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PROJECT SUMMARY/ABSTRACT BACKGROUND: A number of studies describe physical or mental conditions among OEF-OIF veterans. It is becoming clear, however, that many veterans have multiple conditions concomitantly which increases the complexity of their treatment. The growing body of research on OEF-OIF veterans suggests that there are patterns of comorbid conditions that cluster together. The VA HSR&D funded a Traumatic Brain Injury State of the Art Conference (TBI SOTA) in May, 2008 that highlighted gaps in our current knowledge and recognized that a systematic identification of common clusters of comorbidities (hereafter comorbidity clusters) is needed to understand the long-term comorbidity patterns and project the health care needs of this veteran cohort, particularly those with TBI. OBJECTIVES: Objective 1. Identify comorbidity clusters among OEF-OIF veterans at baseline and describe patterns of comorbidity trajectories (stable vs. deterioration) over 3 years. Objective 2. Identify risk factors for trajectories of deterioration vs. stability. Objective 3. Compare VA health care utilization for individuals with stable comorbidity trajectories vs. those who exhibit patterns of deterioration. Objective 4. Examine differences in resilient coping, social support and measures of mental health for OEF-OIF VA patients in each comorbidity trajectory (stable or deterioration) in a random sample stratified to represent trajectories within each initial comorbidity cluster. METHODS: This is a prevalent cohort study that will combine data from VA national data repositories (Austin, Pharmacy Benefits Management database, TBI screening database, OEF-OIF database), primary data collection (survey) and national VistA Web chart abstraction to accomplish our objectives. This study will identify initial comorbidity clusters for individuals first seen in VA care (FY02-08). We will then use latent class analysis in conjunction with expert clinical opinion to identify comorbidity trajectories of stablility or deterioration within each comorbidity cluster. We will validate comorbidity clusters by examining the relationship between combat-related exposures and symptoms, and by comparing utilization among individuals with stable and deterioration comorbidity trajectories within each comorbidity cluster. We will then identify a random sample of individuals stratified by initial comorbidity cluster and trajectory to conduct more comprehensive examination using survey and chart abstraction data. Using a Modified Dillman approach, we will obtain survey data from approximately 2,000 OEF-OIF VA patients. Surveys will measure mental health symptoms, coping strategies, social support, structural aspects of relationships, and employment status. We will obtain data from pre- and post-deployment health assessments/ re-assessments (PDHRA) in addition to smoking history, family history of chronic disease, and other relevant clinical variables via VistA Web chart abstraction. The PDHRA will provide baseline information on self-reported health, relationship concerns, and mental health screening instruments. We will use descriptive and bivariate statistics to examine the relationships among self-reported resilient coping strategies, social support, depression severity and comorbidity trajectory, controlling for pre-and post-deployment initial self-reported health and deployment concerns. Generalized linear mixed effects modeling will be conducted to examine the associations between comorbidity trajectory and resilient coping, social support, and measures of mental health.
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VA-DoD Long-Term Impact of Military-Relevant Brain Injury Consortium (LIMBIC): Phenotypes of Persistent Comorbidity in Post‐9/11 Era Veterans with mTBI
VA-DoD Long-Term Impact of Military-Relevant Brain Injury Consortium (LIMBIC): Phenotypes of Persistent Comorbidity in Postâ9/11 Era Veterans with mTBI
VA-DoD Long-Term Impact of Military-Relevant Brain Injury Consortium (LIMBIC): Phenotypes of Persistent Comorbidity in Postâ9/11 Era Veterans with mTBI
VA-DoD Long-Term Impact of Military-Relevant Brain Injury Consortium (LIMBIC): Data and Biostatistics Core
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