Abstract
Objective: Estimates of the stability of a preschooler's diagnosis of Attention Deficit/Hyperactivity Disorder (ADHD) into early elementary school vary greatly. Identified factors associated with diagnostic instability provide little guidance about the likelihood a particular child will have ADHD in elementary school. This study examined an approach to predicting age 6 ADHD-any subtype (ADHD-any) from preschoolers’ demographics and ADHD symptoms. Method: Participants were 796 preschool children (Mage = 4.44; 51% boys; 54% White, non-Hispanic) recruited from primary pediatric care and school settings. Parents completed ADHD Rating Scales at child ages 4 and 5 years, and a structured diagnostic interview (DISC-YC) at ages 4 and 6. Classification tree analyses (CTAs) examined the predictive utility of demographic and symptom variables at ages 4 and 5 years for age 6 ADHD. Results: Over half (52.05%) of preschoolers meeting diagnostic criteria for ADHD-any at age 4 did not meet those criteria at age 6; more than half (52.05%) meeting criteria for ADHD-any at age 6 had not met those criteria at age 4. A CTA conducted at age 4 predicted age 6 ADHD-any diagnosis 65.82% better than chance; an age 5 CTA predicted age 6 ADHD-any 70.60% better than chance. At age 4, likelihood of age 6 ADHD-any diagnosis varied from <5% to >40% across CTA tree branches and from <5% to >78% at age 5. Conclusions: Parent-reported patterns of preschool-age symptoms may differentially predict ADHD-any at age 6. Psychoeducation regarding these patterns may aid in decision about pursuing multidisciplinary evaluations or initiating treatment.
Original language | English (US) |
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Pages (from-to) | 433-441 |
Number of pages | 9 |
Journal | Academic Pediatrics |
Volume | 24 |
Issue number | 3 |
DOIs | |
State | Published - Apr 2024 |
Funding
This study was supported by NIMH RO1 MH 066866, Principal Investigator, John V. Lavigne. The authors gratefully acknowledge the community practices of the Pediatric Practice Research Group and the Chicago Public Schools for their participation in this study. This study was supported by NIMH RO1 MH 066866, Principal Investigator, John V. Lavigne. The authors gratefully acknowledge the community practices of the Pediatric Practice Research Group and the Chicago Public Schools for their participation in this study.
Keywords
- attention deficit/hyperactivity disorder stability
- classification tree analysis
- machine learning
- precision mental health
- preschoolers
- psychoeducation
ASJC Scopus subject areas
- Pediatrics, Perinatology, and Child Health