Examining individual and contextual predictors of disability in Chinese older adults: A machine learning approach.

Background: There is a large gap of understanding the determinants of disability, especially the contextual characteristics. Therefore, this study aimed to examine the important predictors of disability in Chinese older adults based on the social ecological framework.

Methods: We used the China Health and Retirement Longitudinal Study to examine predictors of disability, defined as self-report of any difficulty for six activity of daily living items. We restricted analytical sample to older adults aged 65 or above (N=1816). We considered 44 predictors, including personal-, behavioral-, interpersonal-, community-, and policy-level characteristics. The built-in variable importance measure (VIM) of random forest and SHapley Additive exPlanations (SHAP) were applied to assess key predictors of disability. A multilevel logit regression was further used to examine the associations of individual and contextual characteristics with disability.

Results: The mean age of study sample was 72.62 years old (standard deviation: 5.77). During a 2-year of follow-up, 518 (28.52 %) of them developed into disability. Walking speed, age, and peak expiratory flow were the top important predictors in both VIM and SHAP. Contextual characteristics such as humidity, PM, temperature, normalized difference vegetation index, and landscape also showed promise in predicting disability. Multilevel logit regression showed that people with male gender, arthritis, vision impairment, unable to finish semi tandem, no social activity, lower grip strength, and higher waist circumference, had much higher risk of disability.

Conclusion: Disability prevention strategies should specifically focus on multilevel factors such as individual and contextual characteristics, although the latter is warranted to be verified in future studies.