Evidence map›Paper›PMID 34936171›Full record

ArticlePsychiatry and clinical neurosciences2022

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

Koichiro Shiba, Adel Daoud, Shiho Kino, Daisuke Nishi, Katsunori Kondo, Ichiro Kawachi

Open access · greenAbstract read
In one paragraph

Article in Psychiatry and clinical neurosciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

  1. AI applications in disaster governance with health approach: A scoping review.Archives of public health = Archives belges de sante publique · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 5 institutions in 3 countries.

Koichiro ShibaDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.ORCID https://orcid.org/0000-0001-7956-6485
Adel DaoudCenter for Population and Development Studies, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Shiho KinoDepartment of Health and Social Behavior, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Daisuke NishiDepartment of Mental Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID https://orcid.org/0000-0001-9349-3294
Katsunori KondoCenter for Preventive Medical Sciences, Chiba University, Chiba, Japan.
Ichiro KawachiDepartment of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Harvard University · USChiba University · JPKyoto University · JPLinköping University · SEThe University of Tokyo · JP

Funding

Impact of social cohesion on functional recovery after earthquake and tsunamiR01AG042463 · NIA · HARVARD SCHOOL OF PUBLIC HEALTH · PI KAWACHI, ICHIRO · 2013 to 2022
$4.9M
Japan Agency for Medical Research and Development JP17dk0110017Japan Agency for Medical Research and Development JP18dk0110027Japan Agency for Medical Research and Development JP18le0110009Japan Agency for Medical Research and Development JP18ls0110002Japan Agency for Medical Research and Development JP19dk0110034Japan Agency for Medical Research and Development JP19dk0110037Japan Agency for Medical Research and Development JP20dk0110034Japan Society for the Promotion of Science KAKENHI 20H00557Japan Society for the Promotion of Science KAKENHI 22390400Japan Society for the Promotion of Science KAKENHI 23243070Japan Society for the Promotion of Science KAKENHI 24390469Ministry of Education, Culture, Sports, Science and Technology S0991035Ministry of Health, Labour and Welfare H24-Choju-Wakate-009NIA NIH HHS R01 AG042463
6 · The paper itself

Abstract

aimUnderstanding 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.

methodsData 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.

resultsWe 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.

conclusionsWe found considerable heterogeneity in the association between disaster-related traumatic experiences and subsequent mental health problems.

Indexed as

Mental HealthStress Disorders, Post-TraumaticAgedHumansMachine LearningProspective StudiesSurvivorsTsunamiscausalitydepressiondisastersmachine learningposttraumatic stress symptoms

Identifiers

PMID34936171
PMCPMC9102396
OpenAlexW4200181895

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.