Beyond the ACE Score: Replicable Combinations of Adverse Childhood Experiences That Worsen Depression Risk

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 $\ge 4$ 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 $\tau$ 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 control, as demonstrated in simulations. Guided by EDA, we adopt frequency coding for ACE items, retaining intensity information that reduces false positives relative to binary or score codings. The result is a replicable, pattern-based higher-risk subgroup. On held-out BRFSS 2022, we show that, at the same level of specificity (0.95), using our replicable subgroup as the screening rule increases sensitivity by 26\% compared with an ACE-score cutoff, yielding concrete triggers that are straightforward to implement and help target scarce clinical screening resources toward truly higher-risk profiles.