Cross-Validation and Development of Empirically Derived ADHD Assessment Strategies: Insights From the National Longitudinal Study of Adolescent Health (Add Health)

Abstract

Objective: There is replicated evidence that individual ADHD symptoms differentially predict ADHD diagnostic status, and that non–Diagnostic and Statistical Manual of Mental Disorders (DSM)-based symptom algorithms are superior to DSM criteria for ruling in or ruling out ADHD. However, these findings have been limited to case-control samples, despite the need to replicate them in independent and more representative samples and to increase their generalizability. Method: We analyzed the base rates and psychometric properties of ADHD symptoms in the population-based National Longitudinal Study of Adolescent Health (Add Health; http://www.cpc.unc.edu/projects/addhealth) sample, and evaluated the predictive utility of empirically derived ADHD symptoms against the DSM approach with respect to academic, health, and relational functional outcomes. The sample consisted of 11,247 (54% female) ethnically diverse (45% non-White) individuals who retrospectively self-reported the severity of their ADHD symptoms from 5 to 12 years. Results: Individual ADHD symptoms variably predicted ADHD status, and whereas most symptoms were most predictive when endorsed at the two highest severity levels in previous studies, symptoms in Add Health were often most predictive of ADHD when endorsed at the highest severity level. Conclusion: The divergence in optimal severity thresholds for ADHD symptoms potentially reflects the different symptom base rates in clinical versus population-based samples. The accuracy of the different classification approaches also suggested that different symptom algorithms may be superior to evaluate ADHD depending on the assessment setting.