Uncovering heterogeneous associations of disaster-related traumatic experiences with subsequent mental health problems: A machine learning approach

Artikel i vetenskaplig tidskrift, 2022

Aim:
Understanding the differential mental health effects of traumatic experiences is important to identify particularly vulnerable subpopulations. We examined the heterogeneous associations between disaster-related traumatic experiences and postdisaster 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 before the disaster and the 2 follow-up surveys were conducted 2.5 and 5.5 years after (n = 1150 to n = 1644 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 postdisaster mental health outcomes to examine the heterogeneous associations by 51 predisaster characteristics of the individuals.
Results:
We found that, even when there was no population average association between disaster-related trauma and subsequent mental health outcomes, some subgroups experienced severe impacts. We also identified and compared characteristics of the most and least vulnerable groups (ie, top versus 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 preexisting 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.