Differences In Autism Spectrum Disorder Assessment and Intervention In Rural, Urban, and Suburban Areas

Background: Increasing evidence indicates the need for early identification of children with autism spectrum disorders (ASD), as early intervention leads to better outcomes.   However, although ASDs can be reliably diagnosed by age two (Lord et al., 2006), the median age of diagnosis in America ranges from 41 to 60 months (CDC, 2009).  Factors responsible for delay of diagnosis include living in an urban or rural area, fewer physicians nearby, higher IQ, and being female (Mandell et al., 2010; Shattuck et al., 2009).   

Research indicates that family location is an important predictor of age of diagnosis (Mandell et al., 2010).  There is a significant lag between parents’ first mention of developmental concerns and when they receive an evaluation, which may differ by geographic location.  However, more specific factors that contribute to differences in timely assessment and intervention based on geographic location are not known.  Research indicates the need for a best practice autism specific evaluation that includes a structured observation (ADOS) and interview (ADI-R; Ozonoff et al., 2005).  However, several children are diagnosed without a best practice evaluation.  Previous studies examining age of diagnosis have not distinguished between evaluation procedures, despite the call for best practice evaluations.  In addition, intervention services received may also differ by geographic location. 

Objectives: This study will explore the nature of ASD diagnosis and intervention among children living in urban, suburban, and rural communities in Alabama with a sample of children referred for a best practice autism specific evaluation.  Specifically, differences in age of first parental concern, time from first concern to completion of a best practice evaluation, age of diagnosis, number of previous evaluations, previous diagnoses received from other professionals, and previous intervention services will be compared between rural, suburban, and urban areas.

Methods: Children were evaluated with the ADI-R, ADOS, and a clinical interview.   Final diagnoses were determined by members of the interdisciplinary team based on available data and clinical impressions.  Children who received a diagnosis of an ASD were included in the current study.  Children’s home location was classified according to US Census guidelines (2002).

Results: Diagnostic evaluations have been completed with 59 children (mean age = 61 months).  Results indicated a significant trend between the child’s geographic location and age of diagnosis.  Children who lived in an urban area (1000 people/ sq. mile) were diagnosed with an ASD at an average age of 59 months, children who lived in a suburban area (500 people/sq. mile) were diagnosed at age 52 months, and children who lived in a rural area were diagnosed at age 68 months.  Additional analyses examining differences in ASD assessment and intervention will also be completed. 

Conclusions:  We anticipate completion of 100 additional autism specific evaluations.  Based on the observed trend, we expect significant results with additional participants.  Continued exploration of these factors has important implications for clinical practice and policy decisions.  Specifically, this knowledge can lead to targeted interventions and improved screening methods to increase the number of children who receive a best practice evaluation as early as possible.