Wang Kun, Zhao Jinxu, Hu Jie, Liang Dan, Luo Yansong
Zhongnan University of Economics and Law (School of Philosophy), Wuhan, Hubei, China.
Nankai University (Zhou Enlai School of Government), Tianjin, China.
Front Public Health. 2023 Sep 12;11:1257818. doi: 10.3389/fpubh.2023.1257818. eCollection 2023.
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.
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).
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.
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.
中国人口老龄化导致残疾老年人数量显著增加。这些人在获得足够的日常生活活动（ADL）协助方面面临巨大挑战。这一人群未得到满足的ADL需求可能导致严重的健康后果，并使本已负担沉重的护理系统不堪重负。本研究旨在使用六种机器学习方法，确定影响80岁及以上残疾高龄老人未得到满足的ADL需求的因素。
利用2017 - 2018年中国老年健康影响因素跟踪调查（CLHLS）数据，我们采用六种机器学习方法预测残疾高龄老人未得到满足的ADL需求。使用夏普利值附加解释（SHAP）探索各种因素对未得到满足的ADL需求的预测效果。
在测试的六种机器学习方法中，随机森林模型显示出最高的预测准确率。基于随机森林模型的SHAP分析表明，户籍、残疾类别、经济状况等级、自评健康状况、照料者意愿、感知控制、经济满意度、养老金、受教育程度、给予子女的经济支持、居住安排、子女数量和主要照料者等因素，在残疾高龄老人未得到满足的ADL需求中发挥着重要作用。
我们的研究强调了社会经济因素（如户籍和经济状况等级）、健康状况（如残疾类别和自评健康状况）以及照料关系因素（如照料者意愿和感知控制）在减少中国残疾高龄老人未得到满足的ADL需求方面的重要性。旨在弥合城乡差距、针对健康状况恶化群体以及提高照料者技能的政府干预措施，对于确保这一弱势群体的福祉至关重要。这些研究结果可为政策决策和干预措施提供参考，以更好地满足残疾高龄老人未得到满足的ADL需求。
