A data mining and item response mixture modeling method to retrospectively measure Diagnostic and Statistical Manual of Mental Disorders-5 attention deficit hyperactivity disorder in the 1970 British Cohort Study.

Publisher: John Wiley & Sons Country of Publication: United States NLM ID: 9111433 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1557-0657 (Electronic) Linking ISSN: 10498931 NLM ISO Abbreviation: Int J Methods Psychiatr Res Subsets: MEDLINE

Publication: Hoboken, NJ : John Wiley & Sons
Original Publication: Chichester ; New York : Wiley, c1991-

Data Mining*/methods ; Diagnostic and Statistical Manual of Mental Disorders*; Attention Deficit Disorder with Hyperactivity/*diagnosis; Attention Deficit Disorder with Hyperactivity/psychology ; Child ; Female ; Humans ; Male ; Psychiatric Status Rating Scales ; Psychometrics ; Reproducibility of Results ; Retrospective Studies ; United Kingdom

Objective: To facilitate future outcome studies, we aimed to develop a robust and replicable method for estimating a categorical and dimensional measure of Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5) attention deficit hyperactivity disorder (ADHD) in the 1970 British Cohort Study (BCS70).
Method: Following a data mining framework, we mapped DSM-5 ADHD symptoms to age 10 BCS70 data (N = 11,426) and derived a 16-item scale (α = 0.85). Mapping was validated by an expert panel. A categorical subgroup was derived (n = 594, 5.2%), and a zero-inflated item response theory (IRT) mixture model fitted to estimate a dimensional measure.
Results: Subgroup composition was comparable with other ADHD samples. Relative risk ratios (ADHD/not ADHD) included boys = 1.38, unemployed fathers = 2.07, below average reading = 2.58, and depressed parent = 3.73. Our estimated measures correlated with two derived reference scales: Strengths and Difficulties Questionnaire hyperactivity (r = 0.74) and a Rutter/Conners-based scale (r = 0.81), supporting construct validity. IRT model items (symptoms) had moderate to high discrimination (0.90-2.81) and provided maximum information at average to moderate theta levels of ADHD (0.5-1.75).
Conclusion: We extended previous work to identify ADHD in BCS70, derived scales from existing data, modeled ADHD items with IRT, and adjusted for a zero-inflated distribution. Psychometric properties were promising, and this work will enable future studies of causal mechanisms in ADHD.
(© 2018 John Wiley & Sons, Ltd.)

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Keywords: ADHD; BCS70; IRT; data mining

Date Created: 20181108 Date Completed: 20200115 Latest Revision: 20210109

20221213

PMC6877163

10.1002/mpr.1753

30402897