Age of Autism Diagnosis in a Clinical Setting: Differences by Rural Vs. Urban Counties

Background: Early identification and accurate diagnosis of autism spectrum disorders is critical, since earlier exposure to intervention is associated with better outcomes. Unfortunately, access to specialists and early diagnosis can be limited for traditionally underserved populations (Liptak et al, 2008), and previous studies have shown later age of diagnosis associated with income level, geographic location (Mandell et al, 2005), and race/ethnicity (Valicenti-McDermott et al, 2012). The present study examines factors that may influence age of diagnosis in a clinical setting at the Marcus Autism Center; a center that serves a diverse geographic and socioeconomic population in the Atlanta metropolitan area. 

Objectives: The purpose of this study is to analyze the age of first diagnosis in a clinic setting and the extent to which this age is influenced by factors including race/ethnicity, gender, type of insurance (medicaid vs. private insurance) and geographic location (rural vs. urban).  Autism spectrum subtype was also examined as a predictor of age of first diagnosis. 

Methods: A record review was conducted of 343 diagnostic evaluations conducted between November 2010 and November 2011 at the Marcus Autism Center.  To be included in this study, the evaluation had to include the following components: Diagnostic interview, a developmental/cognitive measure (e.g. Bayley Scales, DAS-II), an adaptive measure (e.g. Vineland Scales), and the Autism Diagnostic Observation Scale (ADOS).  Information extracted from the records included child’s age, race/ethnicity, gender, county of residence, insurance information, prior diagnosis, and primary diagnosis given after receiving the comprehensive assessment. The child’s county was classified as rural or urban using rural-urban continuum codes developed by the United States Department of Agriculture (USDA). Of the 343 reports reviewed, 132 children received a first time diagnosis of autism spectrum disorder, and these were included in the following analyses. 

Results: The average age of autism diagnosis in this clinical setting was 63 months. However, use of one-way ANOVA showed that the age of diagnosis differed significantly based on ASD subtype; F(2, 129) = 9.01, p<.001 (Autism = 56 months; PDD NOS = 69 months; Asperger’s disorder = 89 months). There were no differences in age of diagnosis by race/ethnicity, gender, or insurance type (medicaid vs. private insurance/self pay). However, use of an independent samples t-test demonstrated that children who lived in rural counties had a significantly higher age of diagnosis of autistic disorder (70 months) compared to those children living in urban counties (52 months); t(82) = 2.58, p<.05.  The age of diagnosis for children with PDD NOS and Asperger’s disorder did not differ based on geographic location, as children in both categories had an older age of diagnosis in general. 

Conclusions: These findings show an average age of first autism diagnosis of 5.8 years among children in rural counties, compared to 4.3 years for children living in urban counties. This large difference highlights the importance of efforts to increase outreach and education for providers in rural counties, as well as efforts to improve access to information, specialists and diagnostic assessment for children living in these areas.