Publication

Can adverse childhood experiences predict chronic health conditions? Development of trauma-informed, explainable machine learning models

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Last modified
  • 06/25/2025
Type of Material
Authors
    Hanin B. Afzal, University of TorontoTasfia Jahangir, Emory UniversityYiyang Mei, Emory UniversityAnnabelle Madden, Columbia UniversityAbeed Sarker, Emory UniversitySangmi Kim, Emory University
Language
  • English
Date
  • 2023
Publisher
  • Frontiers
Publication Version
Copyright Statement
  • © 2024 Afzal, Jahangir, Mei, Madden, Sarker and Kim.
License
Final Published Version (URL)
Title of Journal or Parent Work
Volume
  • 11
Start Page
  • 1309490
Grant/Funding Information
  • The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the National Institute of Nursing Research of the National Institutes of Health under Award Number K01NR019651.
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Abstract
  • Introduction Decades of research have established the association between adverse childhood experiences (ACEs) and adult onset of chronic diseases, influenced by health behaviors and social determinants of health (SDoH). Machine Learning (ML) is a powerful tool for computing these complex associations and accurately predicting chronic health conditions. Methods Using the 2021 Behavioral Risk Factor Surveillance Survey, we developed several ML models—random forest, logistic regression, support vector machine, Naïve Bayes, and K-Nearest Neighbor—over data from a sample of 52,268 respondents. We predicted 13 chronic health conditions based on ACE history, health behaviors, SDoH, and demographics. We further assessed each variable’s importance in outcome prediction for model interpretability. We evaluated model performance via the Area Under the Curve (AUC) score. Results With the inclusion of data on ACEs, our models outperformed or demonstrated similar accuracies to existing models in the literature that used SDoH to predict health outcomes. The most accurate models predicted diabetes, pulmonary diseases, and heart attacks. The random forest model was the most effective for diabetes (AUC = 0.784) and heart attacks (AUC = 0.732), and the logistic regression model most accurately predicted pulmonary diseases (AUC = 0.753). The strongest predictors across models were age, ever monitored blood sugar or blood pressure, count of the monitoring behaviors for blood sugar or blood pressure, BMI, time of last cholesterol check, employment status, income, count of vaccines received, health insurance status, and total ACEs. A cumulative measure of ACEs was a stronger predictor than individual ACEs. Discussion Our models can provide an interpretable, trauma-informed framework to identify and intervene with at-risk individuals early to prevent chronic health conditions and address their inequalities in the U.S.
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Keywords
Research Categories
  • Sociology, Public and Social Welfare
  • Psychology, Developmental
  • Psychology, Behavioral

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