Hostname: page-component-8448b6f56d-sxzjt Total loading time: 0 Render date: 2024-04-22T16:20:34.262Z Has data issue: false hasContentIssue false

Low-risk and high-risk groups matter in suicide risk

Published online by Cambridge University Press:  21 November 2018

Chris Gale*
Department of Psychological Medicine, Dunedin School of Medicine, University of Otago, Dunedin New Zealand
Paul Glue
Department of Psychological Medicine, Dunedin School of Medicine, University of Otago, Dunedin New Zealand
Yoram Barak
Department of Psychological Medicine, Dunedin School of Medicine, University of Otago, Dunedin New Zealand
Author for correspondence: Chris Gale, E-mail:
Rights & Permissions [Opens in a new window]


Copyright © Cambridge University Press 2018 

Dear Sir,

We read with interest the article by Large (Large et al., Reference Large, Myles, Myles, Corderoy, Weiser, Davidson and Ryan2018) who reported on suicide risk assessment from a heterogeneous selection of papers, either cohort or case control (Q-value = 143, df (Q) = 17, p value < 0.001, I 2 = 88.1%). Based on their analyses, they questioned the utility of risk assessment, as has another recent narrative review (Carter and Spittal, Reference Carter and Spittal2018). However, we have found many services mandate risk assessment tools. This led us to search for risk assessment measures that published sensitivity, specificity and prevalence rates for completed suicide, which allowed us to estimate Bayesian post-test predictive values (PPV), and to perform a meta-analysis on the pooled sensitivity and specificity for these suicide risk assessment scales in relation to completed suicide.

We searched Medline, PubMed and Embase for papers that reported psychometric properties of suicide risk assessment. We extracted, directly from the paper, or indirectly from the published sensitivity, specificity and mortality data, the true positive, false negative, false positive and true negative rates. We used these to estimate the pooled sensitivity, specificity using a fixed effects model. We estimated heterogeneity using Cochrane's Q (Cochran, Reference Cochran1950) and I 2 (Hedges and Pigott, Reference Hedges and Pigott2001). A Standardized Receiver Operation Curve (SROC) was created, along with 95% confidence intervals for sensitivity and specificity using a bivariate analysis. We also estimated the PPV, from Bayes’ formula.

Post-hoc we divided the papers into low- and high-risk populations from the clinical descriptions of the populations, and the published suicide rates. The high-risk groups all had suicide death rates over one percent. This analysis was performed using the mada (Doebler, Reference Doebler2015) package in R version 3.4 (R Development Core Team, 2017).

Our search identified 495 papers, of which 109 remained after screening abstracts, but only seven papers met our criteria. Of the four high-prevalence papers, one reported on persons who had made a suicide attempt within 1 month (baseline suicide rate 8%) (Stefansson et al., Reference Stefansson, Nordstrom and Jokinen2012). A second paper reported on those admitted after a suicide attempt to a suicide research institute where the baseline suicide rate was 4% (Nimeus et al., Reference Nimeus, Alsen and Traskman-Bendz2000). A third paper was in people with schizophrenia with active suicidal ideation or command hallucinations to suicide (baseline suicide rate 1.3%) (Ayer et al., Reference Ayer, Jayathilake and Meltzer2008). A fourth paper combined a smaller pilot in a high-risk group with two cohorts that presented to UK hospitals (Pallis et al., Reference Pallis, Gibbons and Pierce1984). The suicide rate in the larger of these samples was 1.5%. The other three low prevalence papers were larger. Two were multicenter studies assessing people in emergency department after self-harm (Steeg et al., Reference Steeg, Kapur, Webb, Applegate, Stewart, Hawton, Bergen, Waters and Cooper2012; Cooper et al., Reference Cooper, Kapur, Dunning, Guthrie, Appleby and Mackway-Jones2006), with suicide rates of 0.08% and 0.06% and the other was a 20 year series from the Beck Institute (Beck et al., Reference Beck, Brown, Steer, Dahlsgaard and Grisham1999), which reported a suicide rate of 0.02%.

The PPV ranged from 0.3 to 24%. The three datasets with low prevalence had PPV from 0.3 to 1.8%: in the high-prevalence groups the range was 13–24%. Similarly, the Numbers needed to Intervene (NNI) ranged from 4.2 to 325: the low prevalence groups had an NNI between 28 and 325 and the high-prevalence group had an NNI between four and seven.

The pooled data showed remarkably low heterogeneity (Cochrane's Q = 12, I 2 = 0). The pooled sensitivity was 0.74 (0.63–0.83), specificity 0.54 (0.35–0.72). The pooled diagnostic odds ratio was 3.9 (1.7–9.4). The sensitivity analysis showed a significant difference using a bivariate likelihood ratio test (χ2 = 7, 2 df, p = 0.02). The pooled sensitivity in the high-risk group was 0.89 (0.53–0.98) while in the low-risk group was 0.72 (0.54–0.89). The specificity also differed between high-risk 0.80 (0.48–0.96) and low-risk groups 0.30 (0.08–0.69). The area under the curve was estimated at 0.90 in the high-risk group, and 0.69 in the low-risk group (see Fig. 1).

Fig. 1. SROC for low and high-risk populations, showing in the low-risk group the 95% CIs.

As in all meta-analyses, there are difficulties in obtaining all data: this review was limited to the English language, we were unable to double code all papers, and the number of data points is few. It is of note that our search does not include any papers within Large's analysis, and this may be a result of our requirement that a published scale was used.

The Bayesian analysis indicates that there is a limited clinical utility to using rating scales for predicting suicide death risk. Our conclusion is that in the high-risk group, risk stratification may have some utility, but in general clinical work, or in public health psychiatry, the base rate of suicide is such that risk assessment may indeed be risky (Mulder et al., Reference Mulder, Newton-Howes and Coid2016).

We suggest that this small analysis is further evidence of the lack of utility of risk assessment scales for suicide: the use in high-risk groups is questionable, and it is even more questionable within general psychiatric services. It may be better to focus on developing therapeutic relationships with patients presenting with suicidal thoughts/acts (Gale and Glue, Reference Gale and Glue2018), and designing safety plans and follow-up (Stanley et al., Reference Stanley, Brown, Brenner, Galfalvy, Currier, Knox, Chaudhury, Bush and Green2018), than to continue to concentrate on estimating the risk of completed suicide.

Author ORCIDs

Chris Gale 0000-0001-8032-765X, Paul Glue 0000-0002-7305-2800, Yoram Barak 0000-0002-8271-4255


Ayer, DW, Jayathilake, K and Meltzer, HY (2008) The InterSePT suicide scale for prediction of imminent suicidal behaviors. Psychiatry Research 161, 8796.Google Scholar
Beck, AT, Brown, GK, Steer, RA, Dahlsgaard, KK and Grisham, JR (1999) Suicide ideation at its worst point: a predictor of eventual suicide in psychiatric outpatients. Suicide & Life-Threatening Behavior 29, 19.Google Scholar
Carter, G and Spittal, MJ (2018) Suicide risk assessment. Crisis 39, 229234.Google Scholar
Cochran, WG (1950) The comparison of percentages in matched samples. Biometrika 37, 256266.Google Scholar
Cooper, J, Kapur, N, Dunning, J, Guthrie, E, Appleby, L and Mackway-Jones, K (2006) A clinical tool for assessing risk after self-harm. Annals of Emergency Medicine 48, 459466.Google Scholar
Doebler, P (2015) mada: Meta-Analysis of Diagnostic Accuracy. Available online at (accessed 14 November 2018).Google Scholar
Gale, C and Glue, P (2018) How comprehensive is suicide risk assessment in the emergency department? The New Zealand Medical Journal (Online) 131, 1113.Google Scholar
Hedges, LV and Pigott, TD (2001) The power of statistical tests in meta-analysis. Psychological Methods 6, 203.Google Scholar
Large, M, Myles, N, Myles, H, Corderoy, A, Weiser, M, Davidson, M and Ryan, CJ (2018) Suicide risk assessment among psychiatric inpatients: a systematic review and meta-analysis of high-risk categories. Psychological Medicine 48, 11191127.Google Scholar
Mulder, R, Newton-Howes, G and Coid, JW (2016) The futility of risk prediction in psychiatry. British Journal of Psychiatry 209, 271272.Google Scholar
Nimeus, A, Alsen, M and Traskman-Bendz, L (2000) The suicide assessment scale: an instrument assessing suicide risk of suicide attempters. European Psychiatry: the Journal of the Association of European Psychiatrists 15, 416423.Google Scholar
Pallis, DJ, Gibbons, JS and Pierce, DW (1984) Estimating suicide risk among attempted suicides: II. Efficiency of predictive scales after the attempt. British Journal of Psychiatry 144, 139148.Google Scholar
R Development Core Team (2017) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Stanley, B, Brown, GK, Brenner, LA, Galfalvy, HC, Currier, GW, Knox, KL, Chaudhury, SR, Bush, AL and Green, KL (2018) Comparison of the safety planning intervention with follow-up vs usual care of suicidal patients treated in the emergency department. JAMA Psychiatry 75, 894900.Google Scholar
Steeg, S, Kapur, N, Webb, R, Applegate, E, Stewart, SL, Hawton, K, Bergen, H, Waters, K and Cooper, J (2012) The development of a population-level clinical screening tool for self-harm repetition and suicide: the ReACT Self-Harm Rule. Psychological Medicine 42, 23832394.Google Scholar
Stefansson, J, Nordstrom, P and Jokinen, J (2012) Suicide Intent Scale in the prediction of suicide. Journal of Affective Disorders 136, 167171.Google Scholar
Figure 0

Fig. 1. SROC for low and high-risk populations, showing in the low-risk group the 95% CIs.