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

Australian Youth Self-Harm Atlas: spatial modelling and mapping of self-harm prevalence and related risk and protective factors to inform youth suicide prevention strategies

AIMS

Suicide prevention strategies have shifted in many countries, from a national approach to one that is regionally tailored and responsive to local community needs. Previous Australian studies support this approach. However, most studies have focused on suicide deaths which may not fully capture a complete understanding of prevention needs, and few have focused on the priority population of youth. This was the first nationwide study to examine regional variability of self-harm prevalence and related factors in Australian young people.

METHODS

A random sample of Australian adolescents (12-17-year-olds) were recruited as part of the Young Minds Matter (YMM) survey. Participants completed self-report questions on self-harm (i.e., non-suicidal self-harm and suicide attempts) in the previous 12 months. Using mixed effects regressions, an area-level model was built with YMM and Census data to produce out-of-sample small area predictions for self-harm prevalence. Spatial unit of analysis was Statistical Area Level 1 (average population 400 people), and all prevalence estimates were updated to 2019.

RESULTS

Across Australia, there was large variability in youth self-harm prevalence estimates. Northern Territory, Western Australia, and South Australia had the highest estimated state prevalence. Psychological distress and depression were factors which best predicted self-harm at an individual level. At an area-level, the strongest predictor was a high percentage of single unemployed parents, while being in an area where ≥30% of parents were born overseas was associated with reduced odds of self-harm.

CONCLUSIONS

This study identified characteristics of regions with lower and higher youth self-harm risk. These findings should assist governments and communities with developing and implementing regionally appropriate youth suicide prevention interventions and initiatives.

Quantifying suicide contagion at population scale

The spread of suicidal behavior among individuals is often described as a contagion; however, rigorous modeling of suicide as a dynamic, contagious process is minimal. Here, we develop and validate a model-inference system depicting suicide ideation and death and use it to quantify the contagion processes in the US associated with two prominent celebrity suicide events: Robin Williams during 2014 and Kate Spade and Anthony Bourdain, which occurred 3 days apart during 2018. We show that both events produced large transient increases of suicide contagion contact rates, i.e., the spread of suicidal thought and behavior, and a period of elevated suicidal ideation in the general population. Our modeling approach provides a framework for quantifying suicidal contagion and better understanding, preventing, and containing its spread.

High-Risk Suicide Locations in Australia

Importance

Although several interventions have been shown to be effective in preventing suicide at high-risk locations, the potential for these interventions to be deployed is limited by a lack of knowledge about where high-risk locations are and the factors associated with choosing these locations.

Objective

To identify high-risk suicide locations in Australia and the factors associated with choosing these locations.

Design, Setting, and Participants

This case-control study included data on individuals who died by suicide in Australia between January 2001 and December 2017, obtained from the National Coronial Information System. Data analysis was conducted from February to December 2021.

Exposures

Sociodemographic, residential, incident time, and incident location variables.

Main Outcomes and Measures

The scan statistic was used to detect spatial clusters of suicides in public locations. Suicide locations within significant clusters with at least 0.5 suicides per year were defined as high-risk locations. Multivariable logistic regression analyses were performed to examine the factors associated with choosing a high-risk location.

Results

Over the study period, 10 701 suicides took place in public places. The individuals who died of suicide in public places included 8602 (80.4%) male individuals, and most were aged 25 to 49 years (5825 [54.5%]). A total of 17 high-risk suicide locations in Australia were detected. These involved 495 suicides, which accounted for 4.6% of suicides in public locations. For suicides at high-risk locations, 82.2% (407 of 495) occurred at cliffs and bridges. Being female (adjusted odds ratio [aOR], 1.73; 95% CI, 1.41-2.13), employed (aOR, 1.57; 95% CI, 1.20-2.04), never married (aOR, 1.64; 95% CI, 1.26-2.13), and from a major city (aOR, 3.94; 95% CI, 2.94-5.28) were associated with the choice of a high- over low-risk suicide location. High-risk locations tended to be in major cities.

Conclusions and Relevance

This case-control study found 17 high-risk suicide locations in Australia and the factors associated with the choice of these locations. Actions should be taken to prevent suicide at these locations where possible.

An exploratory study on spatiotemporal clustering of suicide in Korean adolescents

BACKGROUND

Adolescent suicides are more likely to form clusters than those of other age groups. However, the definition of a cluster in the space-time dimension has not been established, neither are the factors contributing to it well known. Therefore, this study aimed to identify space-time clusters in adolescent suicides in Korea and to examine the differences between clustered and non-clustered cases using novel statistical methods.

METHODS

From 2016 to 2020, the dates and locations, including specific addresses from which the latitude and longitude of all student suicides (aged 9-18 years) in Korea were obtained through student suicide reports. Sociodemographic characteristics of the adolescents who died by suicide were collected, and the individual characteristics of each student who died by suicide were reported by teachers using the Strengths and Difficulties Questionnaire (SDQ). Density-Based Spatial Clustering of Applications with Noise (DBSCAN) analysis was used to assess the clustering of suicides.

RESULTS

We identified 23 clusters through the data analysis of 652 adolescent suicides using DBSCAN. By comparing the size of each cluster, we identified 63 (9.7%) spatiotemporally clustered suicides among adolescents, and the temporal range of these clusters was 7-59 days. The suicide cluster group had a lower economic status than the non-clustered group. There were no significant differences in other characteristics between the two groups.

CONCLUSION

This study has defined the space-time cluster of suicides using a novel statistical method. Our findings suggest that when an adolescent suicide occurs, close monitoring and intervention for approximately 2 months are needed to prevent subsequent suicides. Future research using DBSCAN needs to involve a larger sample of adolescents from various countries to further corroborate these findings.

Association between mental health workforce supply and clusters of high and low rates of youth suicide: An Australian study using suicide mortality data from 2016 to 2020

OBJECTIVE

To examine the association between mental health workforce supply and spatial clusters of high versus low incidence of youth suicide.

METHODS

A cross-sectional analysis of spatial suicide clusters in young Australians (aged 10-25) from 2016 to 2020 was conducted using the scan statistic and suicide data from the National Coronial Information System. Mental health workforce was extracted from the 2020 National Health Workforce Dataset by local government areas. The Geographic Index of Relative Supply was used to estimate low and moderate-to-high mental health workforce supply for clusters characterised by a high and low incidence of suicide (termed suicide hotspots and coldspots, respectively). Univariate and multivariate logistic regression was used to determine the association between suicide clusters and a range of sociodemographic characteristics including mental health workforce supply.

RESULTS

Eight suicide hotspots and two suicide coldspots were identified. The multivariate analysis showed low mental health workforce supply was associated with increased odds of being involved in a suicide hotspot (adjusted odds ratio = 8.29; 95% confidence interval = 5.20-13.60), followed by residential remoteness (adjusted odds ratio = 2.85; 95% confidence interval = 1.68-4.89), and illicit drug consumption (adjusted odds ratio = 1.97; 1.24-3.11). Both coldspot clusters occurred in areas with moderate-to-high mental health workforce supply.

CONCLUSION

Findings highlight the potential risk and protective roles that mental health workforce supply may play in the spatial distributions of youth suicide clusters. These findings have important implications for the provision of postvention and the prevention of suicide clusters.

Space-time self-harm and suicide clusters in two cities in Taiwan

AIMS

Suicidal acts may cluster in time and space and lead to community concerns about further imitative suicidal episodes. Although suicide clusters have been researched in previous studies, less is known about the clustering of non-fatal suicidal behaviour (self-harm). Furthermore, most previous studies used crude temporal and spatial information, e.g., numbers aggregated by month and residence area, for cluster detection analysis. This study aimed to (i) identify space-time clusters of self-harm and suicide using daily incidence data and exact address and (ii) investigate the characteristics of cluster-related suicidal acts.

METHODS

Data on emergency department presentations for self-harm and suicide deaths in Taipei City and New Taipei City, Taiwan, were used in this study. In all-age and age-specific analyses, self-harm and suicide clusters were identified using space-time permutation scan statistics. A cut-off of 0.10 for the p value was used to identify possible clusters. Logistic regression was used to investigate the characteristics associated with cluster-related episodes.

RESULTS

A total of 5,291 self-harm episodes and 1,406 suicides in Taipei City (2004-2006) and 20,531 self-harm episodes and 2,329 suicides in New Taipei City (2012-2016) were included in the analysis. In the two cities, two self-harm clusters (n [number of self-harm episodes or suicide deaths in the cluster] = 4 and 8 in Taipei City), four suicide clusters (n = 3 in Taipei City and n = 4, 11 and 4 in New Taipei City) and two self-harm and suicide combined clusters (n = 4 in Taipei City and n = 8 in New Taipei City) were identified. Space-time clusters of self-harm, suicide, and self-harm and suicide combined accounted for 0.05%, 0.59%, and 0.08% of the respective groups of suicidal acts. Cluster-related episodes of self-harm and suicide were more likely to be male (adjusted odds ratio [aOR] = 2.22, 95% confidence interval [CI] 1.26, 3.89) and young people aged 10-29 years (aOR = 2.72, 95% CI 1.43, 5.21) than their cluster-unrelated counterparts.

CONCLUSIONS

Space-time clusters of self-harm, suicide, and self-harm and suicide combined accounted for a relatively small proportion of suicidal acts and were associated with some sex/age characteristics. Focusing on suicide deaths alone may underestimate the size of some clusters and/or lead to some clusters being overlooked. Future research could consider combining self-harm and suicide data and use social connection information to investigate possible clusters of suicidal acts.

Quantitative Methods to Detect Suicide and Self-Harm Clusters: A Systematic Review

Suicide and self-harm clusters exist in various forms, including point, mass, and echo clusters. The early identification of clusters is important to mitigate contagion and allocate timely interventions. A systematic review was conducted to synthesize existing evidence of quantitative analyses of suicide and self-harm clusters. Electronic databases including Medline, Embase, Web of Science, and Scopus were searched from date of inception to December 2020 for studies that statistically analyzed the presence of suicide or self-harm clusters. Extracted data were narratively synthesized due to heterogeneity among the statistical methods applied. Of 7268 identified studies, 79 were eligible for narrative synthesis. Most studies quantitatively verified the presence of suicide and self-harm clusters based on the scale of the data and type of cluster. A Poisson-based scan statistical model was found to be effective in accurately detecting point and echo clusters. Mass clusters are typically detected by a time-series regression model, although limitations exist. Recently, the statistical analysis of suicide and self-harm clusters has progressed due to advances in quantitative methods and geospatial analytical techniques, most notably spatial scanning software. The application of such techniques to real-time surveillance data could effectively detect emerging clusters and provide timely intervention.

Responding to Suicide Clusters in the Community: What Do Existing Suicide Cluster Response Frameworks Recommend and How Are They Implemented?

Suicide clusters involve an excessive number of suicides, suicide attempts, or both, that occur close in space or time or involve social links between cluster members. Although suicide clusters are rare, evidence documenting the implementation of suicide cluster response activities in communities is required yet remains limited. In this study, we identified the core components of existing suicide cluster response frameworks through a search of the grey literature and conducted an international survey to assess the implementation of the core components by stakeholders with experience responding to a suicide cluster. The following six core components were identified from five cluster response frameworks and were incorporated into a survey assessing stakeholders’ experiences of responding to a suicide cluster: (1) Preparing for a suicide cluster; (2) Routine monitoring of suicide, suicide attempts, and cluster detection; (3) Coordination with the media and monitoring social media; (4) Identifying and supporting individuals at risk; (5) Promoting help-seeking and building community resilience; and (6) Long-term follow-up and evaluation. Twenty-six stakeholders completed the online survey. Many of the core components were implemented by stakeholders. However, gaps in practice were reported in terms of cluster surveillance, monitoring of referral uptake among bereaved individuals, and long-term evaluation. Barriers to implementation included the perceived availability and suitability of mental health services, and availability of long-term funding. Strategic policy and planning that addresses the practice-based experiences of communities has the potential to facilitate a more coordinated and timely response to suicide clusters.

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.

Spatial Errors in Automated Geocoding of Incident Locations in Australian Suicide Mortality Data

BACKGROUND

There is increasing interest in the spatial analysis of suicide data to identify high-risk (often public) locations likely to benefit from access restriction measures. The identification of such locations, however, relies on accurately geocoded data. This study aims to examine the extent to which common completeness and positional spatial errors are present in suicide data due to the underlying geocoding process.

METHODS

Using Australian suicide mortality data from the National Coronial Information System for the period of 2008-2017, we compared the custodian automated geocoding process to an alternate multiphase process. Descriptive and kernel density cluster analyses were conducted to ascertain data completeness (address matching rates) and positional accuracy (distance revised) differences between the two datasets.

RESULTS

The alternate geocoding process initially improved address matching from 67.8% in the custodian dataset to 78.4%. Additional manual identification of nonaddress features (such as cliffs or bridges) improved overall match rates to 94.6%. Nearly half (49.2%) of nonresidential suicide locations were revised more than 1,000 m from data custodian coordinates. Spatial misattribution rates were greatest at the smallest levels of geography. Kernel density maps showed clear misidentification of hotspots relying solely on autogeocoded data.

CONCLUSION

Suicide incidents that occur at nonresidential addresses are being erroneously geocoded to centralized fall-back locations in autogeocoding processes, which can lead to misidentification of suicide clusters. Our findings provide insights toward defining the nature of the problem and refining geocoding processes, so that suicide data can be used reliably for the detection of suicide hotspots. See video abstract at, http://links.lww.com/EDE/B862.

Risk factors associated with suicide clusters in Australian youth: Identifying who is at risk and the mechanisms associated with cluster membership

BACKGROUND

It is unclear who is at risk of being involved in a suicide cluster and whether suicide clusters are influenced by the social transmission of suicidal behaviour, assortative relating, or a combination of both.

METHODS

Suicide clusters involving two or more young people were identified from the free text of electronic police and coroners reports in Australia's National Coronial Information System in a nationwide cross-sectional study. The duration of survival among exposed cases were estimated using time-to-event methods. The casewise concordance of demographic, social and clinical characteristics and circumstances of death were examined among index and exposed cases.

FINDINGS

We identified links between 117 young people (51 suicide clusters). 50% of young people died within 90 days of the index suicide. Individuals exposed to railway suicide had an 80% probability of dying by the same method. Those exposed to the suicide of a person aged 10-18 years had an 86% probability of being from the same age group. Young people had a 67% and 60% probability of sharing the same characteristics as the index suicide when the index suicide resided in a remote community or was of Aboriginal and Torres Strait Islander descent.

INTERPRETATION

Suicide clusters may be associated with both the social transmission of suicidal behaviour and assortative relating. Individuals who were close to the deceased should be provided with access to postvention support, particularly within the first 90 days of exposure to an index suicide.

FUNDING

Australian Rotary Health, National Health and Medical Research Council.