Abstract

ObjectiveThe behavioral and emotional profiles underlying adolescent self-harm, and its developmental risk factors, are relatively unknown. We aimed to identify subgroups of young people who self-harm (YPSH) and longitudinal risk factors leading to self-harm.MethodParticipants were from the Millennium Cohort Study (N = 10,827). A clustering algorithm was used to identify subgroups who self-harmed with different behavioral and emotional profiles at age 14 years. We then traced the profiles back in time (ages 5−14 years) and used feature selection analyses to identify concurrent correlates and longitudinal risk factors of self-harming behavior.ResultsThere were 2 distinct subgroups at age 14 years: a smaller group (n = 379) who reported a long history of psychopathology, and a second, much larger group (n = 905) without. Notably, both groups could be predicted almost a decade before the reported self-harm. They were similarly characterized by sleep problems and low self-esteem, but there was developmental differentiation. From an early age, the first group had poorer emotion regulation, were bullied, and their caregivers faced emotional challenges. The second group showed less consistency in early childhood, but later reported more willingness to take risks and less security with peers/family.ConclusionOur results uncover 2 distinct pathways to self-harm: a “psychopathology” pathway, associated with early and persistent emotional difficulties and bullying; and an “adolescent risky behavior” pathway, whereby risk taking and external challenges emerge later into adolescence and are associated with self-harm. At least one of these pathways has a long developmental history, providing an extended window for interventions as well as potential improvements in the identification of children at risk, biopsychosocial causes, and treatment or prevention of self-harm.

Highlights

  • Our finding of 2 distinct subgroups of young people who self-harm (YPSH), based on emotional, behavioral, and mental health measures, in a nationally representative cohort further supports the notion that YPSH is not one homogenous group

  • This is especially important, as most self-harm models are based upon clinical samples.[36]

  • The expected profile of YPSH based on these models would be individuals with depressive symptoms, low self-esteem, interpersonal/familial challenges, early adversity, and environmental stressors5,36—risk factors that are most commonly assessed in empirical studies on self-harm

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Summary

Introduction

As self-harm risk is a nuanced phenomenon with multiple types of risk factors that could vary according to emotional and behavioral profiles, data-driven approaches— machine learning algorithms—are ideal for assessing complex relationships among a large number of possible risk factors in a replicable manner.[7] For this study, a large number (75À97 per sweep) of potential risk factors or concurrent correlates were selected from the MCS dataset on the basis of previous literature (Supplement 1, available online) and more general developmental factors that are associated with a host of different behavioral and mental health outcomes.[25] We grouped these variables according to 6 domains that have been broadly discussed across prior self-harm research and theoretical models: child health (eg, sleep, alcohol consumption)[18]; child mental health (eg, emotional issues, self-esteem)[5,9]; caregiver mental health (eg, health limitations)[9,14]; home environment (eg, housing tenure, neighborhood safety)[26,27]; peer relations (eg, quality of friendships)[10,11]; and adversity (eg, bullying).[10]

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