Predicting Suicide Attempts among Treatment‐Seeking Male Alcoholics: An Exploratory Study

Documented risk factors for suicide among alcohol‐dependent patients are sensitive but insufficiently specific to effectively identify individuals who are prone to future suicide attempt. As a first step to assess factors not previously considered, this pilot study involved a group of male alcohol‐dependent patients (N = 175) coming to detoxification to examine the potential utility of adverse childhood experiences (ACE) along with other documented events to discriminate individuals with a history of attempted suicide from their detoxifying peers. Family health history questionnaires were used to evaluate their ACEs. Receiver operating characteristic (ROC) analysis was applied to examine the predictive power of ACEs, alone or in combination with documented risk factors, to lifetime history of attempted suicide. Among our participants, 48 (27.4%) had a history of a suicide attempt and 156 (89.1%) reported at least one out of the nine categories of ACEs. Modeling by ROC analysis, we found that a cutoff of four or more ACEs plus a history of personal violence achieved the best predictive power to a history of any suicide attempt, producing a sensitivity of 0.7, specificity of 0.81, and area under curve of 0.75. A prospective study to replicate and extend our findings is necessary.     

Development of a Scale to Assess Knowledge about Suicide Postvention using Item Response Theory

Knowledge about suicide postvention (KSPV) is an important distal outcome in the evaluation of suicide prevention programs that focus on the bereaved. However, most scales are specifically tailored to the evaluation study in question and psychometric properties are often unsatisfactory. Therefore, we developed the KSPV scale. Scale properties were investigated with Rasch trees, a newly developed method in the framework of item response theory. Additionally, we provide cues for convergent validity. In summary, the scale shows satisfactory properties for assessing KSPV and could be used to evaluate suicide postvention programs more effectively.     

A Systematic Review of Suicide Prevention Programs for Military or Veterans

Military personnel and veterans have important suicide risk factors. After a systematic review of the literature on suicide prevention, seven (five in the U.S.) studies of military personnel were identified containing interventions that may reduce the risk of suicide. The effectiveness of the individual components was not assessed, and problems in methodology or reporting of data were common. Overall, multifaceted interventions for active duty military personnel are supported by consistent evidence, although of very mixed quality, and in some cases during intervals of declines in suicide rates in the general population. There were insufficient studies of U.S. Veterans to reach conclusions.     

Comparative Analysis of Suicide,Accidental, and Undetermined Cause of Death Classification

Suicide determination is not standardized across medical examiners, and many suspected suicides are later classified as accidental or undetermined. The present study investigated patterns between these three groups using a medical examiner database and 633 structured interviews with next of kin. There were similarities across all three classification groups, including rates of mental illness and psychiatric symptoms. Those classified suicide were more likely to be male, to have died in a violent fashion, and have a stronger family history of suicide. Chronic pain was very common across all three groups, but significantly higher in the accidental and undetermined groups.     

A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial

Death by suicide demonstrates profound personal suffering and societal failure. While basic sciences provide the opportunity to understand biological markers related to suicide, computer science provides opportunities to understand suicide thought markers. In this novel prospective, multimodal, multicenter, mixed demographic study, we used machine learning to measure and fuse two classes of suicidal thought markers: verbal and nonverbal. Machine learning algorithms were used with the subjects’ words and vocal characteristics to classify 379 subjects recruited from two academic medical centers and a rural community hospital into one of three groups: suicidal, mentally ill but not suicidal, or controls. By combining linguistic and acoustic characteristics, subjects could be classified into one of the three groups with up to 85% accuracy. The results provide insight into how advanced technology can be used for suicide assessment and prevention.     

Basic Psychological Needs,Suicidal Ideation,and Risk for Suicidal Behavior in Young Adults

Associations between the satisfaction of basic psychological needs of autonomy, competence, and relatedness with current suicidal ideation and risk for suicidal behavior were examined. Two logistic regressions were conducted with a cross‐sectional database of 440 university students to examine the association of need satisfaction with suicidal ideation and risk for suicidal behavior, while controlling for demographics and depressive symptoms. Suicidal ideation was reported by 15% of participants and 18% were found to be at risk for suicidal behavior. A one standard deviation increase in need satisfaction reduced the odds of suicidal ideation by 53%, OR (95% CI) = 0.47 (0.33–0.67), and the odds of being at risk for suicidal behavior by 50%, OR (95% CI) = 0.50 (0.37–0.69). Young adults whose basic psychological needs are met may be less likely to consider suicide and engage in suicidal behavior. Prospective research is needed to confirm these associations.