Citation

Jaramillo CA, Cooper DB, Wang CP, Tate DF, Eapen BC, York GE, Pugh MJ. Brain Imaging Behav. 2015; 9(3): 445-455.

Affiliation

Polytrauma Rehabilitation Center, South Texas Veterans Healthcare System (ALMD), 7400 Merton Minter Blvd (11C6), San Antonio, TX, 78229-4404, USA, jaramilloc3@uthscsa.edu.

Copyright

(Copyright © 2015, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s11682-015-9402-8

PMID

25963862

Abstract

U. S. veterans of Iraq and Afghanistan are known to have a high prevalence of traumatic brain injury (TBI), posttraumatic stress disorder (PTSD), and depression, which are often comorbid and share many symptoms. Attempts to describe this cohort by single diagnoses have limited our understanding of the complex nature of this population. The objective of this study was to identify subgroups of Iraq and Afghanistan veterans (IAVs) with distinct compositions of symptoms associated with TBI, PTSD, and depression. Our cross-sectional, observational study included 303,716 IAVs who received care in the Veterans Health Administration in 2010-2011. Symptoms and conditions were defined using International Classification of Diseases, Ninth Revision codes and symptom-clusters were identified using latent class analysis. We identified seven classes with distinct symptom compositions. One class had low probability of any condition and low health care utilization (HCU) (48 %). Other classes were characterized by high probabilities of mental health comorbidities (14 %); chronic pain and sleep disturbance (20 %); headaches and memory problems (6 %); and auditory problems (2.5 %). Another class had mental health comorbidities and chronic pain (7 %), and the last had high probabilities of most symptoms examined (3 %). These last two classes had the highest likelihood of TBI, PTSD, and depression and were identified as high healthcare utilizers. There are subgroups of IAVs with distinct clusters of symptom that are meaningfully associated with TBI, PTSD, depression, and HCU. Additional studies examining these veteran subgroups could improve our understanding of this complex comorbid patient population.

Language: en