Longitudinal studies support the safety and ethics of virtual reality suicide as a research method.

Many have expressed concerns about the safety and ethics of conducting suicide research, especially intense suicide research methods that expose participants to graphic depictions of suicidality. We conducted two studies to evaluate the effects of one such method called virtual reality (VR) suicide. Study 1 tested the effects of VR suicide exposure over the course of one month in participants with (n = 56) and without a history of suicidality (n = 50). Study 2 exposed some participants to VR suicide scenarios (n = 79) and others to control scenarios (n = 80). Participants were invited to complete a follow-up assessment after an average of 2 years. For both studies, the presence of suicidality post exposure was the primary outcome, with closely related constructs (e.g., capability for suicide, agitation) as secondary outcomes. Study 1 found no pre-post increases in suicidality or related variables, but revealed several significant decreases associated with small to medium effect sizes in suicide-related constructs. In Study 2, VR suicide exposure did not cause any significant increases in suicidality or related variables. Together with prior research, these findings suggest that methods involving intense suicide stimuli appear safe and consistent with utilitarian ethics.

Interventions for suicide and self-injury: A meta-analysis of randomized controlled trials across nearly 50 years of research.

Self-injurious thoughts and behaviors (SITBs) are major public health concerns impacting a wide range of individuals and communities. Despite major efforts to develop and refine treatments to reduce SITBs, the efficacy of SITB interventions remains unclear. To provide a comprehensive summary of SITB treatment efficacy, we conducted a meta-analysis of published randomized controlled trials (RCTs) that have attempted to reduce SITBs. A total of 591 published articles from 1,125 unique RCTs with 3,458 effect sizes from the past 50 years were included. The random-effects meta-analysis yielded surprising findings: The overall intervention effects were small across all SITB outcomes; despite a near-exponential increase in the number of RCTs across five decades, intervention efficacy has not improved; all SITB interventions produced similarly small effects, and no intervention appeared significantly and consistently stronger than others; the overall small intervention effects were largely maintained at follow-up assessments; efficacy was similar across age groups, though effects were slightly weaker for child/adolescent populations and few studies focused on older adults; and major sample and study characteristics (e.g., control group type, treatment target, sample size, intervention length) did not consistently moderate treatment efficacy. This meta-analysis suggests that fundamental changes are needed to facilitate progress in SITB intervention efficacy. In particular, powerful interventions target the necessary causes of pathology, but little is known about SITB causes (vs. SITB correlates and risk factors). The field would accordingly benefit from the prioritization of research that aims to identify and target common necessary causes of SITBs. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Anticipated consequences as the primary causes of suicidal behavior: Evidence from a laboratory study.

Due to the limitations of conducting experimental studies on suicide, little is known about its causes. Based on basic behavioral research, we hypothesized that the anticipated consequences of suicidal behavior (e.g., stress relief) are the primary causes of suicidal behavior, and experienced antecedents (e.g., stress) are secondary causes. We evaluated this general hypothesis by testing the causal effects of these two broad phenomena on virtual reality (VR) suicide in 497 participants across five groups. On their own, experienced antecedent manipulations (i.e., stress and rejection) did not significantly increase VR suicide rates relative to the control group. On its own, the anticipated consequence manipulation (i.e., instruction that engaging in VR suicide would allow one to avoid a future stressor) caused a large increase in the VR suicide rate. In the context of an experienced antecedent manipulation (i.e., stress), this anticipated consequence manipulation caused an even larger increase in the VR suicide rate. These findings suggest that the anticipated consequences of suicidal behavior (e.g., avoidance or escape from something unpleasant, attaining something pleasant) are the primary causes of suicidal behavior, and that experienced antecedents (e.g., stress) serve as secondary causes when they make an anticipated consequence of suicidal behavior seem more appealing.

The Differences Between Individuals Engaging in Nonsuicidal Self-Injury and Suicide Attempt Are Complex (vs. Complicated or Simple).

BACKGROUND: Why do some people engage in nonsuicidal self-injury (NSSI) while others attempt suicide? One way to advance knowledge about this question is to shed light on the differences between people who engage in NSSI and people who attempt suicide. These groups could differ in three broad ways. First, these two groups may differ in a simple way, such that one or a small set of factors is both necessary and sufficient to accurately distinguish the two groups. Second, they might differ in a complicated way, meaning that a specific set of a large number of factors is both necessary and sufficient to accurately classify them. Third, they might differ in a complex way, with no necessary factor combinations and potentially no sufficient factor combinations. In this scenario, at the group level, complicated algorithms would either be insufficient (i.e., no complicated algorithm produces good accuracy) or unnecessary (i.e., many complicated algorithms produce good accuracy) to distinguish between groups. This study directly tested these three possibilities in a sample of people with a history of NSSI and/or suicide attempt. METHOD: A total of 954 participants who have either engaged in NSSI and/or suicide attempt in their lifetime were recruited from online forums. Participants completed a series of measures on factors commonly associated with NSSI and suicide attempt. To test for simple differences, univariate logistic regressions were conducted. One theoretically informed multiple logistic regression model with suicidal desire, capability for suicide, and their interaction term was considered as well. To examine complicated and complex differences, multiple logistic regression and machine learning analyses were conducted. RESULTS: No simple algorithm (i.e., single factor or small set of factors) accurately distinguished between groups. Complicated algorithms constructed with cross-validation methods produced fair accuracy; complicated algorithms constructed with bootstrap optimism methods produced good accuracy, but multiple different algorithms with this method produced similar results. CONCLUSIONS: Findings were consistent with complex differences between people who engage in NSSI and suicide attempts. Specific complicated algorithms were either insufficient (cross-validation results) or unnecessary (bootstrap optimism results) to distinguish between these groups with high accuracy.

The differences between suicide ideators and suicide attempters: Simple, complicated, or complex?

OBJECTIVE: Suicide ideators and suicide attempters might differ in 3 possible ways. First, they might differ in a simple way such that one or a small set of factors are both necessary and sufficient to distinguish between the 2 groups. Second, ideators and attempters might differ in a complicated way such that a specific combination of a large set of factors is necessary and sufficient for the distinction. Third, complex differences might exist: many possible combinations of a large set of factors may be sufficient to distinguish the 2 groups, but no combination may be necessary. This study empirically examined these possibilities. METHOD: Across 5 samples (total N = 3,869), univariate logistic regressions were conducted to test for simple differences. To test for complicated and complex differences, machine learning (ML) methods were used to identify the optimized algorithm with all variables. Subsequently, the same methods were repeated after removing the top 5 most important or discriminative variables, and a randomly selected 10% subset of variables. Multiple logistic regressions were conducted with all variables. RESULTS: Results were consistent across samples. Univariate logistic regressions on average yielded chance-level accuracy. ML algorithms with all variables showed good accuracy; substantial deviation from the optimized algorithms through the removal of variables did not result in significantly poorer performance. Multiple logistic regressions produced poor to fair accuracy. CONCLUSIONS: Differences between suicide ideators and attempters are complex. Findings suggest that their differences may be better understood on a psychological primitive level than a biopsychosocial factor level. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Brain Differences Associated with Self-Injurious Thoughts and Behaviors: A Meta-Analysis of Neuroimaging Studies.

This meta-analysis aims to evaluate whether the extant literature justifies any definitive conclusions about whether and how SITBs may be associated with brain differences. A total of 77 papers (N = 4,903) published through January 1, 2019 that compared individuals with and without SITBs were included, resulting in 882 coordinates. A pooled meta-analysis assessing for general risk for SITBs indicated a lack of convergence on structural differences. When all types of control groups were considered, functional differences in the left posterior cingulate cortex (PCC), right amygdala, left hippocampus, and right thalamus were significant using multi-level kernel density analysis (pcorrected < 0.05) but nonsignificant using activation-likelihood estimation. These results suggest that a propensity for internally-oriented, emotional processing coupled with under-active pain processing could potentially underlie SITBs, but additional research is needed to test this possibility. Separate analyses for types of SITBs suggested that the brain differences associated with deliberate self-harm were consistent with the overall findings. Checkered moderator effects were detected. Overall, the meta-analytic evidence was not robust. More studies are needed to reach definitive conclusions about whether SITBs are associated with brain differences.

User-Centered Design of a Machine Learning Intervention for Suicide Risk Prediction in a Military Setting.

Primary care represents a major opportunity for suicide prevention in the military. Significant advances have been made in using electronic health record data to predict suicide attempts in patient populations. With a user-centered design approach, we are developing an intervention that uses predictive analytics to inform care teams about their patients' risk of suicide attempt. We present our experience working with clinicians and staff in a military primary care setting to create preliminary designs and a context-specific usability testing plan for the deployment of the suicide risk indicator.

Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide.

Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial heritability of suicidal behavior; however, collecting the sample sizes necessary for successful genetic studies has remained a challenge. We utilized two different approaches in independent datasets to characterize the contribution of common genetic variation to suicide attempt. The first is a patient reported suicide attempt phenotype asked as part of an online mental health survey taken by a subset of participants (n = 157,366) in the UK Biobank. After quality control, we leveraged a genotyped set of unrelated, white British ancestry participants including 2433 cases and 334,766 controls that included those that did not participate in the survey or were not explicitly asked about attempting suicide. The second leveraged electronic health record (EHR) data from the Vanderbilt University Medical Center (VUMC, 2.8 million patients, 3250 cases) and machine learning to derive probabilities of attempting suicide in 24,546 genotyped patients. We identified significant and comparable heritability estimates of suicide attempt from both the patient reported phenotype in the UK Biobank (h2SNP = 0.035, p = 7.12 × 10-4) and the clinically predicted phenotype from VUMC (h2SNP = 0.046, p = 1.51 × 10-2). A significant genetic overlap was demonstrated between the two measures of suicide attempt in these independent samples through polygenic risk score analysis (t = 4.02, p = 5.75 × 10-5) and genetic correlation (rg = 1.073, SE = 0.36, p = 0.003). Finally, we show significant but incomplete genetic correlation of suicide attempt with insomnia (rg = 0.34-0.81) as well as several psychiatric disorders (rg = 0.26-0.79). This work demonstrates the contribution of common genetic variation to suicide attempt. It points to a genetic underpinning to clinically predicted risk of attempting suicide that is similar to the genetic profile from a patient reported outcome. Lastly, it presents an approach for using EHR data and clinical prediction to generate quantitative measures from binary phenotypes that can improve power for genetic studies.

Psychological primitives can make sense of biopsychosocial factor complexity in psychopathology.

BACKGROUND: Many agree that the biopsychosocial contributions to psychopathology are complex, yet it is unclear how we can make sense of this complexity. One approach is to reduce this complexity to a few necessary and sufficient biopsychosocial factors; although this approach is easy to understand, it has little explanatory power. Another approach is to fully embrace complexity, proposing that each instance of psychopathology is caused by a partially unique set of biopsychosocial factors; this approach has high explanatory power, but is impossible to comprehend. Due to deficits in either explanatory power or comprehensibility, both approaches limit our ability to make substantial advances in understanding, predicting, and preventing psychopathology. Thus, how can we make sense of biopsychosocial factor complexity? MAIN TEXT: There is a third possible approach that can resolve this dilemma, with high explanatory power and high comprehensibility. This approach involves understanding, predicting, and preventing psychopathology in terms of a small set of psychological primitives rather than biopsychosocial factors. Psychological primitives are the fundamental and irreducible elements of the mind, mediating all biopsychosocial factor influences on psychopathology. All psychological phenomena emerge from these primitives. Over the past decade, this approach has been successfully applied within basic psychological science, most notably affective science. It explains the sum of the evidence in affective science and has generated several novel research directions. This approach is equally applicable to psychopathology. The primitive-based approach does not eliminate the role of biopsychosocial factors, but rather recasts them as indeterminate causal influences on psychological primitives. In doing so, it reframes research away from factor-based questions (e.g., which situations cause suicide?) and toward primitive-based questions (e.g., how are suicidality concepts formed, altered, activated, and implemented?). This is a valuable shift because factor-based questions have indeterminate answers (e.g., infinite situations could cause suicide) whereas primitive-based questions have determinate answers (e.g., there are specific processes that undergird all concepts). CONCLUSION: The primitive-based approach accounts for biopsychosocial complexity, ties clinical science more directly to basic psychological science, and could facilitate progress in understanding, predicting, and preventing psychopathology.

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).

Virtual reality suicide: Development of a translational approach for studying suicide causes.

Causal knowledge is crucial for understanding and preventing suicide. Unfortunately, we have little direct knowledge about suicide causes because we cannot conduct experiments that seek to make suicide more likely. In such situations, translational approaches can provide valuable, though tentative, information. We sought to establish a new translational approach by developing a laboratory approximation of suicide with new virtual reality (VR) technologies. Such an approach would allow researchers to tentatively investigate the causes of suicide by conducting experiments that introduce purported causes of suicide and observe their effects on VR suicide rates. Across three studies (total N = 498), results indicated that our two VR suicide scenarios (jumping from heights; shooting oneself) were safe; rated as unpleasant, realistic, and suicide-relevant; associated with several relevant predictors of VR suicide completion, including male sex, suicidal desire, suicidal capability, agitation, and prior suicidality; associated with reasons for not engaging in VR suicide that are similar to the reasons people give for not engaging in actual suicide; and produced 5% completion rates under neutral conditions and 25% completion rates after reward/avoid manipulations. We hope that future work further improves this approach and applies it to more directly test ideas about suicide causes and suicide prevention.

What suicide interventions should target.

Recent reviews and national statistics indicate that, so far, our field has made limited progress on fulfilling its central mission of preventing future suicidal thoughts and behaviors (STBs). We posit that a fundamental reason for our lack of progress is the way in which our field tends to think about and select STB intervention targets. Specifically, the vast majority of our intervention targets are derived from untested theoretical assertions, moderate correlates of STBs, or weak risk factors for STBs. None of these forms of evidence permits causal inferences, which is problematic because successful STB interventions must target the causes of STBs. To develop effective interventions, we must employ experimental designs to identify targets that are causal, necessary, and viable.

Self-Injurious Thoughts and Behaviors May Be More Common and Severe Among People Identifying as a Sexual Minority.

Research indicates that people who identify as a sexual minority are at higher risk of numerous negative outcomes, including self-injurious thoughts and behaviors (SITBs). The minority stress model proposes that people identifying as a sexual minority are at higher risk of these behaviors due to sexual orientation-specific stressors-however, it does not clarify whether SITBs will be more severe among these individuals. The present study tested whether SITBs are more common and more severe among people identifying as a sexual minority using several metrics, including frequency of SITB engagement, age of onset of SITB, desire to discontinue SITB engagement, and likelihood of future SITBs. Four independent research samples were used to test this model. Results were then combined and tested in an internal meta-analysis. Findings converge to indicate a longer and more severe course of SITB engagement among people identifying as a sexual minority. Future research is needed to replicate these findings and to advance the understanding of why this imbalance in risk and severity might exist, and how it can be prevented.

Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning.

BACKGROUND: Adolescents have high rates of nonfatal suicide attempts, but clinically practical risk prediction remains a challenge. Screening can be time consuming to implement at scale, if it is done at all. Computational algorithms may predict suicide risk using only routinely collected clinical data. We used a machine learning approach validated on longitudinal clinical data in adults to address this challenge in adolescents. METHODS: This is a retrospective, longitudinal cohort study. Data were collected from the Vanderbilt Synthetic Derivative from January 1998 to December 2015 and included 974 adolescents with nonfatal suicide attempts and multiple control comparisons: 496 adolescents with other self-injury (OSI), 7,059 adolescents with depressive symptoms, and 25,081 adolescent general hospital controls. Candidate predictors included diagnostic, demographic, medication, and socioeconomic factors. Outcome was determined by multiexpert review of electronic health records. Random forests were validated with optimism adjustment at multiple time points (from 1 week to 2 years). Recalibration was done via isotonic regression. Evaluation metrics included discrimination (AUC, sensitivity/specificity, precision/recall) and calibration (calibration plots, slope/intercept, Brier score). RESULTS: Computational models performed well and did not require face-to-face screening. Performance improved as suicide attempts became more imminent. Discrimination was good in comparison with OSI controls (AUC = 0.83 [0.82-0.84] at 720 days; AUC = 0.85 [0.84-0.87] at 7 days) and depressed controls (AUC = 0.87 [95% CI 0.85-0.90] at 720 days; 0.90 [0.85-0.94] at 7 days) and best in comparison with general hospital controls (AUC 0.94 [0.92-0.96] at 720 days; 0.97 [0.95-0.98] at 7 days). Random forests significantly outperformed logistic regression in every comparison. Recalibration improved performance as much as ninefold - clinical recommendations with poorly calibrated predictions can lead to decision errors. CONCLUSIONS: Machine learning on longitudinal clinical data may provide a scalable approach to broaden screening for risk of nonfatal suicide attempts in adolescents.

Is externalizing psychopathology a robust risk factor for suicidal thoughts and behaviors? A meta-analysis of longitudinal studies.

OBJECTIVE: Our primary objective was to determine the potency of externalizing psychopathology as a risk factor for suicidal thoughts and behaviors (STBs). METHOD: We conducted a random effects meta-analysis of 174 prospective studies (839 unique statistical tests) examining externalizing psychopathology and suicidal thoughts and behaviors (STBs) published prior to December 8, 2017. The weighted mean odds ratios for the overall relationship between externalizing psychopathology and STBs were below 2.00 in magnitude, and all risk factor subcategories were also fairly modest predictors of STBs. Taking publication bias into account reduced the magnitude of these associations, particularly for death. Although externalizing psychopathology modestly predicts STBs, this may be due to design limitations of existing studies. Future research should employ shorter follow-up periods, consider risk factors in combination, and focus on forms of externalizing psychopathology that have not been studied extensively. RESULTS: The weighted mean odds ratios for the overall relationship between externalizing psychopathology and STBs were below 2.00 in magnitude, and all risk factor subcategories were also fairly modest predictors of STBs. Taking publication bias into account reduced the magnitude of these associations, particularly for death. Additionally, our results were mostly consistent regardless of sample age, sample severity, follow-up length, and predictor scale. CONCLUSIONS: Although externalizing psychopathology modestly predicts STBs, this may be due to design limitations of existing studies. Future research should employ shorter follow-up periods, consider risk factors in combination, and focus on forms of externalizing psychopathology that have not been studied extensively.

Depression and hopelessness as risk factors for suicide ideation, attempts and death: meta-analysis of longitudinal studies.

BACKGROUND: Many studies have documented robust relationships between depression and hopelessness and subsequent suicidal thoughts and behaviours; however, much weaker and non-significant effects have also been reported. These inconsistencies raise questions about whether and to what degree these factors confer risk for suicidal thoughts and behaviours.AimsThis study aimed to evaluate the magnitude and clinical utility of depression and hopelessness as risk factors for suicide ideation, attempts and death. METHOD: We conducted a meta-analysis of published studies from 1971 to 31 December 2014 that included at least one longitudinal analysis predicting suicide ideation, attempt or death using any depression or hopelessness variable. RESULTS: Overall prediction was weaker than anticipated, with weighted mean odds ratios of 1.96 (1.81-2.13) for ideation, 1.63 (1.55-1.72) for attempt and 1.33 (1.18-1.49) for death. Adjusting for publication bias further reduced estimates. Effects generally persisted regardless of sample severity, sample age or follow-up length. CONCLUSIONS: Several methodological constraints were prominent across studies; addressing these issues would likely be fruitful moving forward.Declaration of interestNone.

Understanding suicide risk within the Research Domain Criteria (RDoC) framework: A meta-analytic review.

BACKGROUND: The field is in need of novel and transdiagnostic risk factors for suicide. The National Institute of Mental Health's Research Domain Criteria (RDoC) provides a framework that may help advance research on suicidal behavior. METHOD: We conducted a meta-analytic review of existing prospective risk and protective factors for suicidal thoughts and behaviors (ideation, attempts, and deaths) that fall within one of the five RDoC domains or relate to a prominent suicide theory. Predictors were selected from a database of 4,082 prospective risk and protective factors for suicide outcomes. RESULTS: A total of 460 predictors met inclusion criteria for this meta-analytic review and most examined risk (vs. protective) factors for suicidal thoughts and behaviors. The overall effect of risk factors was statistically significant, but relatively small, in predicting suicide ideation (weighted mean odds ratio: wOR = 1.72; 95% CI: 1.59-1.87), suicide attempt (wOR = 1.66 [1.57-1.76), and suicide death (wOR = 1.41 [1.24-1.60]). Across all suicide outcomes, most risk factors related to the Negative Valence Systems domain, although effect sizes were of similar magnitude across RDoC domains. CONCLUSIONS: This study demonstrated that the RDoC framework provides a novel and promising approach to suicide research; however, relatively few studies of suicidal behavior fit within this framework. Future studies must go beyond the "usual suspects" of suicide risk factors (e.g., mental disorders, sociodemographics) to understand the processes that combine to lead to this deadly outcome.

Affect toward the self and self-injury stimuli as potential risk factors for nonsuicidal self-injury.

Few risk factors for nonsuicidal self-injury (NSSI) have been identified. This study investigated diminished aversion toward self-injury (i.e., NSSI, suicide/death stimuli) and self-criticism as unique NSSI risk factors. After terminating a treatment study, 154 adults with a recent and frequent NSSI history completed self-report and computer-based measures of psychopathology, implicit and explicit self-criticism, and implicit aversion to NSSI and suicide/death. Participants were then contacted 4 weeks later to test factors predicting NSSI frequency over this follow-up period. Diminished aversion toward NSSI stimuli and self-criticism significantly predicted NSSI 4 weeks later. These effects were unique from other theoretically important predictors, such as past week NSSI frequency and total number of NSSI methods used. Findings provide support that erosion of barriers to NSSI (e.g., aversion to self-injurious stimuli, decreased self-worth) may facilitate continued engagement in these dangerous behaviors. Results shed light on potential treatment targets for NSSI.

Accounting for Diversity in Suicide Research: Sampling and Sample Reporting Practices in the United States.

Research on suicidal thoughts and behaviors (STB) has identified many risk factors, but whether these findings generalize to diverse populations remains unclear. We review longitudinal studies on STB risk factors over the past 50 years in the United States and evaluate the methodological practices of sampling and reporting sample characteristics. We found that articles frequently reported participant age and sex, less frequently reported participant race and ethnicity, and rarely reported participant veteran status or lesbian, gay, bisexual, and transgender status. Sample reporting practices modestly and inconsistently improved over time. Finally, articles predominantly featured White, non-Hispanic, young adult samples.

Demographics as predictors of suicidal thoughts and behaviors: A meta-analysis.

BACKGROUND: Certain demographic factors have long been cited to confer risk or protection for suicidal thoughts and behaviors. However, many studies have found weak or non-significant effects. Determining the effect strength and clinical utility of demographics as predictors is crucial for suicide risk assessment and theory development. As such, we conducted a meta-analysis to determine the effect strength and clinical utility of demographics as predictors. METHODS: We searched PsycInfo, PubMed, and GoogleScholar for studies published before January 1st, 2015. Inclusion criteria required that studies use at least one demographic factor to longitudinally predict suicide ideation, attempt, or death. The initial search yielded 2,541 studies, 159 of which were eligible. A total of 752 unique statistical tests were included in analysis. RESULTS: Suicide death was the most commonly studied outcome, followed by attempt and ideation. The average follow-up length was 9.4 years. The overall effects of demographic factors studied in the field as risk factors were significant but weak, and that of demographic factors studied as protective factors were non-significant. Adjusting for publication bias further reduced effect estimates. No specific demographic factors appeared to be strong predictors. The effects were consistent across multiple moderators. CONCLUSIONS: At least within the narrow methodological constraints of the existing literature, demographic factors were statistically significant risk factors, but not protective factors. Even as risk factors, demographics offer very little improvement in predictive accuracy. Future studies that go beyond the limitations of the existing literature are needed to further understand the effects of demographics.