Suicide is among the leading causes of death worldwide. Despite decades of research focused on suicide, we still remain extremely limited in our understanding of this problem and in our ability to predict and prevent it. Sadly, the current state-of-the-art in identifying those at risk for suicide continues to be to wait for such individuals to inform others of their suicidal thoughts, and once they do, to tell a clinician when they believe they might act upon those thoughts. Given this state of affairs, many suicides appear to occur out of the blue, without any warning to family, friends, or healthcare professionals. Indeed, prior studies suggest that 30-40% of people who die by suicide never told anyone that they were considering suicide before their death [1, 2] and nearly 80% of those who died by suicide while in hospital care explicitly denied suicidal thoughts or intentions in their last assessment before dying by suicide [3]. What is needed are new, objective methods of identifying those at risk of suicidal behavior that don’t rely on a person’s self-report of suicidal thoughts or behaviors.

People diagnosed with MS have a risk of suicide that is twice as high as that among people of similar ages and backgrounds but without MS [4]. Although the link between MS and suicidal behavior is well known, and several risk factors for suicide among those with MS have been identified, the field currently lacks a method for synthesizing knowledge of these risk factors in a way that tells us which people with MS are at the highest risk for suicide, and should be acted upon by clinicians. As such, clinicians are left to use their intuition in guessing at which patients with MS may be most likely to develop suicidal thoughts or behaviors. Sadly, clinicians are no better than chance (e.g., a coin toss) at predicting suicidal behavior in their patients [5]. The Grant Gordon Foundation has chosen to fund research conducted by Dr.Matthew Nock and Harvard University to address this problem and to advance the understanding of this under-studied area.

Dr. Nock is a leading clinical psychologist and professor at Harvard University, where he is also the Director of the Laboratory for Clinical and Developmental Research. He, along with Drs. Ben Reis at Boston Chldrens Hospital and Jordan Smoller at Massachusetts General Hospital, will be developing an electronic suicide prediction tool that uses information from electronic health records to create algorithms that can be used to identify those at risk for future suicidal behavior. This project will focus specifically on the prediction of suicide risk among those diagnosed with Multiple Sclerosis and other neurological conditions.

The research leverages an extremely large and representative database in combination with powerful analytics tools to tailor earlier work by studying suicidal behavior among those with MS and other neurological conditions. As such, this study has the potential both to identify new risk factors for suicide in this group, and to provide predictive models that could be used by clinicians to improve the detection of patients at risk for suicidal behavior. This project will immediately lead to expanding education and clinical care to those affected by Multiple Sclerosis

  • References
  • 1. Cavanagh, J.T., et al., Psychological autopsy studies of suicide: a systematic review. Psychol Med, 2003. 33(3): p. 395-405.
  • 2. Robins, E., et al., The communication of suicidal intent: a study of 134 consecutive cases of successful (completed) suicide. Am J Psychiatry, 1959. 115(8): p. 724-33.
  • 3. Busch, K.A., J. Fawcett, and D.G. Jacobs, Clinical correlates of inpatient suicide. Journal of Clinical Psychiatry, 2003. 64(1): p. 14-9.
  • 4. Manouchehrinia, A., et al., Mortality in multiple sclerosis: meta-analysis of standardised mortality ratios. J Neurol Neurosurg Psychiatry, 2015.
  • 5. Nock, M.K., et al., Measuring the suicidal mind: Implicit cognition predicts suicidal behavior. Psychological Science, 2010. 21(4): p. 511-7.