Should We Subtype ADHD According to the Context in Which Symptoms Occur? Criterion Validity of Recognising Context-Based ADHD Presentations

Aja Murray, Denis Ribeaud, Manuel Eisner, George Murray, Karen McKenzie

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
36 Downloads (Pure)

Abstract

ADHD symptoms show considerable individual variation in the contexts in which they are expressed. It has previously been proposed that subtyping individuals according to the contexts in which symptoms are expressed may be clinically useful. We examined context-based patterns of ADHD symptoms in a longitudinal cohort study of n = 1388 children, as well as context-specific and context-general predictors of symptoms. Participants were community-ascertained and provided ADHD symptom data at ages 7, 9, and 11. Using growth mixture modelling we identified five inattention and five hyperactivity/impulsivity categories that differed in the developmental patterns of symptoms reported by parent and teacher informants. We found some evidence that context-specific predictors were related to context-specific expressions. Specifically, after controlling for other risk factors for ADHD symptoms, relationships with teachers predicted school-specific (teacher-reported) but not home-specific (parent-reported) symptom levels. However, no subtypes defined by exclusively home-based symptoms emerged, suggesting that while symptoms may sometimes be specific to the school context, they are only rarely confined to the home context. Subtyping by context could be informative; however, further work will required to uncover the nature of any etiological, functional, or outcome differences between those who show symptom expression in different contexts.
Original languageEnglish
Pages (from-to)308-320
Number of pages13
JournalChild Psychiatry and Human Development
Volume50
Issue number2
Early online date30 Aug 2018
DOIs
Publication statusPublished - 1 Apr 2019

Keywords

  • ADHD
  • Development
  • Informant discrepancies
  • Growth mixture modelling

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