Benjamin Zablotsky1, Matthew D Bramlett1, Susanna N Visser2, Melissa L Danielson2, Stephen J Blumberg1. 1. National Center for Health Statistics, Centers for Disease Control and Prevention as the overseeing organization, Hyattsville, MD. 2. National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention as the overseeing organization, Atlanta, GA.
Abstract
OBJECTIVE: Many children diagnosed with attention-deficit/hyperactivity disorder (ADHD) experience co-occurring neurodevelopmental and psychiatric disorders, and those who do often exhibit higher levels of impairment than children with ADHD alone. This study provides a latent class analysis (LCA) approach to categorizing children with ADHD into comorbidity groups, evaluating condition expression and treatment patterns in each group. METHODS: Parent-reported data from a large probability-based national sample of children diagnosed with ADHD (2014 National Survey of the Diagnosis and Treatment of ADHD and Tourette Syndrome) were used for an LCA to identify groups of children with similar groupings of neurodevelopmental and psychiatric comorbidities among children with current ADHD (n = 2495). Differences between classes were compared using multivariate logistic regressions. RESULTS: LCA placed children who were indicated to have ADHD into 4 classes: (low comorbidity [LCM] [64.5%], predominantly developmental disorders [PDD] [13.7%], predominantly internalizing disorders [PID] [18.5%], and high comorbidity [HCM] [3.3%]). Children belonging to the HCM class were most likely to have a combined ADHD subtype and the highest number of impaired domains. Children belonging to the PDD class were most likely to be receiving school services, whereas children in the PID class were more likely to be taking medication than those belonging to the LCM class who were least likely to receive psychosocial treatments. CONCLUSION: Latent classes based on co-occurring psychiatric conditions predicted use of varied treatments. These findings contribute to the characterization of the ADHD phenotype and may help clinicians identify how services could be best organized and coordinated in treating ADHD.
OBJECTIVE: Many children diagnosed with attention-deficit/hyperactivity disorder (ADHD) experience co-occurring neurodevelopmental and psychiatric disorders, and those who do often exhibit higher levels of impairment than children with ADHD alone. This study provides a latent class analysis (LCA) approach to categorizing children with ADHD into comorbidity groups, evaluating condition expression and treatment patterns in each group. METHODS: Parent-reported data from a large probability-based national sample of children diagnosed with ADHD (2014 National Survey of the Diagnosis and Treatment of ADHD and Tourette Syndrome) were used for an LCA to identify groups of children with similar groupings of neurodevelopmental and psychiatric comorbidities among children with current ADHD (n = 2495). Differences between classes were compared using multivariate logistic regressions. RESULTS: LCA placed children who were indicated to have ADHD into 4 classes: (low comorbidity [LCM] [64.5%], predominantly developmental disorders [PDD] [13.7%], predominantly internalizing disorders [PID] [18.5%], and high comorbidity [HCM] [3.3%]). Children belonging to the HCM class were most likely to have a combined ADHD subtype and the highest number of impaired domains. Children belonging to the PDD class were most likely to be receiving school services, whereas children in the PID class were more likely to be taking medication than those belonging to the LCM class who were least likely to receive psychosocial treatments. CONCLUSION: Latent classes based on co-occurring psychiatric conditions predicted use of varied treatments. These findings contribute to the characterization of the ADHD phenotype and may help clinicians identify how services could be best organized and coordinated in treating ADHD.
Authors: Mark Wolraich; Lawrence Brown; Ronald T Brown; George DuPaul; Marian Earls; Heidi M Feldman; Theodore G Ganiats; Beth Kaplanek; Bruce Meyer; James Perrin; Karen Pierce; Michael Reiff; Martin T Stein; Susanna Visser Journal: Pediatrics Date: 2011-10-16 Impact factor: 7.124
Authors: Timothy E Wilens; Joseph Biederman; Sarah Brown; Sarah Tanguay; Michael C Monuteaux; Christie Blake; Thomas J Spencer Journal: J Am Acad Child Adolesc Psychiatry Date: 2002-03 Impact factor: 8.829
Authors: Susanna N Visser; Benjamin Zablotsky; Joseph R Holbrook; Melissa L Danielson; Rebecca H Bitsko Journal: Natl Health Stat Report Date: 2015-09-03
Authors: Mahim Jain; Luis Guillermo Palacio; F Xavier Castellanos; Juan David Palacio; David Pineda; Maria I Restrepo; Juan F Muñoz; Francisco Lopera; Deeann Wallis; Kate Berg; Joan E Bailey-Wilson; Mauricio Arcos-Burgos; Maximilian Muenke Journal: Biol Psychiatry Date: 2006-09-01 Impact factor: 13.382
Authors: Irene Tung; James J Li; Jocelyn I Meza; Kristen L Jezior; Jessica S V Kianmahd; Patrick G Hentschel; Paul M O'Neil; Steve S Lee Journal: Pediatrics Date: 2016-09-21 Impact factor: 7.124
Authors: Steven P Cuffe; Susanna N Visser; Joseph R Holbrook; Melissa L Danielson; Lorie L Geryk; Mark L Wolraich; Robert E McKeown Journal: J Atten Disord Date: 2015-11-25 Impact factor: 3.256
Authors: Susan E Sprich; Steven A Safren; Daniel Finkelstein; Jocelyn E Remmert; Paul Hammerness Journal: J Child Psychol Psychiatry Date: 2016-03-17 Impact factor: 8.982
Authors: José J Bauermeister; Patrick E Shrout; Rafael Ramírez; Milagros Bravo; Margarita Alegría; Alfonso Martínez-Taboas; Ligia Chávez; Maritza Rubio-Stipec; Pedro García; Julio C Ribera; Glorisa Canino Journal: J Abnorm Child Psychol Date: 2007-12