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

Abstract

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 exposureoutcome combinations. Conclusions:We found considerable heterogeneity in the association between disaster-related traumatic experiences and subsequent mental health problems.

Sociodemographic Heterogeneity in the Associations of Social Isolation With Mortality

“…Fourth, our study did not examine multidimensional heterogeneity; age, gender, income, and employment may collectively influence the association between social isolation and mortality. Future research, potentially employing machine learning, could explore this complex heterogeneity . Fifth, we could not determine the temporal sequence of exposure, moderators, and confounding factors due to simultaneous baseline assessments.…”

Long-Term Associations between Disaster-Related Home Loss and Health and Well-Being of Older Survivors: Nine Years after the 2011 Great East Japan Earthquake and Tsunami

“…For example, the adverse effects of home loss on survivors’ mental health has been reported to be greater among people with socioeconomically disadvantaged backgrounds because they tend to have fewer resources (e.g., income, social support) that they can mobilize to cope with the stressors. 40 Taken together, disasters and subsequent home loss affect survivors’ health on average but also tend to widen social inequalities in health. Thus, highly affected subpopulations need to be considered in planning policies for resilience building and post-disaster responses.…”

Statistical Modeling: The Three Cultures

“…Predicting counterfactuals and potential outcomes enables an inductive analysis of treatment heterogeneity Imai & Ratkovic, 2013;Künzel et al, 2018). While average treatment effect analysis focuses on the aggregated effect of an exposure on a population, effect heterogeneity analysis focuses on more granular effects, the group-specific effects disaggregated by subpopulations Shiba, Daoud, Hikichi, et al, 2022;Shiba, Daoud, Kino, et al, 2022). For example, although a famine or an economic crisis is likely to affect an entire country adversely, some combination of socioeconomic factors may protect certain groups better than others (Daoud & Johansson, 2020).…”