Please use this identifier to cite or link to this item: https://doi.org/10.3390/ijerph19159195
Title: Classifying Young Children with Attention-Deficit/Hyperactivity Disorder Based on Child, Parent, and Family Characteristics: A Cross-Validation Study
Authors: Law, Evelyn 
Sideridis, Georgios
Alkhadim, Ghadah
Snyder, Jenna
Sheridan, Margaret
Keywords: Science & Technology
Life Sciences & Biomedicine
Environmental Sciences
Public, Environmental & Occupational Health
Environmental Sciences & Ecology
attention-deficit
hyperactivity disorder
SES
preschool
DEFICIT HYPERACTIVITY DISORDER
LATENT PROFILE ANALYSIS
TERM SCHOOL OUTCOMES
FOLLOW-UP
PSYCHIATRIC-DISORDERS
PRESCHOOL-CHILDREN
COMORBID SYMPTOMS
POPULATION-SAMPLE
WORKING-MEMORY
ADHD SUBTYPES
Issue Date: 1-Aug-2022
Publisher: MDPI
Citation: Law, Evelyn, Sideridis, Georgios, Alkhadim, Ghadah, Snyder, Jenna, Sheridan, Margaret (2022-08-01). Classifying Young Children with Attention-Deficit/Hyperactivity Disorder Based on Child, Parent, and Family Characteristics: A Cross-Validation Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 19 (15). ScholarBank@NUS Repository. https://doi.org/10.3390/ijerph19159195
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.
Source Title: INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
URI: https://scholarbank.nus.edu.sg/handle/10635/237360
ISSN: 1661-7827
1660-4601
DOI: 10.3390/ijerph19159195
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