Early prediction of mental health problems following military deployment: Integrating pre- and post-deployment factors in neural network models
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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.
In: Journal of Psychiatric Research, Vol. 163, 07.2023, p. 109-117.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Early prediction of mental health problems following military deployment
T2 - Integrating pre- and post-deployment factors in neural network models
AU - Karstoft, Karen Inge
AU - Eskelund, Kasper
AU - Gradus, Jaimie L.
AU - Andersen, Søren B.
AU - Nissen, Lars R.
N1 - Publisher Copyright: © 2023
PY - 2023/7
Y1 - 2023/7
N2 - 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.
AB - 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.
KW - Military personnel
KW - Neural networks
KW - Post-deployment mental health
KW - Prevention
KW - Risk factors
U2 - 10.1016/j.jpsychires.2023.05.014
DO - 10.1016/j.jpsychires.2023.05.014
M3 - Journal article
C2 - 37209616
AN - SCOPUS:85159471026
VL - 163
SP - 109
EP - 117
JO - Journal of Psychiatric Research
JF - Journal of Psychiatric Research
SN - 0022-3956
ER -
ID: 371924208