Predictive Factors in Special Education Eligibility for Children with ASD

Background: As prevalence rates of autism spectrum disorders (ASD) in the United States reach 1 in 68 (1.5%), public schools are increasingly challenged with educating children with ASD through special education programs (Baio, 2014). Within the U.S., less than .5% of children ages 3-21 are eligible for special education services in the area of ASD (OSERS, 2014). Therefore, approximately 2 out of 3 children with a health diagnosis of ASD are not receiving special education services specific to their disability. This is consistent with findings that children with an ASD health diagnosis often do not have a ASD special education eligibility (Pinborough-Zimmerman et. al, 2012). Little is known about characteristics of children receiving special education versus those who do not. Related to health diagnoses of ASD, children with more severe symptoms have been identified as having younger age of diagnosis (Jo et. al, 2015; Mandell, 2005). In addition, identification rates of ASD are generally higher in white, non-Hispanic males (Baio, 2014; Pettygrove et al., 2013).

Objectives: This study examined the rate of special education eligibility for children diagnosed with ASD as well as factors that predict if a child diagnosed with an autism spectrum disorder receives special education services. It was predicted that children with more significant adaptive delays would receive special education services at higher rates. Higher levels of special education eligibility were also expected for white, non-Hispanic males.

Methods: Data from a clinical sample of 285 children ages 3-21 who were diagnosed with ASD at a regional autism center was used.  Data from record review including information regarding age(M = 4.8; SD =2.27), race (White N = 111, Black N = 117; Hispanic N = 35; Other N =9), gender (male N = 256; female N = 24), and adaptive behavior scores.  The overall adaptive composite scores from the Vineland-II and the ABAS-II were combined for use in the analyses (M = 69.0,SD =12.48).  Chi-square and binary logistic regression analyses were used to consider the effect of race, gender, and adaptive abilities on special education eligibility.

Results: Within the sample, 67% of children diagnosed with ASD received special education services. Gender was not predictive, but minority status trended towards significance, with minority children having higher rates of IEP placement than Caucasian children (X² (1, N =272) = 3.49, p =.06).  Children’s adaptive abilities were predictive of special education eligibility (Wald = 5.27, p<.05)

Conclusions:   Consistent with previous studies, many children diagnosed with ASD did not receive special education services.  Gender was not predictive of special education eligibility, though there was a trend towards minority children being more likely to have special education eligibility than Caucasian children. This trend is interesting given that previous studies have found a later age of diagnosis for children from diverse racial backgrounds. Higher rates of special education eligibility among minority children may reflect the extent to which children have more universal access to special education services than clinical diagnostic services. Results also indicate that children with more significant impairment receive special education services.