Suicide is a leading cause of death in adolescent populations. While numerous risk factors have been identified for adolescent suicidality, the underlying mechanisms remain elusive. The current manuscript attempts to elucidate these mechanisms through the application of the interpersonal theory for suicide. A review of the published literature is presented with an emphasis on risk and protective factors for adolescent suicidality. Current risk assessment protocols and treatment approaches are then summarized. We conclude with recommendations for future research.
To test Coyne's (1976b) theory of depression, students' levels of depressive symptoms, reassurance seeking, and self-esteem were assessed at Time 1, and their same-gender roommates' appraisals of them were assessed 5 weeks later. Mildly depressed students engaged in the type of reassurance seeking described by Coyne. Among men, but not women, mildly depressed students were rejected if they strongly sought reassurance and had low self-esteem but not if they did not seek reassurance or had high self-esteem. Although induction of depressed symptoms in roommates did occur, this contagion effect did not account for the depression-rejection relationship. The prediction that unsupportive, intolerant, or unempathic others would be particularly likely to respond with rejection to reassurance-seeking depressed students with low self-esteem received partial support. Implications for future work on the interpersonal aspects of depression are discussed.
High rates of mental disorders, suicidality, and interpersonal violence early in the military career have raised interest in implementing preventive interventions with high-risk new enlistees. The Army Study to Assess Risk and Resilience in Servicemembers (STARRS) developed risk-targeting systems for these outcomes based on machine learning methods using administrative data predictors. However, administrative data omit many risk factors, raising the question whether risk targeting could be improved by adding self-report survey data to prediction models. If so, the Army may gain from routinely administering surveys that assess additional risk factors. The STARRS New Soldier Survey was administered to 21,790 Regular Army soldiers who agreed to have survey data linked to administrative records. As reported previously, machine learning models using administrative data as predictors found that small proportions of high-risk soldiers accounted for high proportions of negative outcomes. Other machine learning models using self-report survey data as predictors were developed previously for three of these outcomes: major physical violence and sexual violence perpetration among men and sexual violence victimization among women. Here we examined the extent to which this survey information increases prediction accuracy, over models based solely on administrative data, for those three outcomes. We used discrete-time survival analysis to estimate a series of models predicting first occurrence, assessing how model fit improved and concentration of risk increased when adding the predicted risk score based on survey data to the predicted risk score based on administrative data. The addition of survey data improved prediction significantly for all outcomes. In the most extreme case, the percentage of reported sexual violence victimization among the 5% of female soldiers with highest predicted risk increased from 17.5% using only administrative predictors to 29.4% adding survey predictors, a 67.9% proportional increase in prediction accuracy. Other proportional increases in concentration of risk ranged from 4.8% to 49.5% (median = 26.0%). Data from an ongoing New Soldier Survey could substantially improve accuracy of risk models compared to models based exclusively on administrative predictors. Depending upon the characteristics of interventions used, the increase in targeting accuracy from survey data might offset survey administration costs.
