Predicting unmet activities of daily living needs among the oldest old with disabilities in China

A machine learning approach

Bibliographic Data

ID22085462
AuthorsKun Wang (0000-0002-6199-4315, Zhongnan University of Economics and Law), Jinxu Zhao (0009-0007-5931-8960, Zhongnan University of Economics and Law), Jie Hu (0000-0003-4772-0315, Wuhan University), Dan Liang (0000-0001-8346-2654, Huazhong University of Science and Technology), Yansong Luo (Zhongnan University of Economics and Law, corresponding author)
Year2023
Volume11
Pages1257818-1257818
Publication date2023-09-12
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2023.1257818
PMID37771828
OpenAlexW4386695823
LanguageEN
Citations received3
References cited48

Background: The ageing population in China has led to a significant increase in the number of older persons with disabilities. These individuals face substantial challenges in accessing adequate activities of daily living (ADL) assistance. Unmet ADL needs among this population can result in severe health consequences and strain an already burdened care system. This study aims to identify the factors influencing unmet ADL needs of the oldest old (those aged 80 and above) with disabilities using six machine learning methods. Methods: Drawing from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) 2017-2018 data, we employed six machine learning methods to predict unmet ADL needs among the oldest old with disabilities. The predictive effects of various factors on unmet ADL needs were explored using Shapley Additive exPlanations (SHAP). Results: The Random Forest model showed the highest prediction accuracy among the six machine learning methods tested. SHAP analysis based on the Random Forest model revealed that factors such as household registration, disability class, economic rank, self-rated health, caregiver willingness, perceived control, economic satisfaction, pension, educational attainment, financial support given to children, living arrangement, number of children, and primary caregiver played significant roles in the unmet ADL needs of the oldest old with disabilities. Conclusion: Our study highlights the importance of socioeconomic factors (e.g., household registration and economic rank), health status (e.g., disability class and self-rated health), and caregiving relationship factors (e.g., caregiver willingness and perceived control) in reducing unmet ADL needs among the oldest old with disabilities in China. Government interventions aimed at bridging the urban-rural divide, targeting groups with deteriorating health status, and enhancing caregiver skills are essential for ensuring the well-being of this vulnerable population. These findings can inform policy decisions and interventions to better address the unmet ADL needs among the oldest old with disabilities

Activities of daily living · Educational attainment · Environmental health · Population · Psychiatry · Psychological intervention · Socioeconomic status · Health disparities and outcomes · Intergenerational Family Dynamics and Caregiving · Medicine · Nursing · Psychology · Technology Use by Older Adults · Gerontology

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