Beyond the ACE Score: Replicable Combinations of Adverse Childhood Experiences That Worsen Depression Risk
Ruizhe Zhang, Jooyoung Kong, Dylan S. Small, William Bekerman

TL;DR
This paper develops a new method to identify specific combinations of adverse childhood experiences that more accurately predict adult depression risk than traditional ACE scores, enabling better targeted screening.
Contribution
It introduces a data turnover framework with isotonic subgroup selection to find replicable, higher-risk ACE combinations while controlling error rates, improving depression risk prediction.
Findings
Replicable ACE combinations identified that predict depression more effectively.
Using these combinations increases screening sensitivity by 26% at high specificity.
Method demonstrated successfully on BRFSS 2022 data.
Abstract
Adverse childhood experiences (ACEs) are categories of childhood abuse, neglect, and household dysfunction. Screening by a single additive ACE score (e.g., a cutoff) has poor individual-level discrimination. We instead identify replicable combinations of ACEs that elevate adult depression risk. Our data turnover framework enables a single research team to explore, confirm, and replicate within one observational dataset while controlling the family-wise error rate. We integrate isotonic subgroup selection (ISS) to estimate a higher-risk subgroup under a monotonicity assumption -- additional ACE exposure or higher intensity cannot reduce depression risk. We pre-specify a risk threshold corresponding to roughly a two-fold increase in the odds of depression relative to the no-ACE baseline. Within data turnover, the prespecified component improves power while maintaining FWER…
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Taxonomy
TopicsChild Abuse and Trauma · Child Abuse and Related Trauma · Child and Adolescent Psychosocial and Emotional Development
