International Meeting for Autism Research: Autism Spectrum Disorders & Comorbid Disorders: Findings From New Jersey Autism Study

Autism Spectrum Disorders & Comorbid Disorders: Findings From New Jersey Autism Study

Friday, May 13, 2011: 3:00 PM
Elizabeth Ballroom D (Manchester Grand Hyatt)
1:15 PM
J. Shenouda1, P. Khandge2, H. Patel2, N. Scotto-Rosato3, S. Howell3 and W. Zahorodny4, (1)Pediatrics, New Jersey Medical School, Newark, NJ, (2)Pediatrics, UMDNJ, Newark, NJ, (3)NJ State Health Department, Trenton, NJ, (4)New Jersey Medical School, Newark
Background: Children with Autism Spectrum Disorder (ASD) often display behaviors that are not related to the core features of ASD; however, some of these behaviors are often consistent with other disorders, and some of these children may be affected by other co-occurring disorders. Population-based studies are needed to appreciate the occurrence and expression of ASD and comorbid disorders and their effects on each other.

Objectives: This study investigated the number, proportion and demographic distribution of children with ASD with a comorbid disorder. The study also investigated the association between the presence of comorbid disorders and the average age of ASD diagnosis.

Methods: Data were collected as part of the New Jersey Autism Study (NJAS), a population-based ASD surveillance investigation carried out in Essex, Union, Hudson and Ocean Counties.  ASD ascertainment was by an active, retrospective, multiple-source, case-finding method, developed by the Centers for Disease Control and Prevention (CDC), based on review and analysis of information contained in health and education records.   Demographic variables and case-specific data including previous diagnosis were analyzed. The socioeconomic status (SES) of ASD cases was represented by the District Factor Group (DFG) ranking, a community-level index.   A number of mean difference tests were performed to gauge whether the presence and type of comorbid diagnosis; SES, and other demographic factors were significantly associated with the age of ASD diagnosis. 

Results: 533, 8 year old, children were identified with ASD from a population of over 30,000 children. 248 children with ASD (46.5%) had one or more comorbid disorders. The most frequently diagnosed comorbid disorder was ADHD, accounting for 208 ASD children (39%). 85 children with ASD (15.9%) had other psychiatric disorders, including mood disorders, oppositional defiant disorder, and anxiety disorders. 41 ASD children (7.7%) had other medical disorders including genetic disorders, epilepsy/seizure disorders, hearing and/or visual impairments, and cerebral palsy. Overall, the distribution of comorbid disorders and average age of ASD diagnosis did not significantly vary by race, gender, or SES. However, it was found that ASD children without a comorbid disorder were identified earlier with ASD (50.6 months) than ASD children with any comorbid disorder present, (55.8 months) (t=2.24, p<.05). We also found that ASD children with ADHD were identified with ASD at a later age (57.7 months) compared to ASD children without ADHD (50.2 months) (t=3.15, p<.001).

Conclusions: There has been an overwhelming increase in ASD prevalence over the past decade.  Many studies support early identification of ASD and timely intervention. Based on the results of this study, it was observed that many children with ASD are also afflicted with other disorders, and the presence of a comorbid disorder, specifically ADHD, is associated with delayed identification of ASD. This suggests that the presence of a comorbid disorder may mask the presentation of ASD, leading to a delayed identification of ASD as well as a delayed intervention to ASD.

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