Application of Scan Statistics to Detect Suicide Clusters in Australia
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2013-01-14
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PLoS ONE
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PUBLIC LIBRARY SCIENCE
Melbourne Authors
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Melbourne School of Population and Global Health
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Journal Article
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Cheung, Y. T. D., Spittal, M. J., Williamson, M. K., Tung, S. J. & Pirkis, J. (2013). Application of Scan Statistics to Detect Suicide Clusters in Australia. Plos One, 8 (1), https://doi.org/10.1371/journal.pone.0054168.
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Abstract
BACKGROUND: Suicide clustering occurs when multiple suicide incidents take place in a small area or/and within a short period of time. In spite of the multi-national research attention and particular efforts in preparing guidelines for tackling suicide clusters, the broader picture of epidemiology of suicide clustering remains unclear. This study aimed to develop techniques in using scan statistics to detect clusters, with the detection of suicide clusters in Australia as example. METHODS AND FINDINGS: Scan statistics was applied to detect clusters among suicides occurring between 2004 and 2008. Manipulation of parameter settings and change of area for scan statistics were performed to remedy shortcomings in existing methods. In total, 243 suicides out of 10,176 (2.4%) were identified as belonging to 15 suicide clusters. These clusters were mainly located in the Northern Territory, the northern part of Western Australia, and the northern part of Queensland. Among the 15 clusters, 4 (26.7%) were detected by both national and state cluster detections, 8 (53.3%) were only detected by the state cluster detection, and 3 (20%) were only detected by the national cluster detection. CONCLUSIONS: These findings illustrate that the majority of spatial-temporal clusters of suicide were located in the inland northern areas, with socio-economic deprivation and higher proportions of indigenous people. Discrepancies between national and state/territory cluster detection by scan statistics were due to the contrast of the underlying suicide rates across states/territories. Performing both small-area and large-area analyses, and applying multiple parameter settings may yield the maximum benefits for exploring clusters.