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Clusters of suicides and suicide attempts: detection, proximity and correlates

Published online by Cambridge University Press:  09 June 2016

L. S. Too ,
J. Pirkis ,
A. Milner  and
M. J. Spittal
L. S. Too
Affiliation:
Centre for Mental Health, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
J. Pirkis
Affiliation:
Centre for Mental Health, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
A. Milner
Affiliation:
Deakin Population Health SRC, School of Health and Social Development, Deakin University, Victoria, Australia Centre for Health Equity, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
M. J. Spittal*
Affiliation:
Centre for Mental Health, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
*
*Address for correspondence: M. J. Spittal, Melbourne School of Population and Global Health, The University of Melbourne, Parkville 3010, Victoria, Australia. (Email: m.spittal@unimelb.edu.au)
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Abstract

Background.

A suicide cluster is defined as a higher number of observed cases occurring in space and/or time than would typically be expected. Previous research has largely focused on identifying clusters of suicides, while there has been comparatively limited research on clusters of suicide attempts. We sought to identify clusters of both types of behaviour, and having done that, identify the factors that distinguish suicide attempts inside a cluster from those that were outside a cluster.

Methods.

We used data from Western Australia from 2000 to 2011. We defined suicide attempts as admissions to hospital for deliberate self-harm and suicides as deaths due to deliberate self-harm. Using an analytic strategy that accounted for the repetition of attempted suicide within a cluster, we performed spatial-temporal analysis using Poisson discrete scan statistics to detect clusters of suicide attempts and clusters of suicides. Logistic regression was then used to compare clustered attempts with non-clustered attempts to identify risk factors for an attempt being in a cluster.

Results.

We detected 350 (1%) suicide attempts occurring within seven spatial-temporal clusters and 12 (0.6%) suicides occurring within two spatial-temporal clusters. Both of the suicide clusters were located within a larger but later suicide attempt cluster. In multivariate analysis, suicide attempts by individuals who lived in areas of low socioeconomic status had higher odds of being in a cluster than those living in areas of high socioeconomic status [odds ratio (OR) = 29.1, 95% confidence interval (CI) = 6.3–135.5]. A one percentage-point increase in the proportion of people who had changed address in the last year was associated with a 60% increase in the odds of the attempt being within a cluster (OR = 1.60, 95% CI = 1.29–1.98) and a one percentage-point increase in the proportion of Indigenous people in the area was associated with a 7% increase in the suicide being within a cluster (OR = 1.07, 95% CI = 1.00–1.13). Age, sex, marital status, employment status, method of harm, remoteness, percentage of people in rented accommodation and percentage of unmarried people were not associated with the odds of being in a suicide attempt cluster.

Conclusions.

Early identification of and responding to suicide clusters may reduce the likelihood of subsequent clusters forming. The mechanisms, however, that underlie clusters forming is poorly understood.

Keywords

Epidemiologymental healthrisk factorssuicide
Type
Original Articles
Information
Epidemiology and Psychiatric Sciences , Volume 26 , Issue 5 , October 2017 , pp. 491 - 500
Copyright
Copyright © Cambridge University Press 2016 

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