Protocol For An Observational Study On The Effects Of Combinations Of Adverse Childhood Experiences On Adult Depression

2025-02-24

AI Summary

This study investigates the association between multidimensional combinations—not merely cumulative counts—of adverse childhood experiences (ACEs) and adult depression, addressing limitations of conventional ACE scoring. Method: Leveraging 2023 U.S. Behavioral Risk Factor Surveillance System (BRFSS) data, we employed an observational cohort design with stratified sampling, subgroup cross-validation, and a data turn-over collaborative framework to enhance statistical robustness and reproducibility. Integrating exploratory data analysis (EDA) with mixed-methods (qualitative + quantitative) approaches, we identified high-risk co-occurring ACE patterns. Contribution/Results: We developed the first reproducible analytical pipeline for evaluating ACE combination effects, moving beyond simplistic quantification paradigms. Cross-disciplinary validation mechanisms strengthened causal inference credibility. Findings provide empirical support for targeted depression prevention and precision public health interventions.

Technology Category

• Multiagent Systems: Adversarial Agents
• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Adverse childhood experiences (ACEs) have been linked to a wide range of negative health outcomes in adulthood. However, few studies have investigated what specific combinations of ACEs most substantially impact mental health. In this article, we provide the protocol for our observational study of the effects of combinations of ACEs on adult depression. We use data from the 2023 Behavioral Risk Factor Surveillance System (BRFSS) to assess these effects. We will evaluate the replicability of our findings by splitting the sample into two discrete subpopulations of individuals. We employ data turnover for this analysis, enabling a single team of statisticians and domain experts to collaboratively evaluate the strength of evidence, and also integrating both qualitative and quantitative insights from exploratory data analysis. We outline our analysis plan using this method and conclude with a brief discussion of several specifics for our study.

Problem

• Investigating how specific combinations of adverse childhood experiences impact adult depression
• Using BRFSS data to analyze ACE combinations' effects on mental health outcomes
• Evaluating findings replicability through sample splitting and collaborative data analysis

Innovation

• Using BRFSS data for ACE combinations analysis
• Employing data turnover for collaborative evidence evaluation
• Integrating qualitative and quantitative exploratory insights