A Pilot Case-Control Study of the Social Media Activity Following Cluster and Non-Cluster Suicides in Australia

Social media may play a role in the “contagion” mechanism thought to underpin suicide clusters. Our pilot case-control study presented a novel methodological approach to examining whether Facebook activity following cluster and non-cluster suicides differed. We used a scan statistic to identify suicide cluster cases occurring in spatiotemporal clusters and matched each case to 10 non-cluster control suicides. We identified the Facebook accounts of 3/48 cluster cases and 20/480 non-cluster controls and their respective friends-lists and retrieved 48 posthumous posts and replies (text segments) referring to the deceased for the former and 606 for the latter. We examined text segments for “putatively harmful” and “putatively protective” content (e.g., discussion of the suicide method vs. messages discouraging suicidal acts). We also used concept mapping, word-emotion association, and sentiment analysis and gauged user reactions to posts using the reactions-to-posts ratio. We found no “putatively harmful” or “putatively protective” content following any suicides. However, “family” and “son” concepts were more common for cluster cases and “xx”, “sorry” and “loss” concepts were more common for non-cluster controls, and there were twice as many surprise- and disgust-associated words for cluster cases. Posts pertaining to non-cluster controls were four times as receptive as those about cluster cases. We hope that the approach we have presented may help to guide future research to explain suicide clusters and social-media contagion.

Suicide contagion, the suicide pact and the effects of suicidal behaviour in therapeutic and family relationships

This article is about suicide and relationships. How suicidal thoughts and behaviours can impact relationships for the suicidal person and those around them. This includes relationships between the suicidal person and other suicidal people as well as the suicidal person and others who are non-suicidal. How the suicidal can impact the other and how the other in turn then impacts the suicidal person back. What effects they have on each other in terms of how they think and feel and then how that effects their transactions with each other. More specifically it examines suicide clusters, suicide pacts, suicidality in the therapeutic relationship and suicidality in family relationships.

Understanding the characteristics and mechanisms underlying suicide clusters: a comparison of cluster detection methods in Australian youth

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 descendents. 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 was largely limited to 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.

The effects of interventions preventing self-harm and suicide in children and adolescents: an overview of systematic reviews

Background: Self-harm and suicide in children and adolescents are of serious consequence and increase during the adolescent years. Consequently, there is need for interventions that prevent such behaviour. The objective of this paper: to evaluate the effects of interventions preventing self-harm and suicide in children and adolescents in an overview of systematic reviews. Methods: We conducted an overview of systematic reviews (OoO). We included reviews evaluating any preventive or therapeutic intervention. The methodological quality of the included reviews was assessed independently, and data was extracted by two reviewers. We report the review findings descriptively. The certainty of evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation (GRADE). Results: Moderate certainty evidence suggests that school-based interventions prevent suicidal ideation and attempts short term, and possibly suicide attempts long term. The effects of community-based interventions following suicide clusters and local suicide plans are unknown, as are the benefits and harms of screening young people for suicide risk. The effects of most interventions targeting children and adolescents with known self-harm are unknown. However, low certainty evidence suggests that dialectical behavioural therapy and developmental group therapy are equally as effective on repetition of self-harm as enhanced treatment as usual. Conclusions: Research on several recommended practices, such as local suicide plans, prevention of suicide clusters and approaches to risk assessment, is lacking. When such interventions are implemented, the effects should be closely evaluated. There is also need for more research on treatment of repeated self-harm. Further research should include long term follow-up, and investigate possible adverse effects. In prevention of self-harm and suicide in children and adolescents, policy makers and health providers should consider evidence from population-based studies with mixed-age samples, adult samples, and studies on conditions associated with self-harm and/or suicidality, such as depression and psychosis. PROSPERO registration: CRD42019117942 08/02/19

Understanding the characteristics and mechanisms underlying suicide clusters in Australian youth: a comparison of cluster detection methods

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.