Research indicates substantial overlap between child abuse and neglect (CAN), exposure to domestic violence and sibling abuse, with multiple victimisation experiences conferring greater risk for adverse mental health outcomes than does exposure to a single subtype. The application of latent class analysis (LCA) to child maltreatment has gained momentum, but it remains the case that few studies have incorporated a comprehensive range of subtypes, meaning that real-life patterns in victimisation experiences cannot be accurately modelled. Based on self-report data from an ethnically diverse sample (N = 2,813) of 10-17 year olds in the United Kingdom, the current study used LCA to model constellations among nine types of maltreatment in the home (physical, emotional, and sexual abuse; physical and emotional neglect; exposure to physical and verbal domestic violence, or a drug-related threat; and sibling violence). A four-class solution comprising of a low victimisation class (59.3% of participants), an emotional abuse and neglect class (19.0%), a high verbal domestic violence class (10.5%), and a maltreatment and domestic violence class (11.2%) provided the best fit for the data. Associations with sociodemographic variables were examined, revealing differences in the composition of the classes. Compared to the low victimisation class, participants in the verbal domestic violence class, emotional abuse and neglect class, and especially the maltreatment and domestic violence class, reported higher symptoms of anxiety and depression and an increased likelihood of non-suicidal self-injury, suicide ideation and suicide attempt. The findings carry important implications for understanding patterns of child maltreatment, and the implications for preventative strategies and support services are discussed.
|Number of pages||22|
|Journal||Journal of Interpersonal Violence|
|Publication status||Published - 21 Apr 2022|
Bibliographical noteFunding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the publication is based on data collected by the None in Three Research Centre. The research was funded by the UK Research and Innovation (UKRI) (project reference: AH/P014240/1) and University of Huddersfield. The contents of this publication are the sole responsibility of its authors and can in no way be taken to reflect the views of the UKRI.
© The Author(s) 2022.