Perceived barriers to mental healthcare among spouses of military service members
Abstract: Barriers to mental healthcare services are reported among military service members. However, little is known about these barriers among the spouses of military personnel, who face unique stressors and may subsequently be at high-need for mental health services. Understanding barriers to care among this vulnerable population may help improve access to psychological services. The current study utilized data from the Millennium Cohort Family Study. Participants were referred by their military spouses or through targeted mailers. Participants completed self-report measures of mood, psychosocial functioning, and perceived barriers to mental healthcare via web- or paper-based surveys. A factor analysis was conducted to identify subscales of the barriers to mental healthcare measure, and logistic regressions were conducted adjusting for relevant sociodemographic variables, to determine psychosocial factors associated with likelihood of reporting barriers to mental healthcare. The sample comprised 9,666 military spouses (86% female; Mage: 27.73 ± 5.09; 29.2% racial/ethnic minority; 19.5% with prior/current military service). Logistic factors were the most frequently reported barrier to care (63%), followed by negative beliefs about mental healthcare (52%), fear of social/occupational consequences (35%), and internalized stigma (32%). Spouses with prior or current military service themselves and individuals with a psychiatric condition were most likely to report barriers to mental healthcare. A preponderance of military spouses reported barriers to mental healthcare services. Prospective data are needed to elucidate the associations between barriers to care and mental healthcare utilization. Efforts may be warranted to improve access to mental healthcare among the spouses of military personnel.
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.