In this article we use unsupervised machine learning to discover hidden structures and patterns in a longitudinal police dataset of domestic abuse suspects, to provide a police force with an overarching or ‘baseline’ picture of how domestic abuse manifests locally. 3 algorithms were used to analyse 12 variables in a longitudinal dataset of over 40,000 suspects, organising them into discreet “clusters” or profiles with common characteristics and highlighting the differences and continuities between these. The quantitative findings, which highlighted clusters of abuse that had not previously been ‘on the radar’ of domestic abuse services in the specific force area, were then contextualised through qualitative interviews with a range of stakeholders to help identify priorities for intervention and further research. Our study shows how cutting-edge quantitative methods can be applied to improve understanding of prevalence and features of police-recorded abuse; draw attention to previously under-addressed types of abuse; serve as the groundwork for further, more in-depth research; and provide an evidence-base for local decision-making.

Purpose: As a phenomenon, abusive behavior between adolescents in intimate relationships remains relatively invisible, due in part to the persistent yet unfounded assumption that domestic abuse is something that occurs between adults. This review investigates adolescent domestic abuse (ADA), by focusing on the impacts and risk factors for adolescents, particularly those under the age of 16, experiencing ADA. Methods: We conducted a rapid systematic review by searching three electronic databases (PsycInfo, Embase, and Social Sciences Citation Index). We utilized pre-existing systematic reviews to identify relevant primary studies. Findings of the included studies were described and summarized using narrative synthesis. Results: Seventy-nine studies were identified for inclusion. Synthesis of the findings of these studies identified five categories of risk and protective factors, including bullying and parental intimate partner violence, social and cultural factors, school and neighborhood environment and health and wellbeing. However, the review also identified a gap of qualitative research and a lack of attention to how ADA intersects with cultural factors, gender differences, criminalization, and poor mental health. Many of the studies report on school-based settings, limiting understanding of the role of neighborhood factors in prevention, protection and recovery. Participatory research on help-seeking behaviors of adolescents is rare. Conclusions: The review synthesized risk and protective factors associated with ADA, especially those occurring between younger adolescents. It highlighted the complex interplay and overlap between using and experiencing violence and abuse and the need for systematic research to inform the development of advocacy, interventions and prevention that is right for young people.

Much of the literature on domestic abuse focuses on those in intimate partner relationships or ex-partners, however, in the UK the Home Office definition also includes those in familial relationships. The Domestic Abuse, Stalking, and Harassment and Honour-Based Violence Risk Assessment assumes homogeneous risk factors across all relationships. This paper therefore examines the risk factors for repeat victimization of domestic abuse by relationship type between the victim and perpetrator in a UK police force. Using police-recorded domestic abuse incident and crime data, a logistic regression model found that the most similar repeat victimization risk profiles for 14,519 victims were amongst partners and ex-partners, with both relationships demonstrating the greatest degree of gender asymmetry, compared with other familial relationships. Physical violence was the strongest predictor of repeat victimization and was a statistically significant predictor for ex-partners, partners, and all familial relationships. Coercive behaviour was also a significant predictor for all relationships apart from partners, but not at the same magnitude as physical abuse. Recognizing the difference in risk by relationship type may assist the police in deciding the most appropriate response and interventions to reduce the risk of further harm.

This study explores the relationship between changes in neighbourhood characteristics and changes in violence rates across England and Wales from 2011 to 2021. Rising social disadvantage, particularly increased unemployment, poor health, lone-parent households, residential mobility and social housing, was significantly associated with rising violence. While overall ethnic heterogeneity was not linked to violence, increases in heterogeneity were more strongly associated with violence in areas where poor health and low qualifications also rose. Growth in transport infrastructure was linked to rising violence, suggesting that increased footfall may elevate risk. Findings highlight the need to address structural inequalities through investment in housing, health, education and community stability. Policy responses should extend beyond criminal justice to promote long-term violence reduction and community well-being.