature selection via Elastic Net Regularization, we applied DLNMs to evaluate the lagged effects of risk factors. Disability was defined as the presence of any difficulties in basic activities of daily living (BADL). The cumulative relative risk (CRR) was calculated by summing the lag-specific risk estimates, representing the cumulative disability risk over the specified lag period. Effect modifications and sensitivity analyses were also performed.

This study included a total of 2,318 participants. Early-phase lag factors, such as the difficulty in stooping (CRR = 3.58; 95 %CI: 2.31-5.55; P < 0.001) and walking (CRR = 2.77; 95 %CI: 1.39-5.55; P < 0.001), exerted the strongest effects immediately upon occurrence. Mid-phase lag factors, such as arthritis (CRR = 1.51; 95 %CI: 1.10-2.06; P = 0.001), showed a resurgence in disability risk within 2-3 years. Late-phase lag factors, including depressive symptoms (CRR = 2.38; 95 %CI: 1.30-4.35; P < 0.001) and elevated systolic blood pressure (CRR = 1.64; 95 %CI: 1.06-2.79; P = 0.02), exhibited significant long-term cumulative risks. Conversely, grip strength (CRR = 0.80; 95 %CI: 0.54-0.95; P = 0.02) and social participation (CRR = 0.89; 95 %CI: 0.73-0.99; P = 0.04) were significant protective factors.

The findings underscore the importance of tailored interventions that account for various lag characteristics of different factors to effectively mitigate disability risk. Future studies should explore the underlying biological and sociological mechanisms of these lagged effects, identify intervention strategies that target risk factors with different lagged patterns, and evaluate their effectiveness.