Uncovering Heterogeneous Associations Between Disaster-Related Trauma and Subsequent Functional Limitations: A Machine-Learning Approach
Uncovering Heterogeneous Associations Between Disaster-Related Trauma and Subsequent Functional Limitations: A Machine-Learning Approach
复制标题
揭示灾害相关创伤与后续功能限制之间的异质关联：机器学习方法
DOI：
10.1093/aje/kwac187
复制
发表时间：
2022
影响因子：
5
通讯作者：
Kawachi Ichiro
中科院分区：
文献类型：
--
作者：
Shiba Koichiro;Daoud Adel;Hikichi Hiroyuki;Yazawa Aki;Aida Jun;Kondo Katsunori;Kawachi Ichiro
本研究探讨了与灾害有关的家庭损失和老年人的功能限制之间的关联的异质性，并确定了脆弱亚群的特征。数据来自2011年日本大地震日本老年幸存者的前瞻性队列研究。客观地评估了完整的家庭损失。2013年（n= 3，350）和2016年（n= 2，664）的结果包括认证的身体残疾水平，自我报告的日常生活活动和日常生活的工具活动。我们估计人口平均家庭损失和功能限制之间的关联，通过有针对性的最大似然估计与超级学习和其异质性，通过广义随机森林算法。我们调整了灾难前7个月进行的基线调查中幸存者的55个特征。虽然失去家园与平均功能限制增加相关，但有证据表明所有结局的效应异质性。比较最脆弱和最不脆弱的群体，最脆弱的群体往往年龄较大，未婚，独居，没有工作，在灾难发生前就存在健康问题。受教育程度较低但收入较高的个人似乎也容易受到某些结果的影响。我们使用机器学习算法的效果异质性的归纳方法揭示了日本老年幸存者在灾后功能限制方面的巨大而复杂的异质性。
This study examined heterogeneity in the association between disaster-related home loss and functional limitations of older adults, and identified characteristics of vulnerable subpopulations. Data were from a prospective cohort study of Japanese older survivors of the 2011 Japan Earthquake. Complete home loss was objectively assessed. Outcomes in 2013 (n= 3,350) and 2016 (n= 2,664) included certified physical disability levels, self-reported activities of daily living, and instrumental activities of daily living. We estimated population average associations between home loss and functional limitations via targeted maximum likelihood estimation with SuperLearning and its heterogeneity via the generalized random forest algorithm. We adjusted for 55 characteristics of survivors from the baseline survey conducted 7 months before the disaster. While home loss was consistently associated with increased functional limitations on average, there was evidence of effect heterogeneity for all outcomes. Comparing the most and least vulnerable groups, the most vulnerable group tended to be older, not married, living alone, and not working, with preexisting health problems before the disaster. Individuals who were less educated but had higher income also appeared vulnerable for some outcomes. Our inductive approach for effect heterogeneity using machine learning algorithm uncovered large and complex heterogeneity in postdisaster functional limitations among Japanese older survivors.