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Abstract
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AIM: Understanding the differential mental health effects of traumatic experiences is important to identify particularly vulnerable sub-populations. We examined the heterogeneous associations between disaster-related traumatic experiences and post-disaster mental health, using a novel machine learning-based causal inference approach.
METHODS: Data were from a prospective cohort study of Japanese older adults in an area severely affected by the 2011 Great East Japan Earthquake. The baseline survey was conducted 7 months prior to the disaster and the two follow-up surveys were conducted 2.5 and 5.5 years after (n = 1,150 to n = 1,644 depending on the exposure-outcome combinations). As disaster-related traumatic experiences, we assessed complete home loss and loss of loved ones. Using the generalized random forest algorithm, we estimated conditional average treatment effects (CATEs) of the disaster damages on post-disaster mental health outcomes to examine the heterogeneous associations by 51 pre-disaster characteristics of the subjects.
RESULTS: We found that, even when there was no population average association between disaster-related trauma and subsequent mental health outcomes, some sub-groups experienced severe impacts. We also identified and compared characteristics of the most and least vulnerable groups (i.e., top vs. bottom deciles of the estimated CATEs). While there were some unique patterns specific to each exposure-outcome combination, the most vulnerable group tended to be from lower socioeconomic backgrounds with pre-existing depressive symptoms for many exposure-outcome combinations.
CONCLUSIONS: We found considerable heterogeneity in the association between disaster-related traumatic experiences and subsequent mental health problems. This article is protected by copyright. All rights reserved.
Language: en |