Impact of SARS-CoV-2 Infection on Long-Term Depression Symptoms among Veterans

Abstract: Background: Prior research demonstrates that SARS-COV-2 infection can be associated with a broad range of mental health outcomes including depression symptoms. Veterans, in particular, may be at elevated risk of increased depression following SARS-COV-2 infection given their high rates of pre-existing mental and physical health comorbidities. However, few studies have tried to isolate SARS-COV-2 infection associations with long term, patient-reported depression symptoms from other factors (e.g., physical health comorbidities, pandemic-related stress). Objective: To evaluate the association between SARS-COV-2 infection and subsequent depression symptoms among United States Military Veterans. Design: Survey-based non-randomized cohort study with matched comparators. Participants: A matched-dyadic sample from a larger, stratified random sample of participants with and without known to SARS-COV-2 infection were invited to participate in a survey evaluating mental health and wellness 18-months after their index infection date. Sampled participants were stratified by infection severity of the participant infected with SARS-COV-2 (hospitalized or not) and by month of index date. A total of 186 participants in each group agreed to participate in the survey and had sufficient data for inclusion in analyses. Those in the uninfected group who were later infected were excluded from analyses. Main measures: Participants were administered the Patient Health Questionnaire-9 as part of a phone interview survey. Demographics, physical and mental health comorbidities were extracted from VHA administrative data. Key results: Veterans infected with SARS-COV-2 had significantly higher depression symptoms scores compared with those uninfected. In particular, psychological symptoms (e.g., low mood, suicidal ideation) scores were elevated relative to the comparator group (M(Infected) = 3.16, 95%CI: 2.5, 3.8; M(Uninfected) = 1.96, 95%CI: 1.4, 2.5). Findings were similar regardless of history of depression. Conclusion: SARS-COV-2 infection was associated with more depression symptoms among Veterans at 18-months post-infection. Routine evaluation of depression symptoms over time following SARS-COV-2 infection is important to facilitate adequate assessment and treatment.

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.