American support of public programs for Veterans: Estimates from a national survey including a discrete choice experiment

Abstract: Do Americans see veterans as particularly deserving or simply as other members of their community? From a nationally representative survey fielded between June and September 2021 with over 2,000 respondents, we find that Americans state high levels of support for veterans and are willing to pay additional tax dollars to provide assistance programs. We find that most Americans support free health care, free college, and affordable housing for all Americans, and the support is notably stronger for programs for veterans. From a discrete choice experiment, we find that Americans are willing to pay hundreds of dollars in additional taxes to provide assistance programs to either veterans or to all community members, and Americans are willing to pay significantly more for certain programs for veterans. In addition, we look at differences in willingness to pay based on military and political affiliation and find significant differences in willingness to pay by political affiliation.

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.