Abstract
BackgroundExtensive literature has shown an association of Adverse Childhood Experiences (ACEs) with adverse health outcomes; however, its ability to predict events or stratify risks is less known. Individuals with mental illness and ACE exposure have been shown to visit emergency departments (ED) more often than those in the general population. This study thus examined the ability of the ACEs checklist to predict ED visits within the subsequent year among children and adolescents presenting to mental health clinics with pre-existing mental health issues.MethodsThe study analyzed linked data (n = 6100) from two databases provided by Alberta Health Services (AHS). The Regional Access and Intake System (RAIS 2016–2018) database provided data on the predictors (ACE items, age, sex, residence, mental health program type, and primary diagnosis) regarding children and adolescents (aged 0–17 years) accessing addiction and mental health services within Calgary Zone, and the National Ambulatory Care Reporting System (NACRS 2016–2019) database provided data on ED visits. A 25% random sample of the data was reserved for validation purposes. Two Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression models, each employing a different method to tune the shrinkage parameter lambda (namely cross-validated and adaptive) and performing 10-fold cross-validation for a set of 100 lambdas in each model were examined.ResultsThe adaptive LASSO model had a slightly better fit in the validation dataset than the cross-validated model; however, it still demonstrated poor discrimination (AUC 0.60, sensitivity 37.8%, PPV 49.6%) and poor calibration (over-triaged in low-risk and under-triaged in high-risk subgroups). The model’s poor performance was evident from an out-of-sample deviance ratio of − 0.044.ConclusionThe ACEs checklist did not perform well in predicting ED visits among children and adolescents with existing mental health concerns. The diverse causes of ED visits may have hindered accurate predictions, requiring more advanced statistical procedures. Future studies exploring other machine learning approaches and including a more extensive set of childhood adversities and other important predictors may produce better predictions. Furthermore, despite highly significant associations being observed, ACEs may not be deterministic in predicting health-related events at the individual level, such as general ED use.
Highlights
Extensive literature has shown an association of Adverse Childhood Experiences (ACEs) with adverse health outcomes; its ability to predict events or stratify risks is less known
Despite highly significant associations being observed, ACEs may not be deterministic in predicting health-related events at the individual level, such as general emergency departments (ED) use
With the assumption that ACE items and the clinical and demographic information might be better predictive of cause-specific ED visits, we explored the predictive performance for mental health issues-specific ED visits among children and adolescents
Summary
Extensive literature has shown an association of Adverse Childhood Experiences (ACEs) with adverse health outcomes; its ability to predict events or stratify risks is less known. With various studies reporting the prevalence of at least one ACE to be over 50% [1, 10], ACEs are commonplace These adversities are associated with poor health and well-being throughout life, the adverse outcomes ranging from smoking, alcoholism, drug abuse, suicide attempts, chronic diseases, mental illness, and even mortality [1, 3,4,5, 11]. Studies have reported that as many as 25–30% of the ED visitors have been noted to have a mental illness [19, 20] Both ACE exposure and mental illnesses can contribute to overcrowding in emergency departments and place administrative and economic burdens on the health system [11, 19,20,21]
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