CHERISH: Understanding Suicide Clusters tHrough ExploRIng Self Harm behaviours IJPDS (2017) Issue 1, Vol 1:227 Proceedings of the IPDLN Conference (August 2016)

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

Abstract

ABSTRACT

Objectives
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.

Article Details

How to Cite
Delpozo-Banos, M., Hawton, K., Gunnel, D., Lloyd, K., Scourfield, J., Dennis, M. and John, A. (2017) “CHERISH: Understanding Suicide Clusters tHrough ExploRIng Self Harm behaviours: IJPDS (2017) Issue 1, Vol 1:227 Proceedings of the IPDLN Conference (August 2016)”, International Journal of Population Data Science, 1(1). doi: 10.23889/ijpds.v1i1.247.

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