Uncovering Heterogeneous Associations Between Disaster-Related Trauma and Subsequent Functional Limitations: A Machine-Learning Approach
Journal article, 2023

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.

machine learning

functional limitation

causal inference

instrumental activities of daily living

effect heterogeneity

natural disaster

Author

Koichiro Shiba

School of Public Health

Adel Daoud

Harvard School of Public Health

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

University of Gothenburg

Linköping University

Hiroyuki Hikichi

Kitasato University

Aki Yazawa

Harvard School of Public Health

National Center for Global Health and Medicine

Jun Aida

Graduate School of Medical and Dental Sciences

Katsunori Kondo

National Center for Geriatrics and Gerontology

Chiba University

Ichiro Kawachi

Harvard School of Public Health

American Journal of Epidemiology

0002-9262 (ISSN) 1476-6256 (eISSN)

Vol. 192 2 217-229

Subject Categories (SSIF 2025)

Public Health, Global Health and Social Medicine

Gerontology, specialising in Medical and Health Sciences

DOI

10.1093/aje/kwac187

PubMed

36255224

More information

Latest update

11/17/2025