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Cited by 3Posttraumatic Stress Disorder ResearchMigration, Health and TraumaOccupational Health and Performance

Early prediction of mental health problems following military deployment: Integrating pre- and post-deployment factors in neural network models

Karstoft, Karen-Inge, Eskelund, Kasper, Gradus, Jaimie L., Andersen, Søren B., Nissen, Lars R.·2023·Journal of Psychiatric Research
DOI 10.1016/j.jpsychires.2023.05.014
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Abstract

Military personnel deployed to war zones are at increased risk of mental health problems such as posttraumatic stress disorder (PTSD) or depression. Early pre- or post-deployment identification of those at highest risk of such problems is crucial to target intervention to those in need. However, sufficiently accurate models predicting objectively assessed mental health outcomes have not been put forward. In a sample consisting of all Danish military personnel who deployed to war zones for the first (N = 27,594), second (N = 11,083) and third (N = 5,161) time between 1992 and 2013, we apply neural networks to predict psychiatric diagnoses or use of psychotropic medicine in the years following deployment. Models are based on pre-deployment registry data alone or on pre-deployment registry data in combination with post-deployment questionnaire data on deployment experiences or early post-deployment reactions. Further, we identified the most central predictors of importance for the first, second, and third deployment. Models based on pre-deployment registry data alone had lower accuracy (AUCs ranging from 0.61 (third deployment) to 0.67 (first deployment)) than models including pre- and post-deployment data (AUCs ranging from 0.70 (third deployment) to 0.74 (first deployment)). Age at deployment, deployment year and previous physical trauma were important across deployments. Post-deployment predictors varied across deployments but included deployment exposures as well as early post-deployment symptoms. The results suggest that neural network models combining pre- and early post-deployment data can be utilized for screening tools that identify individuals at risk of severe mental health problems in the years following military deployment.

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Authors
Karstoft, K.-I., Eskelund, K., Gradus, J. L., Andersen, S. B., & Nissen, L. R.
Affiliations
  • University of Copenhagen (DK)
  • Oticon Medical (Denmark) (DK)
  • Boston University (US)
  • The Danish Veteran Centre (DK)
Corresponding author
Karstoft, Karen-Inge
ORCID
Venue
Journal of Psychiatric Research
Publisher
Elsevier BV
Published
2023-07
Volume
163
Pages
109-117
ISSN
0022-3956 (ISSN-L)
DOI
10.1016/j.jpsychires.2023.05.014
OpenAlex
W4372203539
Cited by
3
References
41
Citations by year
2025: 2 · 2023: 1
FWCI
0.87
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Journal of Psychiatric Research (publishedVersion)
  • PubMed (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion)
Funders
Danish Foundation TrygFonden (122623)
Topics
  • Social Sciences › Psychology › Clinical Psychology › Posttraumatic Stress Disorder Research
  • Social Sciences › Psychology › Clinical Psychology › Migration, Health and Trauma
  • Health Sciences › Health Professions › Occupational Therapy › Occupational Health and Performance
Keywords
military deployment, mental illness, post-traumatic stress disorder, depression, psychotropic drug use, deployment experience, risk screening, neural network model
MeSH terms
Military Deployment, Humans, Mental Health, Military Personnel, Risk Factors, Stress Disorders, Post-Traumatic, Neural Networks, Computer

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