The identification of clustering in parasuicide

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.

Patterns of Non-Suicidal Self-Injury and Their Relationship with Suicide Attempts in Youth with Borderline Personality Disorder

The study aimed to identify the relationships between patterns of non-suicidal self-injury (NSSI), their severity, and suicide attempts among 107 youth (aged 15-25 years) with borderline personality disorder (BPD). Two principal patterns were identified via a graphical representation of retrospectively reported 12-month histories of NSSI. These were habitual (NSSI occurring at regular intervals) and random patterns (NSSI inconsistently spaced). Habitual patterns of NSSI were associated with lower severity and fewer suicide attempts than random patterns. Within-person comparisons revealed a reduction in NSSI engaged within a habitual pattern and an increase in NSSI engaged within a random pattern in the month prior to a suicide attempt. Findings suggest that the accuracy of risk assessments among youth with BPD might be improved by identifying an individual's historical pattern of NSSI, as well as any relative increase in NSSI engaged within a random pattern or relative reduction in NSSI engaged within a habitual pattern.

Multilevel modeling was a convenient alternative to common regression designs in longitudinal suicide research

BACKGROUND AND OBJECTIVE

The prospective investigation of repetitive nonfatal suicidal behavior is associated with two methodological problems. Due to the commonly used definitions of nonfatal suicidal behavior, clinical samples usually consist of patients with a considerable between-person variability. Second, repeated nonfatal suicidal episodes of the same subjects are likely to be correlated. We examined three regression techniques to comparatively evaluate their efficiency in addressing the given methodological problems.

STUDY DESIGN AND SETTING

Repeated episodes of nonfatal suicidal behavior were assessed in two independent patient samples during a 2-year follow-up period. The first regression design modeled repetitive nonfatal suicidal behavior as a summary measure. The second regression model treated repeated episodes of the same subject as independent events. The third regression model represented a hierarchical linear model.

RESULTS

The estimated mean effects of the first model were likely to be nonrepresentative for a considerable part of the study subjects. The second regression design overemphasized the impact of the predictor variables. The hierarchical linear model most appropriately accounted for the heterogeneity of the samples and the correlated data structure.

CONCLUSION

The nonhierarchical regression designs did not provide appropriate statistical models for the prospective investigation of repetitive nonfatal suicidal behavior. Multilevel modeling provides a convenient alternative.