Hostname: page-component-745bb68f8f-v2bm5 Total loading time: 0 Render date: 2025-02-06T09:23:24.795Z Has data issue: false hasContentIssue false
  • English
  • Français

Characterizing the phenomenology of passive suicidal ideation: a systematic review and meta-analysis of its prevalence, psychiatric comorbidity, correlates, and comparisons with active suicidal ideation

Published online by Cambridge University Press:  07 January 2020

Richard T. Liu Open the ORCID record for Richard T. Liu [Opens in a new window] ,
Alexandra H. Bettis  and
Taylor A. Burke
Richard T. Liu*
Affiliation:
Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Bradley Hospital, 1011 Veterans Memorial Parkway, East Providence, RI02915, USA
Alexandra H. Bettis
Affiliation:
Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Bradley Hospital, 1011 Veterans Memorial Parkway, East Providence, RI02915, USA
Taylor A. Burke
Affiliation:
Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Bradley Hospital, 1011 Veterans Memorial Parkway, East Providence, RI02915, USA
*
Author for correspondence: Richard T. Liu, E-mail: rtliupsych@gmail.com
Rights & Permissions [Opens in a new window]

Abstract

Background

Compared to active ideation, passive ideation remains relatively understudied and its clinical importance poorly defined. The weight that should be accorded passive ideation in clinical risk assessment is therefore unclear.

Methods

We conducted a systematic review and meta-analysis of the prevalence of passive ideation, its psychiatric comorbidity, associated sociodemographic characteristics, as well as psychological and environmental correlates. For reference, pooled effects were also calculated for direct comparisons of passive and active ideation with respect to potential correlates. Relevant articles published since inception to 9 September 2019 were identified through a systematic search of MEDLINE and PsycINFO.

Results

A total of 86 studies were included in this review. The prevalence of passive ideation was high across sample types, ranging from 5.8% for 1-year prevalence to 10.6% for lifetime prevalence in the general population. Passive ideation was strongly associated with sexual minority status, psychiatric comorbidity, psychological characteristics implicated in risk, and suicide attempts. Preliminary evidence exists for a large association with suicide deaths. The effect sizes for individual correlates of passive and active ideation were largely equivalent and mostly non-significant in head-to-head comparisons.

Conclusions

Passive ideation is a prevalent clinical phenomenon associated with significant psychiatric comorbidity. Current evidence also suggests notable similarities exist between passive and active ideation in terms of psychiatric comorbidity and psychological and other characteristics traditionally associated with risk.

Keywords

Epidemiologymeta-analysissuicidal ideationsuicide
Type
Review Article
Information
Psychological Medicine , Volume 50 , Issue 3 , February 2020 , pp. 367 - 383
Copyright
Copyright © The Author(s) 2020. Published by Cambridge University Press

Introduction

Suicide is one of the leading causes of death worldwide and reducing the rate of its occurrence is a global imperative (Lozano et al., Reference Lozano, Naghavi, Foreman, Lim, Shibuya, Aboyans and Murray2012; World Health Organization, 2014). One of the top three risk factors for suicide deaths (Franklin et al., Reference Franklin, Ribeiro, Fox, Bentley, Kleiman, Huang and Nock2017), and an important clinical concern in its own right, is suicidal ideation. Understanding the phenomenology and etiology of suicidal thoughts is therefore essential for advancing strategies to assess the risk for and to prevent suicidal behavior. Suicidal ideation ranges in severity from passive ideation (i.e. a desire to be dead) to active ideation (i.e. a desire to kill oneself). The idea that passive ideation is of comparably moderate clinical severity may often lead to the conclusion in suicide risk assessments that individuals presenting with passive ideation are at relatively lower risk than those with active ideation for engaging in suicidal behavior, and to decisions regarding clinical care being made accordingly (Jacobs, Reference Jacobs2009; National Action Alliance for Suicide Prevention, 2014; Simon, Reference Simon2014). It is uncertain, however, to what degree this position is empirically supported. Moreover, in a recent report commissioned by the National Institute of Mental Health, the National Action Alliance for Suicide Prevention has put forth the view that passive ideation may in fact be comparable to active ideation in its association with negative mental health outcomes, including suicidal behavior (National Action Alliance for Suicide Prevention, 2014).

Hindering critical evaluation of this issue is the relative want of research on passive ideation, especially compared to the considerable body of the empirical literature on active ideation. Indeed, prevalence estimates of suicidal ideation in several of the most important epidemiological surveys to date [e.g. the World Health Organization World Mental Health (WMH) Survey Initiative (WHO World Mental Health Survey Consortium, 2004) as well as the National Comorbidity Survey Replication and Adolescent Supplement (NCS-R and NCS-A; Husky et al., Reference Husky, Olfson, He, Nock, Swanson and Merikangas2012; Kessler, Berglund, Borges, Nock, & Wang, Reference Kessler, Berglund, Borges, Nock and Wang2005; Nock et al., Reference Nock, Green, Hwang, McLaughlin, Sampson, Zaslavsky and Kessler2013)] are in actuality of active ideation only. That passive ideation has received less attention in clinical research is also reflected by the fact that several of the most essential measures of depression and suicide include assessments of active but not passive ideation (e.g. Beck, Brown, & Steer, Reference Beck, Brown and Steer1996; Kessler & Üstün, Reference Kessler and Üstün2004; Kovacs, Reference Kovacs2010; Osman et al., Reference Osman, Bagge, Gutierrez, Konick, Kopper and Barrios2001). Consequently, several fundamental aspects of passive ideation remain poorly understood. This relative neglect of passive ideation in the empirical literature is doubtless in some measure a reflection of the greater clinical importance accorded active ideation in comparison to passive ideation. The clinical weight of passive ideation is therefore unclear, and insofar as passive ideation is clinically important, the scale of suicidal ideation as a public health problem is likely to be significantly underestimated in epidemiological data.

As an important step toward characterizing the phenomenology of passive ideation and its clinical significance, the current review provides a quantitative synthesis systematically characterizing the empirical literature to date on passive ideation. First, pooled estimates of the prevalence of passive ideation were calculated for different time-frames (e.g. current/past week and lifetime) and populations (e.g. psychiatric and community). Pooled effects were derived for sociodemographic, clinical, psychological, and other correlates of passive ideation. For each of these correlates, corresponding pooled effects from the same studies were also calculated for active ideation, thereby to facilitate comparisons between passive and active ideation. For this reason too, pooled effects were also calculated whenever possible for direct comparisons between active and passive ideation with respect to correlates of interest. Thus, we aimed to address the following, with active ideation serving as a reference point: (i) what are the sociodemographic characteristics associated with passive ideation; (ii) the psychiatric comorbidity of passive ideation, thereby providing a preliminary evaluation of the clinical importance of passive ideation; and (iii) the strength of psychological and environmental correlates of passive ideation, thereby identifying candidate risk factors to be evaluated in future research ultimately to inform future risk identification and clinical intervention strategies.

Method

Search strategy and eligibility criteria

A systematic search of the literature was conducted in PsycINFO and MEDLINE to identify the studies of potential relevance to the current review published from inception to 9 September 2019. The following search string was applied: (passive and suicid*) OR ‘passive SI’ OR ‘desire for death’ OR ‘suicidal desire’ OR ‘death ideation’ OR ‘thought of death’ OR ‘thoughts of death.’ The search results were limited to: (i) English-language publications and (ii) peer-reviewed journals. This search strategy yielded a total of 2192 articles, of which 1668 were unique reports. In cases where the eligibility of an article could not be ruled out based on the title and abstract, the full text was also examined.

The study inclusion criteria were: (i) passive ideation was analyzed separately from other constructs (e.g. active ideation); (ii) passive ideation was assessed systematically over a standardized time-frame; and (iii) quantitative data were presented on either the prevalence of passive ideation or its correlates. In the case of studies where more information on the measurement of the constructs of interest was needed to determine study eligibility, every effort was made to obtain additional details in other publications describing the measure (e.g. other publications based on the same dataset) or by contacting the corresponding author.

Data extraction

We extracted data for six study characteristics. These included three sample characteristics: (i) mean age of sample, (ii) sample type (i.e. epidemiological, community, at-risk/mixed/medical, and psychiatric), and (iii) percentage of female participants in the study sample. Data for three study design characteristics were also extracted: (i) suicidal ideation instrument (i.e. self-report v. interview), (ii) time-frame of suicidal ideation assessment, and (iii) whether an assessment of ‘pure’ passive suicidal ideation was used (i.e. passive ideation without co-occurring active ideation).

Data analysis

All analyses were conducted with Comprehensive Meta-Analysis Version 3.3.070 (Biostat, 2014). The standardized mean difference (SMD; Cohen's d) was used as the primary index of effect size for analyses of potential correlates of passive and active ideation, respectively. An SMD of 0.20 is interpreted as a small effect size, 0.50 as medium, and 0.80 as large (Kraemer et al., Reference Kraemer, Morgan, Leech, Gliner, Vaske and Harmon2003). All pooled effects were calculated such that values >0 reflected a positive association between the correlate of interest and suicidal ideation (i.e. the correlate is associated with greater experiences of suicidal ideation). For analyses directly comparing passive to active ideation with respect to a given correlate of interest, effect sizes were calculated such that values larger than zero indicated that the correlate was more strongly associated with active than passive ideation. Weighted effect sizes were calculated by pooling effects across all relevant studies. For all analyses, random-effects models were generated in preference to fixed-effects models, so as to account for the high expected heterogeneity across studies resulting from differences in samples, measures, and design. Random-effects models are more appropriate than fixed-effects models in cases where high heterogeneity is observed, in that they account for this heterogeneity by incorporating both sampling and study-level errors, with the pooled effect size representing the mean of a distribution of true effect sizes instead of a single true effect size. In contrast, fixed-effects models estimate only within-study variance, as it assumes that a single true effect size exists across all studies and any variance detected is due strictly to sampling error. Heterogeneity across the studies was evaluated using the I 2 statistic. I 2 indicates the percentage of the variance in an effect estimate that is due to heterogeneity across studies rather than sampling error (i.e. chance). Low heterogeneity is indicated by I 2 values of around 25%, and moderate heterogeneity by I 2 values of 50%. Substantial heterogeneity that is due to real differences in study samples and methodology is indicated by an I 2 value of 75%, which suggests that the observed heterogeneity is more than would be expected with random error (Higgins, Thompson, Deeks, & Altman, Reference Higgins, Thompson, Deeks and Altman2003).

High heterogeneity indicates the need to conduct moderator analyses to account for potential sources of this heterogeneity. Each potential moderator was assessed individually, with the effect size at each level of the moderator estimated. Additionally, given that passive and active ideation often co-occur, moderator analysis was conducted to determine whether the prevalence estimates for passive ideation differed as a function of whether individual studies assessed ‘pure’ passive ideation.

A common concern in conducting meta-analyses is the possibility of publication bias. Studies with small effect sizes or non-significant findings are less likely to be published, and thus may be more likely to be excluded from meta-analyses, resulting in a potentially inflated estimate of the overall effect size. The following publication bias indices were calculated to assess for the presence of potential publication bias: Duval and Tweedie's trim-and-fill analysis (Duval & Tweedie, Reference Duval and Tweedie2000) and Egger's regression intercept (Egger, Davey Smith, Schneider, & Minder, Reference Egger, Davey Smith, Schneider and Minder1997). Duval and Tweedie's trim-and-fill analysis produces an estimate of the number of missing studies based on the asymmetry in a funnel plot of the standard error of each study in a meta-analysis (based on the study's sample size) against the study's effect size. This analysis also calculates an effect size estimate and confidence interval, adjusting for these missing studies. It is important to note that this procedure assumes homogeneity of effect sizes, and thus, its results must be interpreted with a degree of caution in cases where significant heterogeneity is present. Egger's regression intercept also provides an estimate of potential publication bias using a linear regression approach assessing study effect sizes relative to their standard error.

Results

Based on the inclusion criteria, we excluded 1231 reports based on their titles and abstracts. After this initial screen, 344 additional reports were excluded based on a detailed full-text review, resulting in 93 studies satisfying the eligibility criteria. Additional data required for meta-analysis were obtained from the authors of seven of these studies (Barrigón et al., Reference Barrigón, Berrouiguet, Carballo, Bonal-Giménez, Fernández-Navarro, Pfang and Baca-García2017; Glassmire, Tarescavage, Burchett, Martinez, & Gomez, Reference Glassmire, Tarescavage, Burchett, Martinez and Gomez2016; Guidry & Cukrowicz, Reference Guidry and Cukrowicz2016; Handley, Adams, Manly, Cicchetti, & Toth, Reference Handley, Adams, Manly, Cicchetti and Toth2019; Jahn, Poindexter, Graham, & Cukrowicz, Reference Jahn, Poindexter, Graham and Cukrowicz2012; Rufino, Viswanath, Wagner, & Patriquin, Reference Rufino, Viswanath, Wagner and Patriquin2018; Tal et al., Reference Tal, Mauro, Reynolds, Shear, Simon, Lebowitz and Zisook2017). Five studies included prevalence data for which not enough unique effects existed for meta-analysis (e.g. 6 days) and were thus excluded from this review. For studies with overlapping samples, determination of which study to include in the meta-analysis was based, in descending order, on: (i) inclusion of sufficient reported data for meta-analysis and (ii) largest sample size for the relevant analysis. In cases where two studies used overlapping samples but examined different associations (e.g. passive ideation with different correlates), both studies were retained for the relevant analyses. Whenever it remained unclear after full-text inspection whether two studies reported on overlapping samples, the study authors were contacted to seek clarity on this issue. Seventeen studies featured overlapping samples and two were excluded at this stage, resulting in a final set of 86 studies included in the current review (see Fig. 1 and Table 1). Only three of these studies featured longitudinal analyses with passive ideation as an outcome (Nrugham, Larsson, & Sund, Reference Nrugham, Larsson and Sund2008; Raue, Meyers, Rowe, Heo, & Bruce, Reference Raue, Meyers, Rowe, Heo and Bruce2007; Schimanski, Mouat, Billinghurst, & Linscott, Reference Schimanski, Mouat, Billinghurst and Linscott2017), precluding consideration of this design feature in the analyses of correlates.

Fig. 1. PRISMA flow chart of literature search.

Table 1. Study characteristics

APS-SF, Adolescent Psychopathology Scale – Short Form; AUDADIS-IV, The Alcohol Use Disorder and Associated Disabilities Interview Schedule-IV; BSIS, Beck Scale for Suicide Ideation; CIDI, Composite International Diagnostic Interview; C-SSRS, Columbia-Suicide Severity Rating Scale; DICA, Diagnostic Interview for Children and Adolescents; DIS, Diagnostic Interview Schedule; GHQ, General Health Questionnaire; GSSI, Geriatric Scale for Suicide Ideation; HRSD, Hamilton Rating Scale for Depression; ISCA-D, Interview Schedule for Children & Adolescents – Diagnostic Version; K-SADS, Kiddie Schedule for Affective Disorders and Schizophrenia; MINI, Mini-International Neuropsychiatric Interview; MMPI-2, Minnesota Multiphasic Personality Inventory; MOODS-SR, Mood Spectrum Self-Report; PSS, Paykel Suicide Scale; PSIS, Passive Suicide Ideation Scale; SCISH-CP, Structured Clinical Interview for Suicide History in Chronic Pain; SCS, Social Concerns Scale; SIBQ, Suicide Ideation and Behavior Questionnaire; SSM, study-specific measure; I, interview; Q, questionnaire.

1, 2, 3, 4, 5, 6Studies with identical superscripts were drawn from same or overlapping samples but presented unique data included in this review.

a The sample size, mean age, and percentage female for participants included in relevant analyses, rather than of the entire study sample, are presented and were incorporated in moderator analyses whenever available. For ease of presentation, whenever the sample size, mean age, or percentage female varied across multiple relevant analyses within a study, data for the cumulative number of unique participants across these analyses are presented here, and the sample size used in each analysis was retained in the relevant meta-analysis for purposes of obtaining weighted effect sizes.

b These studies allowed for estimates of the prevalence of passive ideation in a clinical subgroup of the full study sample.

c These studies drew from a sample that overlaps with those of others included in this review but conducted analyses with a different measure of suicidal ideation.

Prevalence of passive ideation

Pooled prevalence rates for passive ideation were calculated for four different sample types (epidemiological, community, at-risk/mixed/medical, and psychiatric) across four different time-frames (current/1-week, 1 month, 1 year, and lifetime). Epidemiological samples were a subset of those included in the analyses of community samples. The prevalence of passive ideation was highest in psychiatric samples, ranging from 33.57% for current/1-week ideation to 47.03% for lifetime ideation, and lowest in epidemiological samples, ranging from 2.35% for current/1-week ideation to 10.57% for lifetime ideation (Table 2).

Table 2. Prevalence rates for passive suicidal ideation by sample type

CI, confidence interval; k, number of unique effects; N, total number of participants included in pooled analyses.

Heterogeneity was uniformly high across all prevalence analyses, indicating that moderator analyses were warranted. Moderator analyses were conducted for each time-frame, with age, percentage of female participants in each sample, and measure type (i.e. self-report v. interview) evaluated as candidate moderators. Results were generally consistent across the four time-points (Table 3). Specifically, age significantly moderated prevalence estimates for all time-frames, with the exception of current/1-week passive ideation. For the three remaining time-frames, age was negatively associated with the prevalence of passive ideation; samples with a lower mean age generally had higher prevalence rates than did samples with a higher mean age. Neither sample sex composition nor measure type emerged as a significant moderator of passive ideation prevalence rate for any time-frame. Prevalence estimates for passive ideation did not differ as a function of whether ‘pure’ passive ideation was assessed or if passive ideation was assessed irrespective of the co-occurrence of active ideation.

Table 3. Moderator analyses for the prevalence of passive suicidal ideation

CI, confidence interval; k, number of unique effects; N, total number of subjects included in pooled analyses.

a In moderator analyses of the construct of passive suicidal ideation, prevalence of ‘pure’ passive suicidal ideation was based only on individuals who endorsed passive but not active suicidal ideation; studies were considered conservatively not to have assessed ‘pure’ passive suicidal ideation if they either included individuals with active ideation in their assessment of passive ideation or were unclear as to whether that decision was made. For lifetime passive suicidal ideation, only one study with two unique effects for ‘pure’ passive suicidal ideation was available for lifetime prevalence of passive suicidal ideation, and thus moderator analysis was not conducted.

Analyses of publication bias were conducted for lifetime prevalence of passive ideation by sample type. Across all analyses, there was relatively little evidence of publication bias. Egger's regression test indicated significant publication bias only in the case of at-risk/mixed/medical samples (intercept = 6.77, p < 0.01) and not for the remaining three sample types (interceptEpidemiological Samples = −0.45, p = 0.88; interceptCommunity Samples = 2.09, p = 0.33; interceptPsychiatric Samples = 3.40, p = 0.26). The trim-and-fill method yielded slightly different lifetime prevalence rates for only epidemiological (adjusted prevalence = 9.74%, 95% CI 8.94–10.96%) and community samples (adjusted prevalence = 14.51%, 95% CI 12.52–16.76%). Evidence of asymmetry in the funnel plots of the effect sizes for all sample types was correspondingly modest, suggesting the limited presence of publication bias (Figs 2a–2d).

Fig. 2. Funnel plots for effect sizes in the meta-analyses. The vertical line indicates the weighted mean effect. Open circles indicate observed effects for actual studies, and closed circles indicate imputed effects for studies believed to be missing due to publication bias. The clear diamond reflects the unadjusted weighted mean effect size, whereas the black diamond reflects the weighted mean effect size after adjusting for publication bias. (a) Lifetime prevalence of passive ideation in epidemiological samples. (b) Lifetime prevalence of passive ideation in community samples. (c) Lifetime prevalence of passive ideation in at-risk/mixed/medical samples. (d) Lifetime prevalence of passive ideation in psychiatric samples.

Correlates of passive and active ideation

Table 4 presents the results for analyses of correlates of passive and active ideation with all time-frames for suicidal ideation assessment combined, and where possible, of direct comparisons between passive and active ideation with respect to correlates. Most of the effects included in these analyses were for sociodemographic and clinical factors. In general, passive and active ideation were comparable to each other in their relation to the correlates under study.

Table 4. Correlates of passive and active ideation

CI, confidence interval; k, number of unique effects; effect size estimates where k = 3 should be interpreted with a degree of caution.

a The lower end of the confidence interval was rounded down but exceeded 0.

Although passive ideation was significantly correlated with sociodemographic factors, save for ethnic minority status and being never married, the pooled effect sizes were small in almost every case. The exceptions were being divorced, which had a small-to-medium pooled effect, and sexual minority status, with a medium pooled effect. A similar pattern of results emerged for active ideation, the primary difference being that the correlation with education status was no longer significant. In head-to-head comparisons, none of the sociodemographic factors significantly differentiated between passive and active ideation, and pooled effect sizes ranged from trivial to small.

The largest effects of passive ideation were observed for clinical and psychological factors. In the case of the former, generally large and significant pooled effects were found for psychiatric problems, depression, anxiety, and psychosis. Only substance use and psychiatric treatment were not significantly correlated with passive ideation. A similar set of results was found for active ideation, again with the largest pooled effects for psychiatric problems, depression, anxiety, and psychosis. Additionally, small-to-medium significant pooled effects were evident for active ideation in relation to substance use and psychiatric care. In direct comparisons of passive and active ideation, small significant pooled effects indicating a stronger association with active ideation were observed for alcohol use problems and psychiatric treatment utilization. As for psychological factors, all except for cognitive functioning were associated with passive ideation, with large pooled effects in every case. These findings were largely mirrored in the analyses of active ideation, again with only cognitive functioning demonstrating a non-significant association and large pooled effects detected for all other factors. Direct comparison between passive and active ideation was only possible for cognitive functioning and a non-significant pooled effect was found.

All remaining factors, relating to difficulties in financial, physical, and interpersonal domains, as well as general stress, were significantly correlated with passive ideation, with the size of pooled effects ranging from small-to-medium to medium. Results for active ideation were essentially the same, with small-to-medium and medium pooled effects observed across the factors under consideration.

Finally, analyses were conducted evaluating the association between passive ideation and suicide-related outcomes, specifically suicide attempts and deaths (Table 5). A large pooled effect was observed for passive ideation in relation to suicide attempts.Footnote 1Footnote † This was also the case for the association between active ideation and attempts. Given the clinical interest in whether passive and active ideation differ in their association with suicide attempts, a preliminary analysis was conducted of this head-to-head comparison with the two available relevant studies, and a small, non-significant pooled effect was found. For the same reasons, preliminary analyses were conducted for passive and active ideation, respectively, predicting suicide deaths. Large pooled effects were detected in both cases. As only two unique effects were included in each of these preliminary analyses, caution should be taken in interpreting their results.

Table 5. Prediction of suicide attempts and deaths by passive and active ideation

CI, confidence interval; k, number of unique effects; effect size estimates where k = 2 should be interpreted with a degree of caution.

Discussion

The aim of the current review was to characterize the existing literature on passive ideation, particularly the prevalence of this clinical phenomenon, its associated sociodemographic characteristics, the nature of its psychiatric comorbidity, and its psychological and environmental correlates. Furthermore, it aimed systematically to evaluate the degree to which passive ideation differs from active ideation in the strength of its association with these correlates.

Passive ideation was highly prevalent in psychiatric populations, with approximately a third of all individuals experiencing current passive ideation and almost half of all individuals having a lifetime history of passive ideation. Although considerably lower, the prevalence of passive ideation is still concerning in epidemiological studies. Pooled estimates from these studies revealed that approximately one in 20 individuals in the general population experience passive ideation in any given year, and this figure increases to one in 10 individuals for a lifetime passive ideation. The 1-year prevalence of passive ideation in epidemiological samples (5.8%) was somewhat higher than for active ideation reported in the NCS-R (3.3%; Kessler et al., Reference Kessler, Berglund, Borges, Nock and Wang2005) and NCS-A (3.6%; Husky et al., Reference Husky, Olfson, He, Nock, Swanson and Merikangas2012). Lifetime prevalence of passive ideation in epidemiological samples in the current meta-analysis (10.6%) fell within the range of lifetime rates reported for active ideation in the WMH surveys (9.2%; Nock et al., Reference Nock, Borges, Bromet, Alonso, Angermeyer, Beautrais and Williams2008a), NCS-A (12.1%; Nock et al., Reference Nock, Green, Hwang, McLaughlin, Sampson, Zaslavsky and Kessler2013) and NCS (13.5%; Kessler, Borges, & Walters, Reference Kessler, Borges and Walters1999) There was little evidence that the prevalence estimates in the current review were influenced by publication bias.

That age was inversely related to the prevalence of passive ideation, particularly in the case of lifetime prevalence, is a curious finding warranting discussion. This finding suggests that younger individuals have a higher lifetime prevalence of passive ideation than do older counterparts, a pattern that seems counterintuitive but has also been found for lifetime prevalence of active ideation (Nock et al., Reference Nock, Borges, Bromet, Alonso, Angermeyer, Beautrais and Williams2008a). One possible explanation for the current finding is the presence of a cohort effect, with passive ideation becoming more common among younger individuals. Another explanation may be that older individuals are more likely to forget experiences of passive ideation over time, especially if they occurred many years in the past. Indeed, it is possible that passive ideation might be less memorable compared to active ideation, particularly when recall of its occurrence is over longer periods of time. In support of this latter possibility, one study has found some adolescents to forget previously endorsed suicidal ideation, albeit active ideation, over a 4-year period (Goldney, Smith, Winefield, Tiggeman, & Winefield, Reference Goldney, Smith, Winefield, Tiggeman and Winefield1991). It is also consistent with the broader literature, in which higher rates of psychiatric disorders have been found when assessed prospectively than retrospectively (Liu, Reference Liu2016; Moffitt et al., Reference Moffitt, Caspi, Taylor, Kokaua, Milne, Polanczyk and Poulton2010). These two possibilities, a cohort effect and decreased rates due to forgetting over time, are not mutually exclusive, however, and require additional research to resolve.

Regarding the inverse association for age relative to 1-month and 1-year prevalence of passive ideation, a different explanation is likely relevant. Specifically, active ideation has been found to peak during adolescence and early adulthood (Kessler et al., Reference Kessler, Borges and Walters1999), an implication of which is that prevalence of current or recent active ideation should be higher among adolescents and young adults than older age groups. It is a reasonable possibility that a similar pattern may hold for passive ideation, and it may therefore be expected to have a higher incidence among younger age groups. The absence of a similar inverse relation between age and current/1-week passive ideation, on the other hand, is likely due to the availability of only one study with a youth sample (Urrila et al., Reference Urrila, Karlsson, Kiviruusu, Pelkonen, Strandholm and Marttunen2012) in the relevant analysis. That is, this modest representation of younger samples likely limited the ability to detect potential moderating effects of age on prevalence rates.

In analyses of correlates, sociodemographic characteristics were generally modestly associated with passive ideation, and are consequently of limited utility in risk screening strategies. As these sociodemographic characteristics are generally easier and quicker to ascertain, and thus more amenable to inclusion in brief clinical risk assessment protocols, than are the other correlates included in this review, this pattern of findings underscores the significant challenges of screening for the risk for passive ideation. One notable exception is sexual minority individuals, who appear to be an especially at-risk population, with a medium pooled effect observed in the current review, a finding that is consistent with the broader suicide literature (Haas et al., Reference Haas, Eliason, Mays, Mathy, Cochran, D'Augelli and Clayton2011; Hottes, Bogaert, Rhodes, Brennan, & Gesink, Reference Hottes, Bogaert, Rhodes, Brennan and Gesink2016; O'Brien, Liu, Putney, Burke, & Aguinaldo, Reference O'Brien, Liu, Putney, Burke, Aguinaldo, Smalley, Warren and Barefoot2017). This group may therefore particularly benefit from increased preventive intervention efforts and be given greater weight when risk stratification is warranted.

Underscoring the clinical significance of passive ideation, it was associated with considerable psychiatric comorbidity as well as psychological characteristics traditionally implicated in the risk for suicide and other negative mental health outcomes, with some of the largest pooled effects being observed for these correlates. These findings appeared most robust for overall psychopathology, depression, and anxiety, based on the number of unique effects available for each analysis and the size of their corresponding pooled effects. Moreover, passive ideation was strongly associated with suicide attempts, and although preliminary, suicide deaths. Indeed, among the largest pooled effects for passive ideation were found in its association with these outcomes. Collectively, these findings point to the need for future research investigating the clinical importance of passive ideation. It is therefore concerning that passive ideation was not associated with the receipt of psychiatric care in our analyses.

Although sizeable pooled effects were observed for the remaining correlates involving general and domain-specific stress (i.e. financial, physical health, social), it is important to note that with most of these correlates (i.e. financial and physical health problems) and associated studies, the focus of research in this area has been predominantly more relevant to adult populations than to youth. Insofar as passive ideation mirrors active ideation with regards to its dramatic increase in the first onset during adolescence (Kessler et al., Reference Kessler, Borges and Walters1999; Nock, Borges, Bromet, Cha, Kessler, & Lee, Reference Nock, Borges, Bromet, Cha, Kessler and Lee2008b), a discordance exists between where the emphasis has been in the empirical literature and where it is most needed. Although identifying potential markers of risk relevant to passive ideation in adults is undoubtedly important, more research is needed to elucidate potential factors underlying this clinical phenomenon during adolescence, a developmental period of a particular risk. For example, timing of pubertal maturation may be a promising developmentally relevant candidate for future investigation, given that it has been associated with general psychopathology, depression, and anxiety (Hamilton, Hamlat, Stange, Abramson, & Alloy, Reference Hamilton, Hamlat, Stange, Abramson and Alloy2014; Hamlat, Snyder, Young, & Hankin, Reference Hamlat, Snyder, Young and Hankin2019; Reardon, Leen-Feldner, & Hayward, Reference Reardon, Leen-Feldner and Hayward2009; Ullsperger & Nikolas, Reference Ullsperger and Nikolas2017), which as mentioned above, are all strongly correlated with passive ideation.

Of note, these results are cross-sectional in nature, and thus temporal relationships between passive or active ideation and clinical, sociodemographic, suicide-related, and stress-related variables cannot be determined. It is plausible that some correlates may serve as risk factors for the onset and/or maintenance of passive or active ideation, and that these relationships may differ across the lifespan. Alternatively, at least in the case of clinical correlates, it may be possible that passive ideation may prospectively predict negative mental health outcomes, essentially functioning as a prodromal marker of risk, particularly in the case of younger individuals who may be earlier in their clinical course. Prospective research is needed to determine the nature and directionality of these relationships over time to inform screening and intervention efforts.

The similarities between passive and active ideation were notable. Not only were their pooled effects for individual correlates largely equivalent in size, but head-to-head comparisons also consistently yielded trivial to small pooled effects, most of which were not significant. When interpreted together with the aforementioned findings of psychiatric and psychological correlates, the analyses involving active ideation lend weight to the possibility that passive ideation may be comparable to active ideation in terms of clinical significance and associated risk. Although results from this meta-analysis are preliminary, our findings highlight the need for rigorous longitudinal studies to explore whether these associations are maintained over time. Specifically, the most logical next steps are to conduct studies (i) evaluating prospective predictors of passive and active ideation and (ii) directly comparing passive and active ideation in prospectively predicting suicidal behavior. Such a design would permit analyses necessary to be able confidently to evaluate the degree to which passive and active ideation may have a shared or different etiology and the common clinical view that passive ideation is not of high clinical concern.

Several limitations should be mentioned. In spite of the significance of the findings from the current review, there is a near absence of longitudinal studies. As noted above, cross-sectional research provides an important first step for addressing these research questions. Future research, however, should employ longitudinal designs to delineate the temporal nature of observed effects. Such studies are necessary to form the important distinction between concomitants and risk factors, and eventually to elucidate the etiological pathways underlying the risk for passive ideation (Kraemer et al., Reference Kraemer, Kazdin, Offord, Kessler, Jensen and Kupfer1997). Although concomitants are of value for aiding in the identification of who may be currently experiencing passive ideation, risk factors have added importance for potentially enhancing efforts to intervene before the onset of ideation. Work on identifying risk factors is also valuable for its potential to identify targets for the development of future intervention strategies. The possibility that the temporal direction of some of the associations under study may be such that passive ideation is the risk factor, rather than the outcome, should also be considered. For instance, it has been hypothesized that suicidal ideation may lead to prospectively greater rates of interpersonal stress during times of clinical acuity (Liu & Spirito, Reference Liu and Spirito2019). Consistent with this possibility, suicidal ideation has been associated with compromised interpersonal functioning during times of stress (Williams, Barnhofer, Crane, & Beck, Reference Williams, Barnhofer, Crane and Beck2005). This greater interpersonal stress, in turn, may produce feelings of thwarted belongingness in vulnerable individuals.

An additional limitation worth noting is related to the measurement of lifetime suicidal ideation across the studies included in this review. As mentioned above, and like all measurements requiring recall over the lifetime, the assessment of lifetime suicidal ideation may lead to underreporting as a result of memory bias. This type of measurement error may disproportionally affect older individuals, given the greater length of time required for recall. It may also disproportionally affect the recall of less salient content (e.g. passive ideation as compared to active ideation). It is important to consider these potential sources of measurement error when interpreting the current findings. Indeed, these considerations collectively suggest that the prevalence rates of passive ideation reported here may be underestimated and this may especially be the case for older adults.

Additionally, several fundamental characteristics relating to the clinical course of passive ideation (e.g. incidence, chronicity, and recurrence) remain undetermined and await future investigation. Furthermore, individual differences in trajectories of suicidal ideation as a broader construct has been recently observed (Czyz & King, Reference Czyz and King2015; Wolff et al., Reference Wolff, Davis, Liu, Cha, Cheek, Nestor and Spirito2018). For what proportion of individuals does passive ideation specifically abate, or alternatively, transition to active ideation or suicidal behavior? Moreover, understanding what characteristics differentiate between those who do and do not transition to active ideation or behavior, as well as the determinants of the timing of these transitions, has potential value for risk stratification strategies. Suicidal behavior has been previously found most often to occur within a year of onset of suicidal ideation as a general construct (Nock et al., Reference Nock, Borges, Bromet, Alonso, Angermeyer, Beautrais and Williams2008a). Clarifying to what degree this holds true or differs for passive ideation specifically may be clinically informative regarding the potential length of the temporal window for intervening in the course from suicidal ideation to action.

Finally, several challenges in conducting prospective research in this area are notable. The low base rate of suicidal behavior introduces substantial challenges for achieving sufficient statistical power to conduct a meaningful analysis of predictors of suicide. Integrative data analysis may offer one solution to this challenge, by pooling data across multiple studies in order to answer questions about phenomena that require larger sample sizes (Hussong, Curran, & Bauer, Reference Hussong, Curran and Bauer2013). Further contributing to the challenges of research in this area is the length of time over which samples are followed. Given that suicidal behavior is a low-base-rate event, past studies have often utilized long follow-up periods in order to increase the likelihood of capturing these events. Studies of long-term risk are important for identifying who may be at risk for suicidal behaviors, but do not inform our understanding of when individuals are at greatest risk for suicide. Recent research has shifted toward greater emphasis on understanding short-term predictors of suicide risk, utilizing, for example, ecological momentary assessment and passive data collection methods, with the goal of identifying factors predicting the transition from suicidal ideation to behavior (Glenn, Cha, Kleiman, & Nock, Reference Glenn, Cha, Kleiman and Nock2017; Glenn & Nock, Reference Glenn and Nock2014).

In conclusion, the current review found the lifetime prevalence of passive ideation in the general population to be substantial. That this is clinically concerning is indicated by the high psychiatric comorbidity found in our analyses, the strong association with suicide attempts, and preliminary evidence of a comparably strong relation with suicide deaths. One group that emerged as being particularly at risk, and thus a priority for the development of screening and intervention protocols, is sexual minority individuals. The current findings are also suggestive of notable similarities between passive and active ideation, particularly in terms of psychiatric comorbidity and psychological and other characteristics traditionally associated with risk. Collectively, these findings are indicative of the need for greater focus on passive ideation in research and clinical contexts.

Acknowledgements

This work was supported by the National Institutes of Health (RF1MH120830, R01MH101138, R01MH115905, and R21MH112055). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agency.

Conflict of interest

The authors report no conflict of interest.

Footnotes

† The notes appear after the main text.

1 The strength of this association did not differ as a function of whether ‘pure’ passive ideation was assessed or if it was assessed regardless of co-occurring active ideation (p = 0.12).

References

*Alaimo, K., Olson, C. M., & Frongillo, E. A. (2002). Family food insufficiency, but not low family income, is positively associated with dysthymia and suicide symptoms in adolescents. Journal of Nutrition, 132, 719–725. doi:10.1093/jn/132.4.719.CrossRefGoogle Scholar
*Allen, M. H., Abar, B. W., McCormick, M., Barnes, D. H., Haukoos, J., Garmel, G. M., & Boudreaux, E. D. (2013). Screening for suicidal ideation and attempts among emergency department medical patients: Instrument and results from the psychiatric emergency research collaboration. Suicide and Life-Threatening Behavior, 43, 313–323. doi:10.1111/sltb.12018.CrossRefGoogle ScholarPubMed
*Ashrafioun, L., Leong, S. H., Pigeon, W. R., & Oslin, D. W. (2018). The associations between suicidality and mental health factors and pain interference in veterans being referred to primary care mental health integration. Psychiatry Research, 269, 264–270. doi:10.1016/j.psychres.2018.08.073.CrossRefGoogle ScholarPubMed
*Ayalon, L., & Litwin, H. (2009). What cognitive functions are associated with passive suicidal ideation? Findings from a national sample of community dwelling Israelis. International Journal of Geriatric Psychiatry, 24, 472–478. doi:10.1002/gps.2140.CrossRefGoogle Scholar
*Ayalon, L., Mackin, S., Arean, P. A., Chen, H., & McDonel Herr, E. C. (2007). The role of cognitive functioning and distress in suicidal ideation in older adults. Journal of the American Geriatrics Society, 55, 1090–1094. doi:10.1111/j.1532-5415.2007.01237.x.CrossRefGoogle ScholarPubMed
*Baca-Garcia, E., Perez-Rodriguez, M. M., Oquendo, M. A., Keyes, K. M., Hasin, D. S., Grant, B. F., & Blanco, C. (2011). Estimating risk for suicide attempt: Are we asking the right questions? Passive suicidal ideation as a marker for suicidal behavior. Journal of Affective Disorders, 134, 327–332. doi:10.1016/j.jad.2011.06.026.CrossRefGoogle ScholarPubMed
*Barrigón, M. L., Berrouiguet, S., Carballo, J. J., Bonal-Giménez, C., Fernández-Navarro, P., Pfang, B., … Baca-García, E. (2017). User profiles of an electronic mental health tool for ecological momentary assessment: MEmind. International Journal of Methods in Psychiatric Research, 26, 1–9. doi:10.1002/mpr.1554.CrossRefGoogle ScholarPubMed
*Barry, L. C., Wakefield, D. B., Trestman, R. L., & Conwell, Y. (2016). Active and passive suicidal ideation in older prisoners. Crisis, 37, 88–94. doi:10.1027/0227-5910/a000350.CrossRefGoogle ScholarPubMed
*Bartels, S. J., Coakley, E., Oxman, T. E., Constantino, G., Oslin, D., Chen, H., … Sanchez, H. (2002). Suicidal and death ideation in older primary care patients with depression, anxiety, and at-risk alcohol use. American Journal of Geriatric Psychiatry, 10, 417–427. doi:10.2459/JCM.0b013e32830f42c8.CrossRefGoogle ScholarPubMed
Beck, A. T., Brown, G., & Steer, R. A. (1996). Beck depression inventory II manual. San Antonio, TX: The Psychological Corporation.Google Scholar
*Bingham, K. S., Rothschild, A. J., Mulsant, B. H., Whyte, E. M., Meyers, B. S., Banerjee, S., … Flint, A. J. (2017). The association of baseline suicidality with treatment outcome in psychotic depression. Journal of Clinical Psychiatry, 78, 1149–1154. doi:dx.doi.org/10.4088/JCP.14m09658.CrossRefGoogle ScholarPubMed
Biostat. (2014). Comprehensive meta-analysis version 3. Englewood, NJ: Biostat.Google Scholar
*Briggs, R., Tobin, K., Kenny, R. A., & Kennelly, S. P. (2018). What is the prevalence of untreated depression and death ideation in older people? Data from the Irish Longitudinal Study on Aging. International Psychogeriatrics, 30, 1393–1401. doi:10.1017/S104161021700299X.CrossRefGoogle ScholarPubMed
*Caetano, S. C., Olvera, R. L., Hunter, K., Hatch, J. P., Najt, P., Bowden, C., … Soares, J. C. (2006). Association of psychosis with suicidality in pediatric bipolar I, II and bipolar NOS patients. Journal of Affective Disorders, 91, 33–37. doi:10.1016/j.jad.2005.12.008.CrossRefGoogle ScholarPubMed
*Castle, K., Duberstein, P. R., Meldrum, S., Conner, K. R., & Conwell, Y. (2004). Risk factors for suicide in blacks and whites: An analysis of data from the 1993 National Mortality Followback Survey. American Journal of Psychiatry, 161, 452–458. doi:10.1176/appi.ajp.161.3.452.CrossRefGoogle ScholarPubMed
*Charles, D. R., Abram, K. M., Mcclelland, G. M., & Teplin, L. A. (2003). Suicidal ideation and behavior among women in jail. Journal of Contemporary Criminal Justice, 19, 65–81. doi:10.1177/1043986202239742.CrossRefGoogle Scholar
*Coêlho, B. M., Andrade, L. H., Guarniero, F. B., & Wang, Y. P. (2010). The influence of the comorbidity between depression and alcohol use disorder on suicidal behaviors in the São Paulo Epidemiologic Catchment Area Study, Brazil. Revista Brasileira de Psiquiatria, 32, 396–408. doi:10.1590/S1516-44462010005000027.CrossRefGoogle Scholar
*Cohen, C. I., Colemon, Y., Yaffee, R., & Casimir, G. J. (2008). Racial differences in suicidality in an older urban population. The Gerontologist, 48, 71–78. doi:10.1093/geront/48.1.71.CrossRefGoogle Scholar
*Cook, J. M., Pearson, J. L., Thompson, R., Black, B. S., & Rabins, P. V. (2002). Suicidality in older African Americans: Findings from the EPOCH study. American Journal of Geriatric Psychiatry, 10, 437–446. doi:10.1097/00019442-200207000-00010.CrossRefGoogle ScholarPubMed
*Cottler, L. B., Campbell, W., Krishna, V. A. S., Cunningham-Williams, R. M., & Abdallah, A. B. (2005). Predictors of high rates of suicidal ideation among drug users. Journal of Nervous and Mental Disease, 193, 431–437. doi:10.1097/01.nmd.0000168245.56563.90.CrossRefGoogle ScholarPubMed
*Cukrowicz, K. C., Jahn, D. R., Graham, R. D., Poindexter, E. K., & Williams, R. B. (2013). Suicide risk in older adults: Evaluating models of risk and predicting excess zeros in a primary care sample. Journal of Abnormal Psychology, 122, 1021–1030. doi:10.1037/a0034953.CrossRefGoogle Scholar
Czyz, E. K., & King, C. A. (2015). Longitudinal trajectories of suicidal ideation and subsequent suicide attempts among adolescent inpatients. Journal of Clinical Child and Adolescent Psychology, 44, 181–193. doi:10.1080/15374416.2013.836454.CrossRefGoogle ScholarPubMed
*de Graaf, R., Sandfort, T. G. M., & Ten Have, M. (2006). Suicidality and sexual orientation: Differences between men and women in a general population-based sample from The Netherlands. Archives of Sexual Behavior, 35, 253–262. doi:10.1007/s10508-006-9020-z.CrossRefGoogle Scholar
*Dell'Osso, L., Carmassi, C., Carlini, M., Rucci, P., Torri, P., Cesari, D., … Maggi, M. (2009). Sexual dysfunctions and suicidality in patients with bipolar disorder and unipolar depression. Journal of Sexual Medicine, 6, 3063–3070. doi:10.1111/j.1743-6109.2009.01455.x.CrossRefGoogle ScholarPubMed
*Dennis, M., Baillon, S., Brugha, T., Lindesay, J., Stewart, R., & Meltzer, H. (2007). The spectrum of suicidal ideation in Great Britain: Comparisons across a 16-74 years age range. Psychological Medicine, 37, 795–805. doi:10.1017/S0033291707000013.CrossRefGoogle ScholarPubMed
*Deykin, E. Y., & Buka, S. L. (1994). Suicidal ideation and attempts among chemically dependent adolescents. American Journal of Public Health, 84, 634–639.CrossRefGoogle ScholarPubMed
*Draper, B., Maccuspie-Moore, C., & Brodaty, H. (1998). Suicidal ideation and the ‘wish to die’ in dementia patients: The role of depression. Age and Ageing, 27, 503–507. doi:10.1093/ageing/27.4.503.CrossRefGoogle Scholar
*Dutta, R., Ball, H. A., Siribaddana, S. H., Sumathipala, A., Samaraweera, S., McGuffin, P., & Hotopf, M. (2017). Genetic and other risk factors for suicidal ideation and the relationship with depression. Psychological Medicine, 47, 2438–2449. doi:10.1017/S0033291717000940.CrossRefGoogle ScholarPubMed
Duval, S., & Tweedie, R. (2000). Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics, 56, 455–463.CrossRefGoogle ScholarPubMed
Egger, M., Davey Smith, G., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315, 629–634.CrossRefGoogle ScholarPubMed
*Estrada, C. A. M., Nonaka, D., Gregorio, E. R., Leynes, C. R., Del Castillo, R. T., Hernandez, P. M. R., … Kobayashi, J. (2019). Suicidal ideation, suicidal behaviors, and attitudes towards suicide of adolescents enrolled in the Alternative Learning System in Manila, Philippines – a mixed methods study. Tropical Medicine and Health, 47, 1–18. doi:10.1186/s41182-019-0149-6.CrossRefGoogle ScholarPubMed
*Farrell, K. R., & Ganzini, L. (1995). Misdiagnosing delirium as depression in medically ill elderly patients. Archives of Internal Medicine, 155, 2459–2464.CrossRefGoogle ScholarPubMed
*Flint, E. P., Hays, J. C., Krishnan, K. R. R., Meador, K. G., & Blazer, D. G. (1998). Suicidal behaviors in depressed men with a family history of suicide: Effects of psychosocial factors and age. Aging and Mental Health, 2, 286–299. doi:10.1080/13607869856533.CrossRefGoogle Scholar
Franklin, J. C., Ribeiro, J. D., Fox, K. R., Bentley, K. H., Kleiman, E. M., Huang, X., … Nock, M. K. (2017). Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research. Psychological Bulletin, 143, 187–232. doi:10.1037/bul0000084.CrossRefGoogle Scholar
*Fu, I. L., & Wang, Y. P. (2008). Comparison of demographic and clinical characteristics between children and adolescents with major depressive disorder. Revista Brasileira de Psiquiatria, 30, 124–131.CrossRefGoogle Scholar
*Ghanizadeh, A., Khajavian, S., & Ashkani, H. (2006). Prevalence of psychiatric disorders, depression, and suicidal behavior in child and adolescent with thalassemia major. Journal of Pediatric Hematology/Oncology, 28, 781–784.CrossRefGoogle ScholarPubMed
*Glassmire, D. M., Tarescavage, A. M., Burchett, D., Martinez, J., & Gomez, A. (2016). Clinical utility of the MMPI-2-RF SUI items and scale in a forensic inpatient setting: Association with interview self-report and future suicidal behaviors. Psychological Assessment, 28, 1502–1509. doi:10.1037/pas0000220.CrossRefGoogle Scholar
Glenn, C. R., Cha, C. B., Kleiman, E. M., & Nock, M. K. (2017). Understanding suicide risk within the Research Domain Criteria (RDoC) framework: Insights, challenges, and future research considerations. Clinical Psychological Science, 5, 568–592. doi:10.1177/2167702616686854.CrossRefGoogle ScholarPubMed
Glenn, C. R., & Nock, M. K. (2014). Improving the short-term prediction of suicidal behavior. American Journal of Preventive Medicine, 47, S176–S180. doi:10.1016/j.amepre.2014.06.004.CrossRefGoogle ScholarPubMed
Goldney, R. D., Smith, S., Winefield, A. H., Tiggeman, M., & Winefield, H. R. (1991). Suicidal ideation: Its enduring nature and associated morbidity. Acta Psychiatrica Scandinavica, 83, 115–120. doi:10.1111/j.1600-0447.1991.tb07375.x.CrossRefGoogle ScholarPubMed
*Guidry, E. T., & Cukrowicz, K. C. (2016). Death ideation in older adults: Psychological symptoms of depression, thwarted belongingness, and perceived burdensomeness. Aging and Mental Health, 20, 823–830. doi:10.1080/13607863.2015.1040721.CrossRefGoogle ScholarPubMed
Haas, A. P., Eliason, M., Mays, V. M., Mathy, R. M., Cochran, S. D., D'Augelli, A. R., … Clayton, P. J. (2011). Suicide and suicide risk in lesbian, gay, bisexual, and transgender populations: Review and recommendations. Journal of Homosexuality, 58, 10–51. doi:10.1080/00918369.2011.534038.CrossRefGoogle ScholarPubMed
Hamilton, J. L., Hamlat, E. J., Stange, J. P., Abramson, L. Y., & Alloy, L. B. (2014). Pubertal timing and vulnerabilities to depression in early adolescence: Differential pathways to depressive symptoms by sex. Journal of Adolescence, 37, 165–174. doi:10.1016/j.adolescence.2013.11.010.CrossRefGoogle Scholar
Hamlat, E. J., Snyder, H. R., Young, J. F., & Hankin, B. L. (2019). Pubertal timing as a transdiagnostic risk for psychopathology in youth. Clinical Psychological Science, 7, 411–429. doi:10.1177/2167702618810518.CrossRefGoogle ScholarPubMed
*Handley, E. D., Adams, T. R., Manly, J. T., Cicchetti, D., & Toth, S. L. (2019). Mother–daughter interpersonal processes underlying the association between child maltreatment and adolescent suicide ideation. Suicide and Life-Threatening Behavior, 49, 1232–1240. doi:10.1111/sltb.12522.CrossRefGoogle ScholarPubMed
*Heisel, M. J., & Flett, G. L. (2006). The development and initial validation of the Geriatric Suicide Ideation Scale. American Journal of Geriatric Psychiatry, 14, 742–751. doi:10.1097/01.JGP.0000218699.27899.f9.CrossRefGoogle ScholarPubMed
*Heisel, M. J., & Flett, G. L. (2016). Investigating the psychometric properties of the Geriatric Suicide Ideation Scale (GSIS) among community-residing older adults. Aging and Mental Health, 20, 208–221. doi:10.1080/13607863.2015.1072798.CrossRefGoogle ScholarPubMed
*Herrell, R., Goldberg, J., True, W. R., Ramakrishnan, V., Lyons, M., Eisen, S., & Tsuang, M. T. (1999). Sexual orientation and suicidality: A co-twin control study in adult men. Archives of General Psychiatry, 56, 867. doi:10.1001/archpsyc.56.10.867.CrossRefGoogle ScholarPubMed
Higgins, J. P. T., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003). Measuring inconsistency in meta-analyses. BMJ, 327, 557–560.CrossRefGoogle ScholarPubMed
Hottes, T. S., Bogaert, L., Rhodes, A. E., Brennan, D. J., & Gesink, D. (2016). Lifetime prevalence of suicide attempts among sexual minority adults by study sampling strategies: A systematic review and meta-analysis. American Journal of Public Health, 106, 921. doi:10.2105/AJPH.2016.303088a.CrossRefGoogle ScholarPubMed
Husky, M. M., Olfson, M., He, J., Nock, M. K., Swanson, S. A., & Merikangas, K. R. (2012). Twelve-month suicidal symptoms and use of services among adolescents: Results from the National Comorbidity Survey. Psychiatric Services, 63, 989–996. doi:10.1176/appi.ps.201200058.CrossRefGoogle ScholarPubMed
Hussong, A. M., Curran, P. J., & Bauer, D. J. (2013). Integrative data analysis in clinical psychology research. Annual Review of Clinical Psychology, 9, 61–89. doi:10.1146/annurev-clinpsy-050212-185522.CrossRefGoogle ScholarPubMed
Jacobs, D. (2009). Suicide Assessment Five-Step Evaluation and Triage (SAFE-T). Retrieved from https://www.integration.samhsa.gov/images/res/SAFE_T.pdf.Google Scholar
*Jahn, D. R., Poindexter, E. K., Graham, R. D., & Cukrowicz, K. C. (2012). The moderating effect of the negative impact of recent life events on the relation between intrinsic religiosity and death ideation in older adults. Suicide and Life-Threatening Behavior, 42, 589–601. doi:10.1111/j.1943-278X.2012.00114.x.CrossRefGoogle ScholarPubMed
Kessler, R. C., Berglund, P., Borges, G., Nock, M., & Wang, P. S. (2005). Trends in suicide ideation, plans, gestures, and attempts in the United States, 1990-1992 to 2001-2003. JAMA, 293, 2487–2495. doi:10.1001/jama.293.20.2487.CrossRefGoogle ScholarPubMed
Kessler, R. C., Borges, G., & Walters, E. E. (1999). Prevalence of and risk factors for lifetime suicide attempts in the national comorbidity survey. Archives of General Psychiatry, 56, 617–626. doi:10.1001/archpsyc.56.7.617.CrossRefGoogle ScholarPubMed
Kessler, R. C., & Üstün, T. B. (2004). The World Mental Health (WMH) Survey Initiative Version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI). International Journal of Methods in Psychiatric Research, 13, 93–121. doi:10.1002/mpr.168.CrossRefGoogle Scholar
*Kienhorst, C. W. M., de Wilde, E. J., van den Bout, J., Broese van Groenou, M. I., Diekstra, R. F. W., & Wolters, W. H. G. (1990). Self-reported suicidal behavior in Dutch secondary education students. Suicide and Life-Threatening Behavior, 20, 101–112. doi:10.1111/j.1943-278X.1990.tb00093.x.Google ScholarPubMed
*Kim, Y. A., Bogner, H. R., Brown, G. K., & Gallo, J. J. (2006). Chronic medical conditions and wishes to die among older primary care patients. International Journal of Psychiatry in Medicine, 36, 183–198. doi:10.2190/3QXD-UR0H-K8FH-2CU8.CrossRefGoogle ScholarPubMed
*Kliem, S., Lohmann, A., Mosle, T., & Brahler, E. (2017). German Beck Scale for Suicide Ideation (BSS): Psychometric properties from a representative population survey. BMC Psychiatry, 17, 389. doi:10.1186/s12888-017-1559-9.CrossRefGoogle ScholarPubMed
Kovacs, M. (2010). Children's depression inventory – second edition (CDI-2) manual. North Tonawanda, NY: Multi-Health Systems Publishing.Google Scholar
Kraemer, H. C., Kazdin, A. E., Offord, D. R., Kessler, R. C., Jensen, P. S., & Kupfer, D. J. (1997). Coming to terms with the terms of risk. Archives of General Psychiatry, 54, 337–343.CrossRefGoogle Scholar
Kraemer, H. C., Morgan, G. A., Leech, N. L., Gliner, J. A., Vaske, J. J., & Harmon, R. J. (2003). Measures of clinical significance. Journal of the American Academy of Child and Adolescent Psychiatry, 42, 1524–1529. doi:10.1097/00004583-200312000-00022.CrossRefGoogle ScholarPubMed
*Lapierre, S., Boyer, R., Desjardins, S., Dubé, M., Lorrain, D., Préville, M., & Brassard, J. (2012). Daily hassles, physical illness, and sleep problems in older adults with wishes to die. International Psychogeriatrics, 24, 243–252. doi:10.1017/S1041610211001591.CrossRefGoogle ScholarPubMed
*Linden, M., & Barnow, S. (1997). The wish to die in very old persons near the end of life: A psychiatric problem? Results from the Berlin aging study. International Psychogeriatrics, 9, 291–307. doi:10.1017/S1041610297004456.CrossRefGoogle ScholarPubMed
Liu, R. T. (2016). Taxometric evidence of a dimensional latent structure for depression in an epidemiological sample of children and adolescents. Psychological Medicine, 46, 1265–1275. doi:10.1017/S0033291715002792.CrossRefGoogle Scholar
Liu, R. T., & Spirito, A. (2019). Suicidal behavior and stress generation in adolescents. Clinical Psychological Science, 7, 488–501. doi:10.1177/2167702618810227.CrossRefGoogle ScholarPubMed
*Liu, X., Gentzler, A. L., Tepper, P., Kiss, E., Kothencné, V. O., Tamás, Z., … Kovacs, M. (2006). Clinical features of depressed children and adolescents with various forms of suicidality. Journal of Clinical Psychiatry, 67, 1442–1450.CrossRefGoogle ScholarPubMed
Lozano, R., Naghavi, M., Foreman, K., Lim, S., Shibuya, K., Aboyans, V., … Murray, C. J. L. (2012). Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet, 380, 2095–2128. doi:10.1016/S0140-6736(12)61728-0.CrossRefGoogle ScholarPubMed
*Lutz, J., Morton, K., Turiano, N. A., & Fiske, A. (2016). Health conditions and passive suicidal ideation in the survey of health, ageing, and retirement in Europe. Journals of Gerontology – Series B Psychological Sciences and Social Sciences, 71, 936–946. doi:10.1093/geronb/gbw019.CrossRefGoogle ScholarPubMed
*Lytle, M. C., De Luca, S. M., Blosnich, J. R., & Brownson, C. (2015). Associations of racial/ethnic identities and religious affiliation with suicidal ideation among lesbian, gay, bisexual, and questioning individuals. Journal of Affective Disorders, 178, 39–45. doi:10.1016/j.jad.2014.07.039.CrossRefGoogle ScholarPubMed
*Magni, G., Rigatti-Luchini, S., Fracca, F., & Merskey, H. (1998). Suicidality in chronic abdominal pain: An analysis of the Hispanic Health and Nutrition Examination Survey (HHANES). Pain, 76, 137–144. doi:10.1016/S0304-3959(98)00035-9.CrossRefGoogle Scholar
*Malfent, D., Wondrak, T., Kapusta, N. D., & Sonneck, G. (2010). Suicidal ideation and its correlates among elderly in residential care homes. International Journal of Geriatric Psychiatry, 25, 843–849. doi:10.1002/gps.2426.CrossRefGoogle ScholarPubMed
*Manu, P., Matthews, D. A., & Lane, T. J. (1991). Panic disorder among patients with chronic fatigue. Southern Medical Journal, 84, 451–456.CrossRefGoogle ScholarPubMed
*May, C. N., Overholser, J. C., Ridley, J., & Raymond, D. (2015). Passive suicidal ideation: A clinically relevant risk factor for suicide in treatment-seeking veterans. Illness Crisis and Loss, 23, 261–277. doi:10.1177/1054137315585422.CrossRefGoogle Scholar
*McBride, O., Cheng, H. G., Slade, T., & Lynskey, M. T. (2016). The role of specific alcohol-related problems in predicting depressive experiences in a cross-sectional National Household Survey. Alcohol and Alcoholism, 51, 655–663. doi:10.1093/alcalc/agw010.CrossRefGoogle Scholar
*Meerwijk, E. L., & Weiss, S. J. (2018). Tolerance for psychological pain and capability for suicide: Contributions to suicidal ideation and behavior. Psychiatry Research, 262, 203–208. doi:10.1016/j.psychres.2018.02.005.CrossRefGoogle ScholarPubMed
*Mendonca, J. D., & Holden, R. R. (1996). Are all suicidal ideas closely linked to hopelessness? Acta Psychiatrica Scandinavica, 93, 246–251. doi:10.1111/j.1600-0447.1996.tb10642.x.CrossRefGoogle ScholarPubMed
*Mitchell, S. M., Brown, S. L., Roush, J. F., Bolaños, A. D., Littlefield, A. K., Marshall, A. J., … Cukrowicz, K. C. (2017). The clinical application of suicide risk assessment: A theory-driven approach. Clinical Psychology & Psychotherapy, 24, 1406–1420. doi:10.1002/cpp.2086.CrossRefGoogle ScholarPubMed
*Mitchell, S. M., Brown, S. L., Roush, J. F., Tucker, R. P., Cukrowicz, K. C., & Joiner, T. E. (in press). The Interpersonal Needs Questionnaire: Statistical considerations for improved clinical application. Assessment. doi:10.1177/1073191118824660Google Scholar
Moffitt, T. E., Caspi, A., Taylor, A., Kokaua, J., Milne, B. J., Polanczyk, G., & Poulton, R. (2010). How common are common mental disorders? Evidence that lifetime prevalence rates are doubled by prospective versus retrospective ascertainment. Psychological Medicine, 40, 899–909. doi:10.1017/S0033291709991036.CrossRefGoogle ScholarPubMed
*Naidoo, S., & Collings, S. J. (2017). Suicidal and death ideation in a cohort of psychiatric outpatients: Prevalence and risk factors. Psychology and Developing Societies, 29, 288–300. doi:10.1177/0971333617716849.CrossRefGoogle Scholar
National Action Alliance for Suicide Prevention. (2014). A prioritized research agenda for suicide prevention: An action plan to save lives. Rockville, MD: National Institute of Mental Health and the Research Prioritization Task Force.Google Scholar
*Nazem, S., Siderowf, A. D., Duda, J. E., Brown, G. K., Have, T. T., Stern, M. B., & Weintraub, D. (2008). Suicidal and death ideation in Parkinson's disease. Movement Disorders, 23, 1573–1579. doi:10.1002/mds.22130.CrossRefGoogle ScholarPubMed
*Neeleman, J., de Graaf, R., & Vollebergh, W. (2004). The suicidal process; prospective comparison between early and later stages. Journal of Affective Disorders, 82, 43–52. doi:10.1016/j.jad.2003.09.005.CrossRefGoogle ScholarPubMed
Nock, M. K., Borges, G., Bromet, E. J., Alonso, J., Angermeyer, M., Beautrais, A., … Williams, D. (2008a). Cross-national prevalence and risk factors for suicidal ideation, plans and attempts. British Journal of Psychiatry, 192, 98–105. doi:10.1192/bjp.bp.107.040113.CrossRefGoogle Scholar
Nock, M. K., Borges, G., Bromet, E. J., Cha, C. B., Kessler, R. C., & Lee, S. (2008b). Suicide and suicidal behavior. Epidemiologic Reviews, 30, 133–154. doi:10.1093/epirev/mxn002.CrossRefGoogle Scholar
Nock, M. K., Green, J. G., Hwang, I., McLaughlin, K. A., Sampson, N. A., Zaslavsky, A. M., & Kessler, R. C. (2013). Prevalence, correlates, and treatment of lifetime suicidal behavior among adolescents: Results from the National Comorbidity Survey Replication Adolescent Supplement. JAMA Psychiatry, 70, 300–310. doi:10.1001/2013.jamapsychiatry.55.CrossRefGoogle ScholarPubMed
*Nrugham, L., Larsson, B., & Sund, A. M. (2008). Specific depressive symptoms and disorders as associates and predictors of suicidal acts across adolescence. Journal of Affective Disorders, 111, 83–93. doi:10.1016/j.jad.2008.02.010.CrossRefGoogle ScholarPubMed
O'Brien, K. H. M., Liu, R. T., Putney, J., Burke, T. A., & Aguinaldo, L. (2017). Suicide and non-suicidal self-injury. In Smalley, K. B., Warren, J. C., & Barefoot, K. N. (Eds.), LGBT health: Meeting the health needs of gender and sexual minorities (pp. 181–198). New York: Springer.Google Scholar
*O'Riley, A. A., Van Orden, K. A., He, H., Richardson, T. M., Podgorski, C., & Conwell, Y. (2014). Suicide and death ideation in older adults obtaining aging services. American Journal of Geriatric Psychiatry, 22, 614–622. doi:10.1016/j.jagp.2012.12.004.CrossRefGoogle ScholarPubMed
Osman, A., Bagge, C. L., Gutierrez, P. M., Konick, L. C., Kopper, B. A., & Barrios, F. X. (2001). The Suicidal Behaviors Questionnaire-Revised (SBQ-R): Validation with clinical and nonclinical samples. Assessment, 8, 443–454. doi:10.1177/107319110100800409.CrossRefGoogle ScholarPubMed
*Pawluk, L. K., Hurwitz, T. D., Schluter, J. L., Ullevig, C., & Mahowald, M. W. (1995). Psychiatric morbidity in narcoleptics on chronic high dose methylphenidate therapy. Journal of Nervous and Mental Disease, 183, 45–48. doi:10.1097/00005053-199501000-00009.CrossRefGoogle ScholarPubMed
*Rabkin, J. G., Remien, R., Katoff, L., & Williams, J. B. W. (1993). Suicidality in AIDS long-term survivors: What is the evidence? AIDS Care, 5, 401–411. doi:10.1080/09540129308258010.CrossRefGoogle ScholarPubMed
*Raue, P. J., Meyers, B. S., Rowe, J. L., Heo, M., & Bruce, M. L. (2007). Suicidal ideation among elderly homecare patients. International Journal of Geriatric Psychiatry, 22, 32–37. doi:10.1002/gps.1649.CrossRefGoogle ScholarPubMed
Reardon, L. E., Leen-Feldner, E. W., & Hayward, C. (2009). A critical review of the empirical literature on the relation between anxiety and puberty. Clinical Psychology Review, 29, 1–23. doi:10.1016/j.cpr.2008.09.005.CrossRefGoogle ScholarPubMed
*Robertson, K., Parsons, T. D., Van Der Horst, C., & Hall, C. (2006). Thoughts of death and suicidal ideation in nonpsychiatric human immunodeficiency virus seropositive individuals. Death Studies, 30, 455–469. doi:10.1080/07481180600614435.CrossRefGoogle ScholarPubMed
*Romans, S. E., Tyas, J., Cohen, M. M., & Silverstone, T. (2007). Gender differences in the symptoms of major depressive disorder. Journal of Nervous and Mental Disease, 195, 905–911. doi:10.1097/NMD.0b013e3181594cb7.CrossRefGoogle ScholarPubMed
*Roth, K. B., Borges, G., Medina-Mora, M. E., Orozco, R., Ouéda, C., & Wilcox, H. C. (2011). Depressed mood and antisocial behavior problems as correlates for suicide-related behaviors in Mexico. Journal of Psychiatric Research, 45, 596–602. doi:10.1016/j.jpsychires.2010.10.009.CrossRefGoogle ScholarPubMed
*Rufino, K. A., Viswanath, H., Wagner, R., & Patriquin, M. A. (2018). Body dissatisfaction and suicidal ideation among psychiatric inpatients with eating disorders. Comprehensive Psychiatry, 84, 22–25. doi:10.1016/j.comppsych.2018.03.013.CrossRefGoogle ScholarPubMed
*Saïas, T., Beck, F., Bodard, J., Guignard, R., & du Roscoät, E. (2012). Social participation, social environment and death ideations in later life. PLoS ONE, 7, 1–8. doi:10.1371/journal.pone.0046723.CrossRefGoogle ScholarPubMed
*Schimanski, I. D., Mouat, K. L., Billinghurst, B. L., & Linscott, R. J. (2017). Preliminary evidence that schizophrenia liability at age 15 predicts suicidal ideation two years later. Schizophrenia Research, 181, 60–62. doi:10.1016/j.schres.2016.08.030.CrossRefGoogle ScholarPubMed
*Scocco, P., Meneghel, G., Dello Buono, M., & De Leo, D. (2001). Hostility as a feature of elderly suicidal ideators. Psychological Reports, 88, 863–868.CrossRefGoogle ScholarPubMed
*Segal, D. L., Gottschling, J., Marty, M., Meyer, W. J., & Coolidge, F. L. (2015). Relationships among depressive, passive-aggressive, sadistic and self-defeating personality disorder features with suicidal ideation and reasons for living among older adults. Aging and Mental Health, 19, 1071–1077. doi:10.1080/13607863.2014.1003280.CrossRefGoogle ScholarPubMed
Simon, R. I. (2014). Passive suicidal ideation: Still a high-risk clinical scenario. Current Psychiatry, 13, 13–15.Google Scholar
*Smith, M. T., Edwards, R. R., Robinson, R. C., & Dworkin, R. H. (2004). Suicidal ideation, plans, and attempts in chronic pain patients: Factors associated with increased risk. Pain, 111, 201–208. doi:10.1016/j.pain.2004.06.016.CrossRefGoogle ScholarPubMed
*Smith, P. N., Gamble, S. A., Cort, N. A., Ward, E. A., Conwell, Y., & Talbot, N. L. (2012). The relationships of attachment style and social maladjustment to death ideation in depressed women with a history of childhood sexual abuse. Journal of Clinical Psychology, 68, 78–87. doi:10.1002/jclp.20852.CrossRefGoogle ScholarPubMed
*Snyder, M., Gertler, R., & Ferneau, E. (1973). Changes in nursing students’ attitudes toward death and dying: A measurement of curriculum integration effectiveness. International Journal of Social Psychiatry, 19, 294–298.CrossRefGoogle Scholar
*Storch, E. A., Kay, B., Wu, M. S., Nadeau, J. M., & Riemann, B. (2017). Suicidal and death ideation among adults with obsessive-compulsive disorder presenting for intensive intervention. Annals of Clinical Psychiatry, 29, 46–53.Google ScholarPubMed
*Tal, I., Mauro, C., Reynolds, C. F., Shear, M. K., Simon, N., Lebowitz, B., … Zisook, S. (2017). Complicated grief after suicide bereavement and other causes of death. Death Studies, 41, 267–275. doi:10.1080/07481187.2016.1265028.CrossRefGoogle ScholarPubMed
*Tateno, M., Jovanović, N., Beezhold, J., Uehara-Aoyama, K., Umene-Nakano, W., Nakamae, T., … Kato, T. A. (2018). Suicidal ideation and burnout among psychiatric trainees in Japan. Early Intervention in Psychiatry, 12, 935–937. doi:10.1111/eip.12466.CrossRefGoogle Scholar
Ullsperger, J. M., & Nikolas, M. A. (2017). A meta-analytic review of the association between pubertal timing and psychopathology in adolescence: Are there sex differences in risk? Psychological Bulletin, 143, 903–938. doi:10.1037/bul0000106.CrossRefGoogle ScholarPubMed
*Undheim, A. M. (2013). Involvement in bullying as predictor of suicidal ideation among 12- to 15-year-old Norwegian adolescents. European Child & Adolescent Psychiatry, 22, 357–365. doi:10.1007/s00787-012-0373-7.CrossRefGoogle ScholarPubMed
*Urrila, A. S., Karlsson, L., Kiviruusu, O., Pelkonen, M., Strandholm, T., & Marttunen, M. (2012). Sleep complaints among adolescent outpatients with major depressive disorder. Sleep Medicine, 13, 816–823. doi:10.1016/j.sleep.2012.04.012.CrossRefGoogle ScholarPubMed
*van Duijn, E., Vrijmoeth, E. M., Giltay, E. J., & Bernhard Landwehrmeyer, G. (2018). Suicidal ideation and suicidal behavior according to the C-SSRS in a European cohort of Huntington's disease gene expansion carriers. Journal of Affective Disorders, 228, 194–204. doi:10.1016/j.jad.2017.11.074.CrossRefGoogle Scholar
*Van Orden, K. A., O'Riley, A. A., Simning, A., Podgorski, C., Richardson, T. M., & Conwell, Y. (2015). Passive suicide ideation: An indicator of risk among older adults seeking aging services? The Gerontologist, 55, 972–980. doi:10.1093/geront/gnu026.CrossRefGoogle ScholarPubMed
*Van Orden, K. A., Simning, A., Conwell, Y., Skoog, I., & Waern, M. (2013). Characteristics and comorbid symptoms of older adults reporting death ideation. American Journal of Geriatric Psychiatry, 21, 803–810. doi:10.1016/j.jagp.2013.01.015.CrossRefGoogle ScholarPubMed
*Vera, M., Reyes-Rabanillo, M. L., Huertas, S., Juarbe, D., Perez-Pedrogo, C., Huertas, A., & Pena, M. (2011). Suicide ideation, plans, and attempts among general practice patients with chronic health conditions in Puerto Rico. International Journal of General Medicine, 4, 197–205. doi:10.2147/IJGM.S17156.CrossRefGoogle ScholarPubMed
*Villa, J., Choi, J., Kangas, J. L., Kaufmann, C. N., Harvey, P. D., & Depp, C. A. (2018). Associations of suicidality with cognitive ability and cognitive insight in outpatients with Schizophrenia. Schizophrenia Research, 192, 340–344. doi:10.1016/j.schres.2017.06.013.CrossRefGoogle ScholarPubMed
*Walker, J., Hansen, C. H., Butcher, I., Sharma, N., Wall, L., Murray, G., & Sharpe, M. (2011). Thoughts of death and suicide reported by cancer patients who endorsed the ‘suicidal thoughts’ item of the PHQ-9 during routine screening for depression. Psychosomatics, 52, 424–427. doi:10.1016/j.psym.2011.02.003.CrossRefGoogle Scholar
WHO World Mental Health Survey Consortium. (2004). Prevalence, severity, and unmet need for treatment of mental disorders in the World Health Organization World Mental Health Surveys. Journal of American Medical Association, 291, 2581–2590. doi:10.1001/jama.291.21.2581.CrossRefGoogle Scholar
Williams, J. M. G., Barnhofer, T., Crane, C., & Beck, A. T. (2005). Problem solving deteriorates following mood challenge in formerly depressed patients with a history of suicidal ideation. Journal of Abnormal Psychology, 114, 421–431. doi:10.1037/0021-843X.114.3.421.CrossRefGoogle ScholarPubMed
Wolff, J. C., Davis, S., Liu, R. T., Cha, C. B., Cheek, S. M., Nestor, B. A., … Spirito, A. (2018). Trajectories of suicidal ideation among adolescents following psychiatric hospitalization. Journal of Abnormal Child Psychology, 46, 355–363. doi:10.1007/s10802-017-0293-6.CrossRefGoogle ScholarPubMed
World Health Organization. (2014). Preventing suicide: A global imperative. Geneva: World Health Organization.Google Scholar
*Yoder, K. A., Whitbeck, L. B., & Hoyt, D. R. (2008). Dimensionality of thoughts of death and suicide: Evidence from a study of homeless adolescents. Social Indicators Research, 86, 83–100. doi:10.1007/s11205-007-9095-5.CrossRefGoogle Scholar
Zhou, E. S., Hu, J. C., Kantoff, P. W., & Recklitis, C. J. (2015). Identifying suicidal symptoms in prostate cancer survivors using brief self-report. Journal of Cancer Survivorship, 9, 59–67. doi:10.1007/s11764-014-0385-z.CrossRefGoogle ScholarPubMed
Figure 0

Fig. 1. PRISMA flow chart of literature search.

Figure 1

Table 1. Study characteristics

Figure 2

Table 2. Prevalence rates for passive suicidal ideation by sample type

Figure 3

Table 3. Moderator analyses for the prevalence of passive suicidal ideation

Figure 4

Fig. 2. Funnel plots for effect sizes in the meta-analyses. The vertical line indicates the weighted mean effect. Open circles indicate observed effects for actual studies, and closed circles indicate imputed effects for studies believed to be missing due to publication bias. The clear diamond reflects the unadjusted weighted mean effect size, whereas the black diamond reflects the weighted mean effect size after adjusting for publication bias. (a) Lifetime prevalence of passive ideation in epidemiological samples. (b) Lifetime prevalence of passive ideation in community samples. (c) Lifetime prevalence of passive ideation in at-risk/mixed/medical samples. (d) Lifetime prevalence of passive ideation in psychiatric samples.

Figure 5

Table 4. Correlates of passive and active ideation

Figure 6

Table 5. Prediction of suicide attempts and deaths by passive and active ideation