25582
Impact of Household Income and Urbanicity on School Services
Objectives: (1) To determine how school type, school services, and suspension rates for children with ASD are associated with household income and urbanicity (as determined by surrounding population density). (2) To determine whether emotional, academic, and special needs of children with ASD are being met in the school system by household income and urbanicity.
Methods: Parent participants in the Interactive Autism Network (IAN)— a large, validated and verified, internet-mediated parent-report research registry—completed the School Service Questionnaire (SSQ) on their child(ren) with ASD. The SSQ asks questions regarding special services, school placement, suspension and drop-out rates, and satisfaction with school services. Data from the IAN registry regarding participants’ household income and urbanicity (using the 2013 NCHS Urban-Rural Classification Scheme for Counties) were also included in the analysis.
Results: Parents of 1774 children with ASD (79.9% male) completed an online survey on school services as part of a series of baseline questionnaires for the Interactive Autism Network (IAN). The data revealed that urbanicity (urban, suburban, small-medium metro, rural) was significantly related to use of certain special services such as behavior therapy (X²(3)=9.12, p=0.028), assistive technology (X²(3)=13.68, p=0.003), applied behavior analysis (X²(3)=14.34, p=0.002), and social skills training groups (X²(3)=13.37, p=0.004). Each of these services was more common in suburban areas compared to urban, small-medium metropolitan, and rural areas. Household income was also significantly related to the use of social skills training groups: families in the third and fourth income quartiles were more likely to use this service (X²(3)=9.85, p=0.02). School placement was significantly affected by urbanicity (X²(12)=39.34, p<0.001), with home school and public school being more common in rural settings, and private/non-public, specialized private, and specialized public being more common in urban settings (see table 1). In terms of income, 69.2% of children in all household income quartiles were in the public school system. However, there were significant differences by income quartile (X²(12)=55.12, p<0.001) as to where the remaining children attended (see table 2). Higher suspension rates were significantly associated with smaller urbanicity settings (X²(3)=13.286, p=0.004), but suspension rates did not significantly differ by household income. Lastly, parents’ satisfaction with school services meeting their child’s emotional, academic, and special needs significantly differed by household income, with higher income quartiles having generally higher rates of satisfaction. Parent satisfaction, however, did not differ by urbanicity.
Conclusions: There are discrepancies in the use of special services, school placement, suspension rates, and level of satisfaction with school services based on urbanicity and household income. These findings point to the need for better educational access for children with ASD who come from less-resourced settings.