Machine learning for identifying caregiving adversities associated with greatest risk for mental health problems in...

Developmental and experiential heterogeneity associated with caregiving-related early adversities (crEAs) poses a major challenge to identifying replicable, generalizable findings. Here conditional random forests evaluated the importance of unique crEA experiences for estimating risks to mental health in 306 children, 6-12 years of age, with heterogeneous crEA experiences (different forms of caregiver-involved abuse and/or neglect or permanent/substantial parent-child separation). The better that crEAs improved the accuracy of symptom estimates in held-out, never-before-seen children, the more important and generalizable they were considered. Here we show that earlier timing and longer duration of crEAs was especially important for elevated general psychopathology (p-factor scores). The mere presence (versus absence) of crEAs was more valuable for estimating symptom risk than were specific adversities in a broad sample. Specific adversities became more important when only looking within the crEA-exposed subsample, with adversities of an interpersonal-affective nature being the most likely to increase transdiagnostic symptom risk. Concurrent consistent caregiving also had high importance, motivating consideration of later-occurring environmental experiences in future studies of early adversity.