Social needs and health outcomes in two rural Veteran populations

Abstract: Background: Addressing social needs is a priority for many health systems, including the Veterans Health Administration (VA). Nearly a quarter of Veterans reside in rural areas and experience a high social need burden. The purpose of this study was to assess the prevalence and association with health outcomes of social needs in two distinct rural Veteran populations. Methods: We conducted a survey (n = 1150) of Veterans at 2 rural VA sites, 1 in the Northeast and 1 in the Southeast (SE), assessing 11 social needs (social disconnection, employment, finance, food, transportation, housing, utilities, internet access, legal needs, activities of daily living [ADL], and discrimination). We ran weighted-logistic regression models to predict the probability of experiencing four outcomes (poor access to care, no-show visits, and self-rated physical and mental health) by individual social need. Findings: More than 80% of Veterans at both sites reported ≥1 social need, with social disconnection the most common; Veterans at the SE site reported much higher rates. A total of 9 out of 11 needs were associated with higher probability of poor physical and mental health, particularly financial needs (average marginal effect [AME]: 0.21-0.32, p < 0.001) and ADL (AME: 0.27-0.34, p < 0.001). We found smaller associations between social needs and poor access to care and no-show visits. Conclusion: High prevalence of social needs in rural Veteran population and significant associations with four health outcomes support the prioritization of addressing social determinants of health for health systems. Differences in the findings between sites support tailoring interventions to specific patient populations.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.