liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Uncovering heterogeneous associations of disaster-related traumatic experiences with subsequent mental health problems: A machine learning approach
Harvard TH Chan Sch Publ Hlth, MA 02115 USA; Harvard TH Chan Sch Publ Hlth, MA 02115 USA.
Linköping University, Department of Management and Engineering, The Institute for Analytical Sociology, IAS. Linköping University, Faculty of Arts and Sciences. Harvard TH Chan Sch Publ Hlth, MA USA; Chalmers Univ Technol, Sweden.
Univ Tokyo, Japan; Kyoto Univ, Japan.
Univ Tokyo, Japan.
Show others and affiliations
2022 (English)In: Psychiatry and Clinical Neurosciences, ISSN 1323-1316, E-ISSN 1440-1819, Vol. 76, no 4, p. 97-105Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
WILEY , 2022. Vol. 76, no 4, p. 97-105
Keywords [en]
causality; depression; disasters; machine learning; posttraumatic stress symptoms
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
URN: urn:nbn:se:liu:diva-182628DOI: 10.1111/pcn.13322ISI: 000745094000001PubMedID: 34936171OAI: oai:DiVA.org:liu-182628DiVA, id: diva2:1634053
Note

Funding Agencies|National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01 AG042463]; Japan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science [KAKENHI 23243070, KAKENHI 22390400, KAKENHI 20H00557, KAKENHI 24390469]; Japanese Ministry of Health, Labour, and WelfareMinistry of Health, Labour and Welfare, Japan [H24-Choju-Wakate-009]; Strategic Research Foundation Grant-Aided Project for Private Universities from the Japanese Ministry of Education, Culture, Sports, Science, and Technology [S0991035]; Japan Agency for Medical Research and Development (AMED)Japan Agency for Medical Research and Development (AMED) [JP17dk0110017, JP18dk0110027, JP18ls0110002, JP18le0110009, JP19dk0110034, JP19dk0110037, JP20dk0110034]

Available from: 2022-02-01 Created: 2022-02-01 Last updated: 2023-03-30Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMed

Authority records

Daoud, Adel

Search in DiVA

By author/editor
Daoud, Adel
By organisation
The Institute for Analytical Sociology, IASFaculty of Arts and Sciences
In the same journal
Psychiatry and Clinical Neurosciences
Public Health, Global Health, Social Medicine and Epidemiology

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 66 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf