Family member incarceration and loneliness among low-income U.S. Veterans

Abstract: Exposure to incarceration can have significant ramifications for one's social relationships. However, the study of how the incarceration of a family member influences loneliness, including among specific segments of the population that experience elevated levels of loneliness, such as U.S. military veterans, has gone understudied. This study aimed to examine the relationship between family member incarceration and perceptions of loneliness among a sample of low-income U.S. military veterans. Data are from the National Veteran Homeless and Other Poverty Experiences Study-a national survey of low-income U.S. veterans collected in December 2022 and January 2023. Multiple Poisson is used to assess the relationship between family member incarceration and a loneliness index, and multinomial logistic regression was used to estimate the relationship with specific constructs in the loneliness index. The results indicate that respondents who ever experienced the incarceration of a family member reported significantly more loneliness (incidence risk ratio = 1.189, 95% CI [1.035, 1.366]). Further, analyses of the specific items in the loneliness index revealed that family member incarceration was related to an increased risk of reporting feelings of often lacking companionship (relative risk ratio = 1.598, 95% CI [1.077, 2.370]) and often feeling isolated from others (1.711, 95% CI [1.014, 2.886]). Given the potential adverse consequences of loneliness and family member incarceration for well-being, the results from this study emphasize the need for increased attention and coordinated approaches in addressing feelings of loneliness, developing efforts to mitigate the harms of family member incarceration within the U.S. veteran community.

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.