CHERISH: Understanding Suicide Clusters tHrough ExploRIng Self Harm behaviours
25/08/2016 | 14:35 - 14:55     Room GH043

Marcos del Pozo Banos
Population, Psychiatry, Suicide and Informatics (PPSI), Swansea University Medical School

Presentation Type: Oral

Themes: Applied projects, Data and linkage quality and Public engagement

Session: Parallel Session 5

Authors:

Marcos Delpozo-Banos, Keith Hawton, David Gunnel, Keith Lloyd, Jonathan Scourfield, Michael Dennis and Ann John


Objective:

In Wales suicide accounts for 20% of deaths among men aged 15-24 years and almost 10% of deaths among women of that age. Up to 2% of suicides in young people are thought to occur in clusters. Yet, our understanding of the social and psychological determinants of suicide clusters is limited, with none of the cross-discipline theories proposed having been tested via in-depth research on an actual cluster. This HCRW funded mixed methods study had qualitative and quantitative data linkage work packages to explore here the factors that trigger a suicide cluster, cause it to continue and then eventually subside.

Approach:

The data of 1866 individuals' who attended the Princes of Wales Hospital emergency department (ED) with self harm between 1st January 2006 and 31st December 2013 was anonymously linked within the Secure Anonymised Information Linkage (SAIL) databank. We had a matching rate of 99.7. We performed both time-trend analysis on this data around the apparant suicide cluster in 2007-08, and a comparison across three defined populations: those attending ED at the time of the cluster; those attending during the same period, one year before; and those attending one year after.

Results:

We are able to present the characteristics of those who attend ED during a cluster with self harm compared to those who attend at other times and their long term outcomes.

Conclusion:

To inform the development of appropriate policy to respond to suicide clusters at an early stage.


Conference Proceedings Published By

International Journal of Population Data Science