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

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.

Spatiotemporal clustering of suicide attempt in Kermanshah, West-Iran

Background

A suicide attempt is a major societal problem because it imposes high costs on societies worldwide. This paper analyses the spatiotemporal clustering of suicide attempt in Kermanshah, Iran from 2006-14.

Methods

This study draws on 18,333 individuals (7,234 males and 11,097 females) who attempted suicide across the Kermanshah province. Data was collected from the records of individuals registered in hospitals across the Kermanshah province between 2006 and 2014. Mean Center, Standard Deviational Ellipse (SDE), Moran's I and Kernel Density Estimation (KDE) in Arc/GIS10.6 software were used for the analysis of the spatial distribution of suicide attempt, while the chi-squared test in SPSS was used to examine the different demographic variables between groups within/outside spatial clusters of suicide.

Results

The results show that a total of 18,331 suicide attempts (39.46% male and 60.53% female) were reported between 2006 and 2014 in the Kermanshah province. The spatial pattern of suicide attempts was clustered in 16 clusters (6 high clusters and 10 low clusters) and statistically significant differences were found within and outside the hotspots of suicide attempts. Most hot spots were formed in and around cities. Younger people were at a greater risk. The rate of suicide attempts reduced in illiterate people and increased in people with university degrees. Unmarried people were associated with a higher risk of suicide attempt than was married status for both males and females.

Conclusion

The results of this study could help public health practitioners and policymakers in Iran prioritize resources and target efforts for suicide attempt prevention.

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.

The application of spatial analysis to understanding the association between area-level socio-economic factors and suicide: a systematic review

BACKGROUND

Little is known about what impact the use of different spatial methodological approaches may have on understanding the relationship between area-level socio-economic factors and suicide.

METHODS

In this systematic review, we searched PubMed, Embase, CINAHL and PsycInfo for original empirical studies examining the relationship between socio-economic factors and suicide with a spatial lens, published up to January 22, 2022. Data on applied spatial methods, indicators of socio-economic factors, and risk of suicide related to socio-economic factors were extracted. The protocol for this systematic review was registered with PROSPERO (CRD42021251387).

RESULTS

A systematic search yielded 6290 potentially relevant results; 58 studies met the inclusion criteria for review. Of the 58 included studies, more than half of the studies (n = 34; 58.6%) used methods that accounted for spatial effects in analyses of the association between socio-economic factors and suicide or examined spatial autocorrelation, while 24 (41.4%) studies applied univariate and multivariate models without considering spatial effects. Bayesian hierarchical models and spatial regression models were commonly used approaches to correct for spatial effects. The risk of suicide relating to socio-economic factors varied greatly by local areas and between studies using various socio-economic indicators. Areas with higher deprivation, higher unemployment, lower income, and lower education level were more likely to have higher suicide risk. There was no significant difference in results between studies using conventional versus spatial statistic methods.

CONCLUSION

An increasing number of studies have applied spatial methods, including Bayesian spatial models and spatial regression models, to explore the relationship between area-level socio-economic factors and suicide. This review of spatial studies provided further evidence that area-level socio-economic factors are generally inversely associated with suicide risk, with or without accounting for spatial autocorrelation.

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.

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.

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.

Factors Associated with Suicide Attempts and Suicides in the General Population of Andalusia (Spain)

Discrepant results have been published by studies comparing deaths by suicide with attempted suicides. This study aimed to determine factors associated with suicides and attempted suicides in Andalusia (Spain) between 2007 and 2013, comparing sex, age, year, and suicide method between these populations. A retrospective study was conducted of data on deaths by suicide and attempted suicides over a seven-year period, calculating the sex and age rates for each behavior. Adjusted Poisson regression was used to analyze the association with study variables, and incidence rate ratios were estimated. During the seven-year study period, 20,254 attempted suicides and 5202 deaths by suicide were recorded. The prevalence of attempted suicide did not differ between the sexes, whereas the prevalence of deaths by suicide was three-fold higher among males than among females and increased with higher age. The most frequently used method was the same in males and females for suicide attempts, but differed between the sexes for suicides. The combined influence of sex and age was greater in the model for death by suicide than in the model for attempted suicide. The key differentiating factor was the method used, while the finding of greatest concern was the suicide behavior among the elderly. Preventive strategies should take these differences into account.

Clustering of suicides in children and adolescents

Suicide is one of the major causes of death in young people, in whom suicide can occur in clusters. In this Review, we have investigated definitions and epidemiology of such clusters, the factors associated with them, mechanisms by which they occur, and means of intervening and preventing them. Clustering of suicidal behaviour is more common in young people (<25 years) than adults. Suicide clusters can occur as a greater number of episodes than expected at a specific location, including in institutions (eg, schools, universities, psychiatric units, and youth offender units). They might also involve linked episodes spread out geographically. Locations exposed to clusters can be at risk for future clusters. Mechanisms involved in clusters include social transmission (particularly via person-to-person transmission and the media), perception that suicidal behaviour is widespread, susceptible young people being likely to socialise with others at risk of suicidal behaviour, and social cohesion contributing to the diffusion of ideas and attitudes. The internet and social media might have particularly important roles in spreading suicidal behaviour. The effect of suicide clusters on communities and institutions is usually profound. Experience of intervening in clusters has resulted in best practice guidance. This guidance includes preparation for occurrence of clusters in both community and institutional settings. Identification of clusters in the community requires real-time monitoring of suicidal behaviour. Effective intervention is more likely if a cluster response group is established than if no such group exists. The response should include bereavement support, provision of help for susceptible individuals, proactive engagement with media interest, and population-based approaches to support and prevention. Social media can provide a powerful means for disseminating information and reaching young people at risk.

Mapping rates of inpatient hospitalizations related to mental disorders in the state of Missouri: A conditional autoregressive model with zip code-level data

Nearly one in five American adults suffers from mental illness in a given year. Mental health conditions are known to be spatially clustered, but no prior work has examined the clustering of mental health related hospitalizations. This analysis uses Bayesian hierarchical models to predict rates of inpatient hospitalizations attributed to mental disorders within zip codes in Missouri, USA. Eight separate models were run, and all models yielded similar estimates for the average rate of mental health related hospitalizations (around 13 per 1000 population). The percent of families receiving food stamps and percent of vacant housing were found to be significantly associated with hospitalization rates, after controlling for age, gender, and race. These rates were also significantly spatially clustered (Moran's I > 0.3 and p < 0.05 for all models). Health professionals can use these results to prioritize regions throughout the state that have the greatest need for mental health service providers and interventions.

Comparison of the Clusters and Non-Clusters Areas of Attempted Suicide Cases in Hamadan Province, Western Iran: Findings from a Pilot Study (2016-2017)

BACKGROUND

Suicide behaviors are complex and multifactorial problems that in the most of the societies are considered as the public health challenge. However, its underlying reasons and spatial pattern remain unclear in Hamadan Province, western Iran.

STUDY DESIGN

Secondary analysis of existing data.

METHODS

We assessed the spatial pattern pre-city regarding some influencing factors by scan-statistics and logistic regression to detect clusters areas and its comparison with other areas for the period of 2016-2017. All of the registered cases of attempted suicide in a quality registry system of suicide in Sina (Farshchian) Hospital affiliated to Hamadan University of Medical Sciences, Hamadan, Iran were enrolled.

RESULTS

Two significant clusters were detected in study areas, formed with relative risk at 5.28 (P=0.001) and 6.33 (P=0.017), and with the centrality of Asadabad and Razan, respectively. Clusters and non-clusters areas were differed in terms of location (OR=0.15, 95%, CI: 0.07, 0.31), self-harms methods (OR=0.28, 95%, CI: 0.9, 0.88) and education. Residents of rural areas, illiterate people and non-drug user cases have more likely to be in a cluster.

CONCLUSIONS

Clusters were not formed equally among cities of Hamadan Province. Accordingly, we suggest the implementation of appropriate, long-term and evidence-based educations for high-risk and vulnerable groups through the intersectoral interventions in different parts of Hamadan Province (considering the cluster and non-clusters areas) to avert deaths and related injuries from attempted suicide.