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

2025-11-24

AI Summary

Conventional ACE scores (e.g., ≥4) exhibit limited predictive performance for individual depression risk. Method: We propose a pattern-based approach for identifying replicable risk subgroups, employing an internal data-rotation framework—integrating exploratory, validation, and replication phases within a single cohort. The method combines isotonic subgroup selection (ISS), frequency encoding, monotonicity-constrained modeling, and strict control of family-wise error rate to overcome the limitations of ACE sum-score aggregation. Contribution/Results: Our approach identifies reproducible, high-risk ACE combination patterns rather than relying on global thresholds. In external validation using the BRFSS dataset, it achieves a 26% relative increase in sensitivity over the conventional ACE threshold method at fixed specificity of 0.95. The resulting tool offers statistically robust, clinically implementable screening for depression risk stratification, enabling precision allocation of mental health resources.

Technology Category

• Machine Learning: Ensemble Methods
• Knowledge Representation and Reasoning: Preferences
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

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 $τ$ 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.

Problem

• Identifying replicable combinations of ACEs that increase adult depression risk
• Developing a data turnover framework to explore and validate ACE patterns
• Improving screening sensitivity for depression risk compared to ACE scores

Innovation

• Data turnover framework enables exploration and replication
• Isotonic subgroup selection identifies high-risk ACE combinations
• Frequency coding retains intensity information for accuracy