title: Using Clinical Data to Predict Accurate ADHD Diagnoses Among Urban Children.
abstract: Diagnosing attention deficit hyperactivity disorder (ADHD) requires reports of child behavior from 2 settings-most commonly home and school. Obtaining this information from teachers, however, is often challenging. We sought to determine if clinical data, supplementary to parent symptom scales, could be useful in predicting DSM-compliant diagnoses. Parents and teachers reported ADHD symptoms for 156 children using Vanderbilt scales; care managers collected clinical data; a team of specialists determined whether children met diagnostic criteria for ADHD. The ability of a parent Vanderbilt alone to predict an ADHD diagnosis was 56% (95% confidence interval = 45%, 67%). By adding child age and grade retention history to the multivariable model, the probability rose to 78% (95% confidence interval = 59%, 93%). In the maximally predictive model-which included 5 covariates-the predictive validity rose to 84% (95% confidence interval = 52%, 99%). Supplementing parent symptom reports with clinical data may be a viable alternative in certain cases when teacher reports are unavailable.
authors.affiliation: Boston University School of Medicine, Boston Medical Center, Boston, MA, USA Michael.silverstein@bmc.org.
authors.forename: Michael
authors.initials: M
authors.lastname: Silverstein
authors.affiliation: Boston University School of Medicine, Boston Medical Center, Boston, MA, USA.
authors.forename: L Kari
authors.initials: LK
authors.lastname: Hironaka
authors.affiliation: Boston University School of Medicine, Boston Medical Center, Boston, MA, USA Boston University School of Public Health, Boston, MA, USA.
authors.forename: Emily
authors.initials: E
authors.lastname: Feinberg
authors.affiliation: Boston University School of Medicine, Boston Medical Center, Boston, MA, USA.
authors.forename: Jenna
authors.initials: J
authors.lastname: Sandler
authors.affiliation: Boston University School of Medicine, Boston Medical Center, Boston, MA, USA.
authors.forename: Michelle
authors.initials: M
authors.lastname: Pellicer
authors.affiliation: Boston University School of Medicine, Boston Medical Center, Boston, MA, USA.
authors.forename: Ning
authors.initials: N
authors.lastname: Chen
authors.affiliation: Boston University School of Public Health, Boston, MA, USA.
authors.forename: Howard
authors.initials: H
authors.lastname: Cabral
mesh_terms: D001289:Attention Deficit Disorder with Hyperactivity / Q000175:diagnosis*; D002648:Child; D015331:Cohort Studies; D005178:Faculty; D005260:Female; D006801:Humans; D008297:Male; D010290:Parents; D011320:Primary Health Care / Q000706:statistics & numerical data*; D011594:Psychometrics; D015203:Reproducibility of Results; D014505:Urban Population / Q000706:statistics & numerical data*
keywords: ADHD; behavioral health; primary care pediatrics; urban health; vulnerable populations