Suicide plan prevalence, recurrence, and longitudinal association with nonfatal suicide attempt.

BACKGROUND: Each year, millions of people develop suicide plans. These plans are assumed to indicate imminent suicide risk, yet this has rarely been tested. The present study seeks to address two questions: (1) how prevalent are specific thoughts of suicide plans among individuals with a history of suicidal thoughts and behaviors and (2) do suicide plans confer risk of future suicide attempts in the short term? METHODS: Secondary data analysis was performed on a longitudinal dataset (N = 1021). Prevalence and frequencies of suicide planning features (i.e., method, time, place) at baseline and 3, 14, and 28 days post-baseline were calculated. Logistic regressions were conducted to assess whether suicide plans confer risk of suicide attempts across a 28-day follow-up period. RESULTS: Suicide planning more commonly involved thoughts of method than place and/or time. High variability in suicide planning was evident and thoughts of suicide plans frequently recurred. Contrary to assumptions, suicide plans displayed weak associations with nonfatal suicide attempt across the 28-day follow-up period. CONCLUSIONS: Suicide plans appear heterogeneous in nature. They do not appear to play a strong role in predicting nonfatal suicide attempts. Re-evaluation of the central role that suicide plans occupy within clinical risk assessments may be warranted.

A meta-analysis of longitudinal peer influence effects in childhood and adolescence.

For decades, psychological research has examined the extent to which children's and adolescents' behavior is influenced by the behavior of their peers (i.e., peer influence effects). This review provides a comprehensive synthesis and meta-analysis of this vast field of psychological science, with a goal to quantify the magnitude of peer influence effects across a broad array of behaviors (externalizing, internalizing, academic). To provide a rigorous test of peer influence effects, only studies that employed longitudinal designs, controlled for youths' baseline behaviors, and used "external informants" (peers' own reports or other external reporters) were included. These criteria yielded a total of 233 effect sizes from 60 independent studies across four different continents. A multilevel meta-analytic approach, allowing the inclusion of multiple dependent effect sizes from the same study, was used to estimate an average cross-lagged regression coefficient, indicating the extent to which peers' behavior predicted changes in youths' own behavior over time. Results revealed a peer influence effect that was small in magnitude (߯ = .08) but significant and robust. Peer influence effects did not vary as a function of the behavioral outcome, age, or peer relationship type (one close friend vs. multiple friends). Time lag and peer context emerged as significant moderators, suggesting stronger peer influence effects over shorter time periods, and when the assessment of peer relationships was not limited to the classroom context. Results provide the most thorough and comprehensive synthesis of childhood and adolescent peer influence to date, indicating that peer influence occurs similarly across a broad range of behaviors and attitudes. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Raising doubt about the anticipated consequences of suicidal behavior: Evidence for a new approach from laboratory and real-world experiments.

OBJECTIVE: Converging evidence from basic science and experimental suicide research suggest that the anticipated consequences of suicide may have direct causal effects on suicidal behavior and accordingly represent a promising intervention target. Raising doubt about individuals' desirable anticipated consequences of suicide may be one means of disrupting this target. We tested this possibility across two complementary experimental studies. METHOD: Study 1 tested the effects of raising doubt about desirable anticipated consequences on virtual reality (VR) suicide in the lab, randomizing 413 participants across four conditions. In Study 2, 226 suicidal adults were randomized to an anticipated consequence manipulation or control condition then re-assessed at 2- and 8-weeks post-baseline. RESULTS: In Study 1, anticipating that engaging in VR suicide would guarantee a desirable outcome significantly increased the VR suicide rate; conversely, raising doubt about the desirable anticipated consequences significantly reduced the VR suicide rate. In Study 2, raising doubt about the anticipated consequences of attempting suicide by firearm significantly reduced the perceived lethality of firearms as well as self-predicted likelihood of future suicide attempts, with effects sustained at 2-week follow-up. CONCLUSIONS: Findings suggest that raising doubt about desirable anticipated consequences of suicide merits further research as one potential approach to inhibit suicidal behavior.

Do suicidal desire and facets of capability for suicide predict future suicidal behavior? A longitudinal test of the desire-capability hypothesis.

Capability-based models propose that people die by suicide because they want to, and they can. Despite the intuitive appeal of this hypothesis, longitudinal evidence testing its predictive validity has been limited. This study tested the predictive validity of the desire-capability hypothesis. A total of 1,020 self-injuring and/or suicidal adults were recruited worldwide online from suicide, self-injury, and mental health web forums. After baseline assessment, participants completed follow-up assessments at 3, 14, and 28 days after baseline. Participant retention was high (>90%) across all follow-up assessments. Analyses examined the effect of the statistical interaction between suicidal desire and indices of capability for suicide on future nonfatal suicide attempts. Main analyses focused on the fearlessness about death facet of capability for suicide; exploratory analyses examined preparations for suicide. Logistic regression was used to predict suicide attempt status at follow-up; zero-inflated negative binomial models were implemented to predict the frequency of nonfatal suicide attempts at follow-up. Results were consistent across models, finding very little evidence of the desire-capability interaction as a significant predictor of suicide attempt status or frequency at follow-up. We close with a discussion of the limitations of this study as well as the implications of our findings for future suicide science. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Sleep disturbances as risk factors for suicidal thoughts and behaviours: a meta-analysis of longitudinal studies.

In recent years, there has been a growing interest in understanding the relationship between sleep and suicide. Although sleep disturbances are commonly cited as critical risk factors for suicidal thoughts and behaviours, it is unclear to what degree sleep disturbances confer risk for suicide. The aim of this meta-analysis was to clarify the extent to which sleep disturbances serve as risk factors (i.e., longitudinal correlates) for suicidal thoughts and behaviours. Our analyses included 156 total effects drawn from 42 studies published between 1982 and 2019. We used a random effects model to analyse the overall effects of sleep disturbances on suicidal ideation, attempts, and death. We additionally explored potential moderators of these associations. Our results indicated that sleep disturbances are statistically significant, yet weak, risk factors for suicidal thoughts and behaviours. The strongest associations were found for insomnia, which significantly predicted suicide ideation (OR 2.10 [95% CI 1.83-2.41]), and nightmares, which significantly predicted suicide attempt (OR 1.81 [95% CI 1.12-2.92]). Given the low base rate of suicidal behaviours, our findings raise questions about the practicality of relying on sleep disturbances as warning signs for imminent suicide risk. Future research is necessary to uncover the causal mechanisms underlying the relationship between sleep disturbances and suicide.

Model complexity improves the prediction of nonsuicidal self-injury.

OBJECTIVE: Efforts to predict nonsuicidal self-injury (NSSI; intentional self-injury enacted without suicidal intent) to date have resulted in near-chance accuracy. Incongruence between theoretical understanding of NSSI and the traditional statistical methods to predict these behaviors may explain this poor prediction. Whereas theoretical models of NSSI assume that the decision to engage in NSSI is relatively complex, statistical models used in NSSI prediction tend to involve simple models with only a few theoretically informed variables. The present study tested whether more complex statistical models would improve NSSI prediction. METHOD: Within a sample of 1,021 high-risk self-injurious and/or suicidal individuals, we examined the accuracy of three different model types, of increasing complexity, in predicting NSSI across 3, 14, and 28 days. Univariate logistic regressions of each predictor and multiple logistic regression with all predictors were conducted for each timepoint and compared with machine learning algorithms derived from all predictors. RESULTS: Results demonstrated that model complexity was associated with predictive accuracy. Multiple logistic regression models (AUCs 0.70-0.72) outperformed univariate logistic models (average AUCs 0.56). Machine learning models that produced algorithms modeling complex associations across variables produced the strongest NSSI prediction across all time points (AUCs 0.87-0.90). These models outperformed all multiple logistic regression models, including those involving identical study variables. Machine learning algorithm performance remained strong even after the most important factor across algorithms was removed. CONCLUSIONS: Results parallel recent findings in suicide research and highlight the complexity that underlies NSSI. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Machine learning in suicide science: Applications and ethics.

For decades, our ability to predict suicide has remained at near-chance levels. Machine learning has recently emerged as a promising tool for advancing suicide science, particularly in the domain of suicide prediction. The present review provides an introduction to machine learning and its potential application to open questions in suicide research. Although only a few studies have implemented machine learning for suicide prediction, results to date indicate considerable improvement in accuracy and positive predictive value. Potential barriers to algorithm integration into clinical practice are discussed, as well as attendant ethical issues. Overall, machine learning approaches hold promise for accurate, scalable, and effective suicide risk detection; however, many critical questions and issues remain unexplored.