Multimethod Examination of Individual and Environmental Factors Associated with Alcohol Use and Behavioral Health Care Access Among Veterans - Project Summary/Abstract Veterans of the post-9/11 era represent a dynamic and evolving population with a wide range of behavioral health needs. Alcohol use and co-occurring symptoms of posttraumatic stress disorder (PTSD) and depression remain highly prevalent among veterans and continue to interfere with functioning, recovery, and care engagement. While much of the existing research has focused on veterans receiving care within the Veterans Affairs (VA) system, approximately half of all veterans receive care outside the VA. As such, studies that exclusively examine VA-connected populations may fail to capture the full range of experiences, needs, and barriers relevant to the broader veteran community. Existing research has also been largely cross-sectional and focused primarily on individual-level factors, without adequately accounting for contextual, psychological, and environmental influences on behavioral health outcomes and treatment access. To address these gaps, the present study will recruit a longitudinal panel of 2,000 post-9/11 veterans not currently engaged in VA behavioral health care. Participants will complete biannual assessments over a four-year period. The project will begin with qualitative interviews with a subset of 65 veterans to explore life course experiences, contextual stressors, and perceived facilitators and barriers to behavioral health care. These interviews will help refine the development of survey measures to ensure their relevance and comprehensiveness. Quantitative data will include a wide range of individual (e.g., stress exposure, coping responses, trauma history, resilience) and environmental (e.g., neighborhood context, geographic access to care, local violence exposure, proximity to alcohol outlets) predictors. Outcome data will focus on behavioral health symptoms, including alcohol use/disorder, PTSD, and depression, as well as behavioral health care access, preparatory care behaviors, and attitudes toward treatment. Analyses will include machine learning models that integrate both individual- and community-level variables to identify which factors are most predictive of behavioral health symptoms and care outcomes. Results will be used to rank predictors in terms of importance and directionality, providing a foundation for more targeted prevention, intervention, and policy development efforts.
