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Classifying Young Children with Attention-Deficit/Hyperactivity Disorder Based on Child, Parent, and Family Characteristics: A Cross-Validation Study

Author

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  • Evelyn Law

    (Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
    Department of Paediatrics, Khoo Teck Puat-National University Children’s Medical Institute, National University Health System, Singapore 119228, Singapore
    Singapore Institute of Clinical Sciences, Agency of Science, Technology and Research, Singapore 117609, Singapore)

  • Georgios Sideridis

    (ICCTR, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
    Department of Primary Education, National and Kapodistrian University of Athens, 157 72 Athens, Greece)

  • Ghadah Alkhadim

    (Department of Psychology, College of Arts, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Jenna Snyder

    (Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC 27599, USA)

  • Margaret Sheridan

    (Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC 27599, USA)

Abstract

We aimed to identify subgroups of young children with differential risks for ADHD, and cross-validate these subgroups with an independent sample of children. All children in Study 1 (N = 120) underwent psychological assessments and were diagnosed with ADHD before age 7. Latent class analysis (LCA) classified children into risk subgroups. Study 2 (N = 168) included an independent sample of children under age 7. A predictive model from Study 1 was applied to Study 2. The latent class analyses in Study 1 indicated preference of a 3-class solution (BIC = 3807.70, p < 0.001). Maternal education, income-to-needs ratio, and family history of psychopathology, defined class membership more strongly than child factors. An almost identical LCA structure from Study 1 was replicated in Study 2 (BIC = 5108.01, p < 0.001). Indices of sensitivity (0.913, 95% C.I. 0.814–0.964) and specificity (0.788, 95% C.I. 0.692–0.861) were high across studies. It is concluded that the classifications represent valid combinations of child, parent, and family characteristics that are predictive of ADHD in young children.

Suggested Citation

  • Evelyn Law & Georgios Sideridis & Ghadah Alkhadim & Jenna Snyder & Margaret Sheridan, 2022. "Classifying Young Children with Attention-Deficit/Hyperactivity Disorder Based on Child, Parent, and Family Characteristics: A Cross-Validation Study," IJERPH, MDPI, vol. 19(15), pages 1-13, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:9195-:d:873284
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    References listed on IDEAS

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    1. Abigail Emma Russell & Tamsin Ford & Ginny Russell, 2015. "Socioeconomic Associations with ADHD: Findings from a Mediation Analysis," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-16, June.
    2. Margaret A Sheridan & Khaled Sarsour & Douglas Jutte & Mark D'Esposito & W Thomas Boyce, 2012. "The Impact of Social Disparity on Prefrontal Function in Childhood," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-13, April.
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