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Identification of a subgroup of post-9/11 veterans with unique combat experiences and worse clinical outcomes
Lay Kodama1, Alexa Verkuilen2, Georgios Asimakopoulos2
1VA San Diego Healthcare System (VASDHS), San Diego, CA, United States; Department of Psychiatry, University of California San Diego, La Jolla, CA, United States.
Objectives:
Prior studies have examined the influence of military deployments and combat exposure on post-9/11 Veterans' health and well-being. Fewer studies, however, have focused on the extent to which specific combat experiences relate to clinical outcomes in this Veteran cohort. We sought to identify distinct groups of post-9/11 Veterans based on their combat experience profiles and examine the relationship between combat experiences and clinical outcomes.
Methods:
We used data from 21,995 deployed, combat-exposed, Iraq/Afghanistan-era Veterans who enrolled in the VA Million Veteran Program (MVP). Combat experiences were evaluated using the "Combat Experiences Scale" from the Deployment Risk and Resilience Inventory.
Results:
We identified five distinct subgroups of Veterans, one of which was characterized by high frequency of exposure across all types of combat experiences. These Veterans had worse self-reported mental health symptoms, cognitive symptoms, and perceptions of their own health compared to other Veterans. Generalized linear models adjusting for age, sex, race, and ethnicity demonstrated that combat and TBI history were independently associated with worse clinical outcomes, with Veterans in the high-combat/TBI history group reporting the poorest functioning.
Conclusions:
Our findings highlight the utility of examining specific combat experiences to identify at-risk post-9/11 Veterans. These findings could facilitate the development of empirically-informed supportive measures to intervene earlier in the post-deployment period and improve clinical outcomes. Future studies to better characterize this at-risk Veteran group using genetic data available through MVP will further inform these prediction models.
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