Understanding the characteristics and mechanisms underlying suicide clusters in Australian youth: a comparison of cluster detection methods. (6th August 2020)

• Record Type: Journal Article
• Authors: Hill, N.T.M.; Too, L.S.; Spittal, M.J.; Robinson, J.
• Is Part Of: Epidemiology and psychiatric sciences. Volume 29(2020)
• Publication Date: 2020-08-06
• Subjects: Adolescents, -- epidemiology, -- research design and methods, -- suicide; Epidemiology -- Periodicals; Mental illness -- Periodicals; Community psychiatry -- Periodicals; 362.2
• DOI: 10.1017/S2045796020000645
• Languages: English
• ISSNs: 2045-7960

Abstract

Abstract: Aims: There is currently no gold-standard definition or method for identifying suicide clusters, resulting in considerable heterogeneity in the types of suicide clusters that are detected. This study sought to identify the characteristics, mechanisms and parameters of suicide clusters using three cluster detection methods. Specifically, the study aimed to: (1) determine the overlap in suicide clusters among each method, (2) compare the spatial and temporal parameters associated with different suicide clusters and (3) identify the demographic characteristics and rates of exposure to suicide among cluster and non-cluster members. Methods: Suicide data were obtained from the National Coronial Information System. N = 3027 Australians, aged 10–24 who died by suicide in 2006–2015 were included. Suicide clusters were determined using: (1) poisson scan statistics, (2) a systematic search of coronial inquests and (3) descriptive network analysis. These methods were chosen to operationalise three different definitions of suicide clusters, namely clusters that are: (1) statistically significant, (2) perceived to be significant and (3) characterised by social links among three or more suicide descendants. For each method, the demographic characteristics and rates of exposure to suicide were identified, in addition to the maximum duration of suicide clusters, the geospatial overlap between suicide clusters, and the overlap of individual cluster members. Results: Eight suicide clusters (69 suicides) were identified from the scan statistic, seven (40 suicides) from coronial inquests; and 11 (37 suicides) from the descriptive network analysis. Of the eight clusters detected using the scan statistic, two overlapped with clusters detected using the descriptive network analysis and one with clusters identified from coronial inquests. Of the seven clusters from coronial inquests, four overlapped with clusters from the descriptive network analysis and one with clusters from the scan statistic. Overall, 9.2% (12 suicides) of individuals were identified by more than one method. Prior exposure to suicide was 10.1% ( N = 7) in clusters from the scan statistic, 32.5% ( N = 13) in clusters from coronial inquest and 56.8% ( N = 21) in clusters from the descriptive network analysis. Conclusion: Each method identified markedly different suicide clusters. Evidence of social links between cluster members typically involved clusters detected using the descriptive network analysis. However, these data were limited to the availability information collected as part of the police and coroner investigation. Communities tasked with detecting and responding to suicide clusters may benefit from using the spatial and temporal parameters revealed in descriptive studies to inform analyses of suicide clusters using inferential methods.