Real-time surveillance for evidence-based responses to suicide contagion and clustering
University College Cork
Background: Although a rare phenomenon, suicide clusters are a cause for great community concern. In recent years, strong emphasis has been placed on the importance of detection and real-time surveillance of suicide clusters. Cluster detection increases understanding on the aetiology of a suicide cluster and provides the basis for targeted intervention to mitigate further contagion through the identification of linked cases and socioecological factors associated with the increased risk of clustering in the affected area or population. Policy makers and public health officials benefit from detection of suicide clusters by means of implementing targeted evidence-based interventions in identified vulnerable populations and high-risk areas in a timely manner. To date, suicide cluster detection has been largely restricted to retrospective investigations, limiting its capacity as a tool for intervention. Aim: The aim of this research was to provide a comprehensive understanding of the real-time surveillance, detection, and responses to suicide contagion and clustering. Methods: The thesis is comprised of five interrelated studies, based on both primary and secondary research. Primary data collection was conducted on coronial records of post-inquest cases of confirmed suicide or open verdicts meeting the criteria for probably suicide, as well as pre-inquest cases of suspected suicide. This research also involved quantitative Census data from the Central Statistics Office (CSO). The retrospective and prospective space-time scan statistics based on a discrete Poisson model was employed via the R software environment using the ‘rsatscan’ and ‘shiny’ packages to conduct the space-time cluster analysis and deliver the mapping and graphic components of the dashboard interface in study 3. Evidence synthesis was conducted by means of narrative review of existing literature in studies 1 and 4, and by comparative review of response to a structured questionnaire in study 2. Study 5 employs a secondary research approach to report on policy and practice implications of real-time suicide surveillance. Results: Study 1 synthesised the existing evidence on quantitative techniques to detect suicide and self-harm clusters detection, revealing that a Poisson-based scan statistical model is most effective in accurately detecting point and echo suicide clusters, while mass clusters are typically detected by a time-series regression model, albeit that limitations exist. Study 2 demonstrated more commonalities than differences in a comparison of the components and practices of real-time suicide surveillance systems internationally. Commonalities included rapid, routine surveillance based on minimal, provisional data to facilitate timely intervention and postvention efforts. Identified differences include timeliness of case submission and system infrastructure. Study 3 tested the validity of the scan statistic as a cluster detection approach inbuilt in a dashboard prototype developed to visually display real-time suicide surveillance data. Study 4 identified consistency in both increased quantity of media reports and portrayal of specific details of suicide cases to be significantly associated with suicide contagion and increased suicide rates or mass clusters. An elevated period of risk of suicide contagion has been found to take place between the first days up to the first three months following the media coverage of suicide. Study 5 demonstrated the importance of real-time suicide surveillance in the context of policy and practice, with a particular reference to public health emergencies and humanitarian crises. Conclusions: The findings of this thesis are of relevance in furthering our knowledge of monitoring, detecting, and responding to suicide clusters. Collectively, the findings from this thesis indicate that we can work more efficiently and collectively to mitigate further suicidal behaviour by utilising real-time, provisional suicide data to guide quicker action. The outcomes of this research have methodological implications in terms of suicide and self-harm cluster detection and real-time suicide surveillance. The implications of this research further extend to suicide prevention and mental health policy, clinical practice, means restriction, crisis planning and response, and media reporting of suicide.
Suicide , Prevention , Real-time , Surveillance , Contagion , Clustering , Early intervention , Evidence-base
Benson, R. 2022. Real-time surveillance for evidence-based responses to suicide contagion and clustering. PhD Thesis, University College Cork.