Traumatic brain injury and suicidality among military veterans: The mediating role of social integration

A novel approach to anxiety level prediction using small sets of judgment and survey variables

Anxiety, a condition characterized by intense fear and persistent worry, affects millions each year and, when severe, is distressing and functionally impairing. Numerous machine learning frameworks have been developed and tested to predict features of anxiety and anxiety traits. This study extended these approaches by using a small set of interpretable judgment variables (n = 15) and contextual variables (demographics, perceived loneliness, COVID-19 history) to (1) understand the relationships between these variables and (2) develop a framework to predict anxiety levels [derived from the State Trait Anxiety Inventory (STAI)]. This set of 15 judgment variables, including loss aversion and risk aversion, models biases in reward/aversion judgments extracted from an unsupervised, short (2-3 min) picture rating task (using the International Affective Picture System) that can be completed on a smartphone. The study cohort consisted of 3476 de-identified adult participants from across the United States who were recruited using an email survey database. Using a balanced Random Forest approach with these judgment and contextual variables, STAI-derived anxiety levels were predicted with up to 81% accuracy and 0.71 AUC ROC. Normalized Gini scores showed that the most important predictors (age, loneliness, household income, employment status) contributed a total of 29-31% of the cumulative relative importance and up to 61% was contributed by judgment variables. Mediation/moderation statistics revealed that the interactions between judgment and contextual variables appears to be important for accurately predicting anxiety levels. Median shifts in judgment variables described a behavioral profile for individuals with higher anxiety levels that was characterized by less resilience, more avoidance, and more indifference behavior. This study supports the hypothesis that distinct constellations of 15 interpretable judgment variables, along with contextual variables, could yield an efficient and highly scalable system for mental health assessment. These results contribute to our understanding of underlying psychological processes that are necessary to characterize what causes variance in anxiety conditions and its behaviors, which can impact treatment development and efficacy.

Loneliness partially mediates the relation between substance use and suicidality in Veterans

America has experienced a rapid increase in loneliness, substance use, and suicidality. This increase is particularly deleterious for Veterans, who, as compared to nonmilitary-connected civilians, experience elevated rates of loneliness, substance use, and suicidality. In this project we investigated the link between loneliness, substance use, and suicidality, paying particular attention to the mediational role of loneliness between substance use and suicidality. 1,469 Veterans (male, n = 1004, 67.2%; female, n = 457, 32.3%; transgender/non-binary/prefer not to say, n = 8, 0.5%) answered online surveys in the Mental Health and Well-Being Project. Items assessed participants on psychosocial antecedents of health and wellness. Pearson correlations and mediational models were used to determine if loneliness, substance use, and suicidality were related and if loneliness mediated the link between substance use and suicidality. Results indicated that loneliness, substance use, and suicidality were significantly and positively related (rs = .33-.42, ps < .01). Additionally, loneliness partially mediated the link between substance use and suicidality (β = .08 [.06-.10]), suggesting that, within the context of substance use in Veterans, loneliness may account for significant variance in suicidality. Together findings suggest the Veterans Health Administration should support, fund, and study community engagement activities that could reduce the development or intensity of substance use, loneliness, and suicidality in Veterans.