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Known unknowns and unknown unknowns in suicide risk assessment: Evidence from meta-analyses of aleatory and epistemic uncertainty

  • Matthew Large (a1), Cherrie Galletly (a2), Nicholas Myles (a3), Christopher James Ryan (a4) and Hannah Myles (a2)...
Summary

Suicide risk assessment aims to reduce uncertainty in order to focus treatment and supervision on those who are judged to be more likely to die by suicide. In this article we consider recent meta-analytic research that highlights the difference between uncertainty about suicide due to chance factors (aleatory uncertainty) and uncertainty that results from lack of knowledge (epistemic uncertainty). We conclude that much of the uncertainty about suicide is aleatory rather than epistemic, and discuss the implications for clinicians.

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Copyright
This is an open-access article published by the Royal College of Psychiatrists and distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Corresponding author
Correspondence to Matthew M. Large (mmbl@bigpond.com)
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Declaration of interest

None.

Footnotes
References
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1 Aven, T. On different types of uncertainties in the context of the precautionary principle. Risk Anal 2011; 31: 1515–25.
2 Buchanan, A. Violence risk assessment in clinical settings: being sure about being sure. Behav Sci Law 2013; 31: 7480.
3 Large, M. The relevance of the early history of probability theory to current risk assessment practices in mental health care. Hist Psychiatry 2013; 24: 427–41.
4 O'Connor, N, Warby, M, Raphael, B, Vassallo, T. Changeability, confidence, common sense and corroboration: comprehensive suicide risk assessment. Australas Psychiatry 2004; 12: 352–60.
5 Silverman, MM, Berman, AL. Suicide risk assessment and risk formulation part I: a focus on suicide ideation in assessing suicide risk. Suicide Life Threat Behav 2014; 44: 420–31.
6 Antai-Otong, D. What every ED nurse should know about suicide risk assessment. J Emerg Nurs 2016; 42: 31–6.
7 Motto, JA, Heilbron, DC, Juster, RP. Development of a clinical instrument to estimate suicide risk. Am J Psychiatry 1985; 142: 680–6.
8 Large, M, Smith, G, Sharma, S, Nielssen, O, Singh, SP. Systematic review and meta-analysis of the clinical factors associated with the suicide of psychiatric in-patients. Acta Psychiatr Scand 2011; 124: 1829.
9 Large, M, Sharma, S, Cannon, E, Ryan, C, Nielssen, O. Risk factors for suicide within a year of discharge from psychiatric hospital: a systematic meta-analysis. Aust NZ J Psychiatry 2011; 45: 619–28.
10 Chapman, CL, Mullin, K, Ryan, CJ, Kuffel, A, Nielssen, O, Large, MM. Meta-analysis of the association between suicidal ideation and later suicide among patients with either a schizophrenia spectrum psychosis or a mood disorder. Acta Psychiatr Scand 2015; 131: 162–73.
11 Ribeiro, JD, Franklin, JC, Fox, KR, Bentley, KH, Kleiman, EM, Chang, BP, et al. Self-injurious thoughts and behaviors as risk factors for future suicide ideation, attempts, and death: a meta-analysis of longitudinal studies. Psych Med 2016; 46: 225–36.
12 Chan, MKY, Bhatti, H, Meader, N, Stockton, S, Evans, J, O'Connor, RC, et al. Predicting suicide following self-harm: systematic review of risk factors and risk scales. Br J Psychiatry 2016; 209: 277–83.
13 Pokorny, AD. Prediction of suicide in psychiatric patients. Report of a prospective study. Arch Gen Psychiatry 1983; 40: 249–57.
14 Large, M, Kaneson, M, Myles, N, Myles, H, Gunaratne, P, Ryan, C. Meta-analysis of longitudinal cohort studies of suicide risk assessment among psychiatric patients: heterogeneity in results and lack of improvement over time. PLoS ONE 2016; 11: e0156322.
15 Carroll, R, Metcalfe, C, Steeg, S, Davies, NM, Cooper, J, Kapur, N, et al. Psychosocial assessment of self-harm patients and risk of repeat presentation: an instrumental variable analysis using time of hospital presentation. PloS ONE 2016; 11: e0149713.
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Known unknowns and unknown unknowns in suicide risk assessment: Evidence from meta-analyses of aleatory and epistemic uncertainty

  • Matthew Large (a1), Cherrie Galletly (a2), Nicholas Myles (a3), Christopher James Ryan (a4) and Hannah Myles (a2)...
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eLetters

Known but unpredictable - an argument for complexity.

Martin Plöderl, Clinical Psychologist, Dept. of Clinical Psychology, Christian Doppler Clinic, Paracelsus Medical University
Clemens Fartacek, Clinical Psychologist, Dept. of Clinical Psychology, Christian Doppler Clinic, Paracelsus Medical University
16 February 2018

Since the seminal paper of Pokorny in 1983 (1), the prediction of suicides has not improved over the last decades, as Large et al. pointed out in their current paper (2) and in previous meta-analyses (3-7). In opposition to most current recommendations in suicide prevention that still require clinicians to formulate levels of suicide risk (8), Large et al. (2) suggest that clinicians should give up risk formulation and instead focus directly on the individual needs of patients to deliver optimal care. They argue that uncertainty in the prediction of suicide is largely aleatory (random processes) and also epistemic (lacking knowledge). We think that one important explanation is missing: complexity. Complexity refers to behaviors produced by nonlinear dynamic systems, which cannot be predicted in the long run, even if the generating system operates completely deterministically and is known in detail. Amongst others, the most prominent type of complex dynamics is deterministic chaos, which became familiar as the butterfly effect. During chaotic dynamics, even smallest differences in initial conditions lead to a massive divergence of the trajectories over time. Because of complex behaviors like chaos, from a nonlinear dynamic perspective, the failure of long-term predictions of suicidal behavior could not only be a consequence of complete epistemic knowledge (e.g., unspecific or unknown risk factors) or aleatory processes (random noise) but also a consequence of the inherent complexity of the underlying system. Are there any alternatives to predict suicidal behavior from a nonlinear dynamic perspective? Natural sciences (e.g., geophysics) developed methods for the short-term prediction of extreme events (e.g., tsunamis), based on continuous monitoring of appropriate signals and the identification of non-linear dynamical precursors (9, 10). This might be a promising approach for suicide research as well. With the current improvements of scientific methods, an empirical application of complexity theory in suicide research seems realistic (11, 12). However, it still has to be demonstrated that such novel approaches are feasible in clinical practice and can in fact improve the prediction of suicides. We believe that suicidology needs to take complexity theory into consideration. If not, much time, effort, and money continues to go into approaches that, from the view of complexity theory, are leading to a dead end. This includes the search for novel risk factors, or some combinations of risk factors (e.g., by applying machine learning) without acknowledging the underlying complex processes.

References

1. Pokorny AD. Prediction of suicide in psychiatric patients. Report of a prospective study. Arch Gen Psychiatry. 1983;40(3):249-57.

2. Large M, Galletly C, Myles N, Ryan CJ, Myles H. Known unknowns and unknown unknowns in suicide risk assessment: evidence from meta-analyses of aleatory and epistemic uncertainty. BJPsych Bull. 2017;41(3):160-3.

3. Chung DT, Ryan CJ, Hadzi-Pavlovic D, Singh SP, Stanton C, Large MM. Suicide Rates After Discharge From Psychiatric Facilities: A Systematic Review and Meta-analysis. JAMA psychiatry. 2017;74(7):694-702.

4. Large M, Kaneson M, Myles N, Myles H, Gunaratne P, Ryan C. Meta-Analysis of Longitudinal Cohort Studies of Suicide Risk Assessment among Psychiatric Patients: Heterogeneity in Results and Lack of Improvement over Time. PLoS One. 2016;11(6):e0156322.

5. Large M, Myles N, Myles H, Corderoy A, Weiser M, Davidson M, et al. Suicide risk assessment among psychiatric inpatients: a systematic review and meta-analysis of high-risk categories. Psychol Med. 2017:1-12.

6. Large M, Sharma S, Cannon E, Ryan C, Nielssen O. Risk factors for suicide within a year of discharge from psychiatric hospital: a systematic meta-analysis. Aust N Z J Psychiatry. 2011;45(8):619-28.

7. Walsh G, Sara G, Ryan CJ, Large M. Meta-analysis of suicide rates among psychiatric in-patients. Acta Psychiatr Scand. 2015;131(3):174-84.

8. Jacobs D, Brewer M. APA practice guideline - Provides recommendations for assessing and treating patients with suicidal behaviors. Psychiatric Annals. 2004;34(5):373-80.

9. Albeverio S, Jentsch V, Kantz H. Extreme events in nature and society. New York: Springer; 2006.

10. Albeverio S, Piterbarg V. Mathematical methods and concepts for the analysis of extreme events. In: Albeverio S, Jentsch V, Kantz H, editors. Extreme events in nature and society. New York: Springer; 2006. p. 47-68.

11. Fartacek C, Schiepek G, Kunrath S, Fartacek R, Ploderl M. Real-Time Monitoring of Non-linear Suicidal Dynamics: Methodology and a Demonstrative Case Report. Front Psychol. 2016;7:130.

12. Schiepek G, Fartacek C, Sturm J, Kralovec K, Fartacek R, Ploderl M. Nonlinear dynamics: theoretical perspectives and application to suicidology. Suicide Life Threat Behav. 2011;41(6):661-75.
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Conflict of interest: None declared

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