Preprint Article Version 1 This version is not peer-reviewed

A Network Perspective on Suicidal Behaviour

Version 1 : Received: 24 January 2017 / Approved: 25 January 2017 / Online: 25 January 2017 (17:19:48 CET)
Version 2 : Received: 8 February 2017 / Approved: 8 February 2017 / Online: 8 February 2017 (09:39:34 CET)
Version 3 : Received: 11 February 2017 / Approved: 13 February 2017 / Online: 13 February 2017 (11:04:49 CET)

A peer-reviewed article of this Preprint also exists.

de Beurs, D. Network Analysis: A Novel Approach to Understand Suicidal Behaviour. Int. J. Environ. Res. Public Health 2017, 14, 219. de Beurs, D. Network Analysis: A Novel Approach to Understand Suicidal Behaviour. Int. J. Environ. Res. Public Health 2017, 14, 219.

Journal reference: IJERPH 2017, 14, 219
DOI: 10.3390/ijerph14030219

Abstract

Although suicide is a major public health issue worldwide, we understand little of the onset and development of suicidal behavior. Suicidal behavior is argued to be the end result of the complex interaction between social, biological and environmental factors. Traditional epidemiological analytics techniques are not equipped to deal with this complexity. A new technique called network analysis can help us better understand suicidal behavior as it allows to visualize and quantify complex association between many symptoms. It moves away from the idea that symptoms are caused by an underlying common cause such as depression or suicidality. Instead, symptoms are thought to cause each other. A network perspective has been successfully applied to the field of depression, psychosis and PTSD, but not yet to the field of suicidology. In this perspective article, I will argue that a network perspective on suicidal behavior can help us to 1) better understand suicidal behavior, 2) develop more sensitive diagnostic tools for subgroups of patients, and 3) help the personalized treatment of suicidal behavior. I will provide examples based on real data, and offer directions for future studies.

Subject Areas

suicide; networkanalysis; symptoms; personalized treatment

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