Adverse Childhood Experiences (ACEs) and Environmental Exposures on Neurocognitive Outcomes in Children: Empirical Evidence, Potential Mechanisms, and Implications

Gladieux, Margaret; Gimness, Nathan; Rodriguez, Bianca; Liu, Jianghong

doi:10.3390/toxics11030259

Cited by 2 publications

(1 citation statement)

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“…In terms of predictors, sociodemographic (e.g., age, sex, gender) and lifestyle factors (e.g., physical activity, lack of sleep, and use of alcohol, tobacco, and other drugs) are predominantly used for modeling chronic health conditions ( 58 ). However, only a small number of studies include ACE exposure in ML models to predict rheumatic and musculoskeletal disease ( 66 ), neurocognitive outcomes ( 67 ), and emergency department visits ( 68 ). Although a study by Ammar and Shaban-Nejad ( 69 ) proposes a proof-of-concept semantic XAI model for using ACEs and SDoH data to improve mental health surveillance, the model’s accuracy and usability are yet to be evaluated.…”

Section: Introductionmentioning

confidence: 99%

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

Afzal,

Jahangir,

Mei

et al. 2024

Front. Public Health

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IntroductionDecades 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.MethodsUsing 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.ResultsWith 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.DiscussionOur 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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