Uncovering Heterogeneous Associations Between Disaster-Related Trauma and Subsequent Functional Limitations: A Machine-Learning Approach

• Published in: American journal of epidemiology Vol. 192; no. 2; pp. 217 - 229
• Main Authors: Shiba, Koichiro, Daoud, Adel, Hikichi, Hiroyuki, Yazawa, Aki, Aida, Jun, Kondo, Katsunori, Kawachi, Ichiro
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 01.02.2023; Oxford Publishing Limited (England)
• Subjects: Activities of Daily Living; Aged; Algorithms; Disasters; Earthquakes; Health problems; Heterogeneity; Humans; Japan - epidemiology; Learning algorithms; Machine Learning; Maximum likelihood estimation; Older people; Original Contribution; Population statistics; Prospective Studies; Seismic activity; Subpopulations; Survival; Japan; effect heterogeneity; functional limitation; instrumental activities of daily living; natural disaster; causal inference; machine learning
• ISSN: 0002-9262; 1476-6256; 1476-6256
• DOI: 10.1093/aje/kwac187

Abstract 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.