Presented by: Michael Jones Carrie Gravely

1. County-Level Variation in the Prevalence of Medicaid-Enrolled Children with autism Spectrum Disorders Written by: David S. Mandell, Knashawn H. Morales, Ming Xie, Daniel Polsky, Aubyn Stahmer and Steven C. Marcus Presented by: Michael Jones Carrie Gravely

2. Summary of Article • Resources used to identify students with ASD in the Medicaid system • How localities’ implementation of Medicaid policies differ • Medicaid is the focus because many of the services needed for children with ASD require only medical services and therefore do not qualify for funding under IDEA • Localities have different identification and treatment policies through Medicaid • Findings suggest identification policies should be improved for ASD

3. Research Methods • 3 data sources • 2004 Medicaid Analytic eXtract (MAX) database • National Center for Education Statistics’ Common Core of Data – revenue and number of children in SPED. • 2004 Resource File – county wide demographic and healthcare resource • Only used children 9 months or older in 2004 • County level data for staffing

4. Variables to the Research • No. of Medicaid enrolled children with ASD • County characteristics • County healthcare resources • County demographic characteristics

5. Results of the Research • Counties with greater per student expenditures had lower identified Medicaid ASD children • Counties with more students identified as needing SPED services had higher ASD Medicaid identified children • Counties with high per capita of pediatricians had greater Medicaid ASD • Counties with higher white population had higher identification of Medicaid ASD

6. Conclusion • Counties differ in how they use the Medicaid policies. • Need outreach to minority groups and doctors to diagnose ASD • Could be geographically or ethnically targeted • States vary in their approach to Medicaid to address needs of children with ASD • May cause counties to not use Medicaid • Important to understand Medicaid policies at the local level for identification

7. Perception of the Rigor of Research • The article was written in 2010 but the data source was from 2004. • Follow up studies in succeeding years would have substantiated the findings from 2004

8. Type of Policy • Regulatory • According to Fowler (2009) regulatory policies are formalized rules expressed in general terms and applied to large groups of people (p. 241). The Medicaid Policy is third largest form of health insurance in the United States. It is then managed by States and then localities. • Strategies for promoting support • Localities need to understand the policy • Medicaid and IDEA are separate funding sources to provide services to children.

9. Benefits of Policy • Money for Schools • Students with Autism comprise the Second Largest paying Medicaid Disability Category for Schools • Including Medicaid Autism can bring in much potential funding • Specially Tailored Instruction for students with Autism from Specially Trained Teachers • Schools should carefully author policies to ensure equal levels of service are provided to students with equivalent levels of impairment

10. Unintended Consequences • Reverse Discrimination • A higher percentage of white students is associated with a higher percentage of services for autism • Diagnostic criteria should be applied fairly • Disproportionality • Some disabilities occur more often in some groups than in others • Subgroups should be ignored and diagnostic criteria should be used similarly with all students

11. Lessons Learned • The Importance of Fairly implementing Identification Policies • Labels Should be Applied Fairly • Placement Guidelines Should be Closely Followed • Some discrepancies between subgroups may naturally occur.

12. References Fowler, F. C. (2009). Policy studies for educational leaders. Boston: Allyn & Bacon. Mandell, D. S., Morales, K. H., Xie, M., Polsky, D., Stahmer, A, & Marcus, S. C. (2010). County-level variation in the prevalence of medicaid-enrolled children with autism spectrum disorders. Journal of Autism and Developmental Disorders, 40(10), 1241-1246. doi: 10.1007/s10803-010-0982-2