• Mary Jo Pugh, Erin P Finley, Chen-Pin Wang, Laurel A Copeland, Carlos A Jaramillo, Alicia A Swan, Christine A Elnitsky, Luci K Leykum, Eric M Mortensen, Blessen A Eapen, Polly H Noel, Jacqueline A Pugh, and TRACC Research Team.
• a South Texas Veterans Health Care System , San Antonio , TX , USA.
• Brain Inj. 2016 Jan 1; 30 (12): 1481-1490.

ObjectivesTo identify and validate trajectories of comorbidity associated with traumatic brain injury in male and female Iraq and Afghanistan war Veterans (IAV).MethodsDerivation and validation cohorts were compiled of IAV who entered the Department of Veterans Affairs (VA) care and received 3 years of VA care between 2002-2011. Chronic disease and comorbidities associated with deployment including TBI were identified using diagnosis codes. A latent class analysis (LCA) of longitudinal comorbidity data was used to identify trajectories of comorbidity.ResultsLCA revealed five trajectories that were similar for women and men: (1) Healthy, (2) Chronic Disease, (3) Mental Health, (4) Pain and (5) Polytrauma Clinical Triad (PCT: pain, mental health and TBI). Two additional classes found in men were 6) Minor Chronic and 7) PCT with chronic disease. Among these gender-stratified trajectories, it was found that women were more likely to experience headache (Pain trajectory) and depression (Mental Health trajectory), while men were more likely to experience lower back pain (Pain trajectory) and substance use disorder (Mental Health trajectory). The probability of TBI was highest in the PCT-related trajectories, with significantly lower probabilities in other trajectories.ConclusionsIt was found that TBI was most common in PCT-related trajectories, indicating that TBI is commonly comorbid with pain and mental health conditions for both men and women. The relatively young age of this cohort raises important questions regarding how disease burden, including the possibility of neurodegenerative sequelae, will accrue alongside normal age-related decline in individuals with TBI. Additional 'big data' methods and a longer observation period may allow the development of predictive models to identify individuals with TBI that are at-risk for adverse outcomes.

32 keywords...

hide…