Background: Identifying high-risk groups of non-suicidal self-injury (NSSI) with multiple risk factors and different functional subtypes contribute to implementing person-centered interventions.
Methods: We investigated NSSI profiles among a sample of 258 psychiatric inpatients aged 18-25 years. All participants completed well-validated measures of internal personal and external environmental characteristics. One-hundred and ninety patients reported a lifetime history of NSSI and completed an additional NSSI assessment. A k-means cluster analysis was conducted to extract characteristics of risk factors and functional subtypes. Independent sample t-test, analysis of variance and χ 2 test were used to test the difference of demographic statistical factors, risk factors and functional scores among groups with different frequency of NSSI.
Results: The clustering of risk factors analyses supported 4-clusters. The proportion of repeat NSSI patients was the highest (67.1%) in the group with unfavorable personal and unfavorable environmental characteristics. Functional subtype clustering analyses supported 5-clusters. Among patients with repeated NSSI, those with depression were mainly accompanied by the "Sensation Seeking" subtype (39.7%), bipolar disorder mainly supported the "Anti-suicide" subtype (37.9%), and eating disorders were mostly "Social Influence" subtype (33.3%). There was an interaction between functional subtypes and mental disorders.
Limitations: All participants were in treatment in a psychiatric service and the results may not be generalizable to a community sample. The data included retrospective self-report which may be inaccurate due to recall bias.
Conclusion: It is necessary to identify high-risk groups of NSSI who with unfavorable personal and environmental characteristics and clinical interventions need to consider the heterogeneity of patients' functional subtypes of NSSI.
Keywords: cluster analysis; mental disorder; non-suicidal self-injury; risk factor; young adult.
Copyright © 2023 Yan, Zhang, Lu, Li, Ge, Mei, Kang, Sun, Li, Yan, Yang, Song, Shi, Shang and Yue.