Top

Journal of Autism and Developmental Disorders

Tip

Swipe om te navigeren naar een ander artikel

Gepubliceerd in:

13-05-2020 | Brief Report

Brief Report: Classifying Rates of Students with Autism and Intellectual Disability in North Carolina: Roles of Race and Economic Disadvantage

Auteurs: Jill Howard, J. Nathan Copeland, Elizabeth J. Gifford, Jennifer Lawson, Yu Bai, Nicole Heilbron, Gary Maslow

Gepubliceerd in: Journal of Autism and Developmental Disorders | Uitgave 1/2021

Abstract

We examined special education classifications among students aged 3–21 in North Carolina public schools, highlighting autism spectrum disorder (ASD) and intellectual disability (ID). Results revealed variability by county in ASD and ID prevalence, and in county-level ratios of ID vs. ASD classifications. Sociodemographic characteristics predicted proportion of ASD or ID within a county; correlations showed an association between race and ID, but not ASD. County’s median household income predicted proportion of students classified as ASD and ID (opposite directions), controlling for number of students and gender. Variability was unlikely related to biological incidence, and more likely related to district/school practices, or differences in resources. Disparities warrant further examination to ensure that North Carolina’s youth with disabilities access necessary, appropriate resources.

vorige artikel Anxiety and Depression Reduction as Distal Outcomes of a College Transition Readiness Program for Adults with Autism

volgende artikel Brief Report: Social Anxiety in Autism Spectrum Disorder is Based on Deficits in Social Competence

Alleen toegankelijk voor geautoriseerde gebruikers

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental Disorders (5th ed.). Washington, DC: Author. CrossRef

Autism and Developmental Disabilities Monitoring Network. (2018). Community report on autism 2018. Centers for Disease Control and Prevention.

Autism and Developmental Disabilities Monitoring Network Surveillance Year 2000 Principal Investigators. (2007). Prevalence of autism spectrum disorders—Autism and Developmental Disabilities Monitoring Network, Six Sites, United States, 2000. MMWR Surveillance Summary, 56(SS-1), 1–11.

Baio, J., Wiggins, L., Christensen, D. L., Maenner, M. J., Daniels, J., Warren, Z., et al. (2014). (2018) Prevalence of autism spectrum disorder among children aged 8 years—Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States. MMWR Surveillance Summary, 67(SS-6), 1–23. https://doi.org/10.15585/mmwr.ss6706a1. CrossRef

Bitterman, A., Daley, T. C., Misra, S., Carlson, E., & Markowitz, J. (2008). A national sample of preschoolers with autism spectrum disorders: Special education services and parent satisfaction. Journal of Autism and Developmental Disorders, 38(8), 1509–1517. https://doi.org/10.1007/s10803-007-0531-9. CrossRefPubMed

Coo, H., Ouellette-Kuntz, H., Lloyd, J. E. V., Kasmara, L., Holden, J. J. A., & Lewis, M. E. S. (2007). Trends in autism prevalence: Diagnostic substitution revisited. Journal of Autism and Developmental Disorders, 38(6), 1036–1046. https://doi.org/10.1007/s10803-007-0478-x. CrossRefPubMed

Donovan, M. S., & Cross, C. T. (Eds.). (2002). Minority students in special and gifted education. Washington, DC: National Academies Press.

Durkin, M. S., Maenner, M. J., Baio, J., Christensen, D., Daniels, J., Fitzgerald, R., et al. (2017). Autism spectrum disorder among US children (2002–2010): Socioeconomic, racial, and ethnic disparities. American Journal of Public Health, 107(11), 1818–1826. https://doi.org/10.2105/AJPH.2017.304032. CrossRefPubMedPubMedCentral

Franklin, M. S., Beyer, L., Maslow, G., Pollock, M., & Docherty, S. (2019). Health care transition for adolescent and young adults with intellectual disability: Views from the parents. Journal of Pediatric Nursing, 47, 148–158. https://doi.org/10.1016/j.pedn.2019.05.008. CrossRefPubMed

Harris, J. C. (2006). Intellectual disability: Understanding its development, causes, classification, evaluation, and treatment. New York: Oxford University Press.

King, M., & Bearman, P. (2009). Diagnostic change and the increased prevalence of autism. International Journal of Epidemiology, 38(5), 1224–1234. https://doi.org/10.1093/ije/dyp261. CrossRefPubMedPubMedCentral

King, M. D., & Bearman, P. S. (2011). Socioeconomic status and the increased prevalence of autism in California. American Sociological Review, 76(2), 320–346. https://doi.org/10.1177/0003122411399389. CrossRefPubMedPubMedCentral

Losen, D. J., & Gillespie, J. (2012). Opportunities suspended: The disparate impact of disciplinary exclusion from school. Los Angeles: The Civil Rights Project, Center for Civil Rights Remedies.

Mandell, D. S., Listerud, J., Levy, S. E., & Pinto-Martin, J. A. (2002). Race differences in the age at diagnosis among Medicaid-eligible children with autism. Journal of the American Academy of Child and Adolescent Psychiatry, 41(12), 1447–1453. https://doi.org/10.1097/00004583. CrossRefPubMed

Mandell, D. S., Wiggins, L. D., Carpenter, L. A., Daniels, J., DiGuiseppi, C., Durkin, M. S., et al. (2008). Racial/ethnic disparities in the identification of children with autism spectrum disorders. American Journal of Public Health, 99(3), 493–498. https://doi.org/10.2105/AJPH.2007.131243]. CrossRefPubMed

Maulik, P. K., Mascarenhas, M. N., Mathers, C. D., Dua, T., & Saxena, S. (2011). Prevalence of intellectual disability: A meta-analysis of population-based studies. Research in Developmental Disabilities, 32(2), 419–436. https://doi.org/10.1016/j.ridd.2010.12.018. CrossRefPubMed

Morgan, P. L., Farkas, G., Hillemeier, M. M., & Maczuga, S. (2017). Replicated evidence of racial and ethnic disparities in disability identification in U.S. schools. Educational Researcher, 46(6), 305–322. https://doi.org/10.3102/0013189X17726282. CrossRef

NC Division of Medical Assistance Medicaid and Health Choice North Carolina Innovations Clinical Coverage Policy No: 8-P Amended Date: November 1, 2016 (2016). Retrieved October 18, 2019, from https://files.nc.gov/ncdma/documents/files/8p.pdf.

Parrish, T. (2002). Racial disparities in the identification, funding, and provision of special education. In D. J. Losen & G. Orfield (Eds.), Racial inequity in special education (pp. 15–35). Cambridge, MA: Harvard Education Press.

Polyak, A., Kubina, R. M., & Girirajan, S. (2015). Comorbidity of intellectual disability confounds ascertainment of autism: Implications for genetic diagnosis. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 168(7), 600–608. https://doi.org/10.1002/ajmg.b.32338. CrossRef

Public Schools of North Carolina, State Board of Education, Department of Public Instruction, Exceptional Children Division. (2018). Policies Governing Services for Children with Disabilities, Amended March 2018.

Rubenstein, E., Daniels, J., Schieve, L. A., Christensen, D. L., Van Naarden Braun, K., Rice, C. E., et al. (2017). Trends in special education eligibility among children with autism spectrum disorder, 2002–2010. Public Health Reports, 133(1), 85–92. https://doi.org/10.1177/0033354917739582. CrossRefPubMedPubMedCentral

Schieve, L. A., Gonzalez, V., Boulet, S. L., Visser, S. N., Rice, C. E., Van Naarden Braun, K., et al. (2012). Concurrent medical conditions and health care use and needs among children with learning and behavioral developmental disabilities, National Health Interview Survey, 2006–2010. Research in Developmental Disabilities, 33(2), 467–476. https://doi.org/10.1016/j.ridd.2011.10.008. CrossRefPubMed

Shattuck, P. T. (2006). The contribution of diagnostic substitution to the growing administrative prevalence of autism in US special education. Pediatrics, 117(4), 1028–1037. https://doi.org/10.1542/peds.2005-1516. CrossRefPubMed

Skiba, R. J., Artiles, A. J., Kozleski, E. B., Losen, D. J., & Harry, E. G. (2016). Risks and consequences of oversimplifying educational inequities: A response to Morgan et al. (2015). Educational Researcher, 45(3), 221–225. https://doi.org/10.3102/0013189X16644606. CrossRef

Travers, J. C., Tincani, M., & Krezmien, M. P. (2013). A multiyear national profile of racial disparity in autism identification. The Journal of Special Education, 47(1), 41–49. CrossRef

U.S. Census Bureau. (2016). Median household income (in 2016 dollars), 2012–2016 American Community Survey 5-year estimates. https://www.census.gov/quickfacts/fact/table/nc/INC110216.

Xu, G., Strathearn, L., Liu, B., & Bao, W. (2018). Prevalence of autism spectrum disorder among US children and adolescents, 2014–2016. JAMA, 319(1), 81–82. https://doi.org/10.1001/jama.2017.17812. CrossRefPubMedPubMedCentral

Titel: Brief Report: Classifying Rates of Students with Autism and Intellectual Disability in North Carolina: Roles of Race and Economic Disadvantage
Auteurs: Jill Howard
J. Nathan Copeland
Elizabeth J. Gifford
Jennifer Lawson
Yu Bai
Nicole Heilbron
Gary Maslow
Publicatiedatum: 13-05-2020
Uitgeverij: Springer US
Gepubliceerd in: Journal of Autism and Developmental Disorders / Uitgave 1/2021
Print ISSN: 0162-3257
Elektronisch ISSN: 1573-3432
DOI: https://doi.org/10.1007/s10803-020-04527-y

Mijn bibliotheek

Shop

Tips voor Mijn BSL

Bohn Stafleu van Loghum

Tip

Swipe om te navigeren naar een ander artikel

Brief Report: Classifying Rates of Students with Autism and Intellectual Disability in North Carolina: Roles of Race and Economic Disadvantage

Abstract

Mijn bibliotheek

Shop

Tips voor Mijn BSL

Bohn Stafleu van Loghum

Tip

Swipe om te navigeren naar een ander artikel

Deel dit onderdeel of sectie (kopieer de link)

Abstract

Log in om toegang te krijgen

Andere artikelen Uitgave 1/2021

Vocational Rehabilitation Service Utilization and Employment Outcomes Among Secondary Students on the Autism Spectrum

Thinking Small to Think Big: Modular Approach for Autism Programming in Schools (MAAPS)

Do Implicit and Explicit Racial Biases Influence Autism Identification and Stigma? An Implicit Association Test Study

Identifying School-Based Factors that Predict Employment Outcomes for Transition-Age Youth with Autism Spectrum Disorder

Copy Number Variants and Polygenic Risk Scores Predict Need of Care in Autism and/or ADHD Families

IQ and Sensory Symptom Severity Best Predict Motor Ability in Children With and Without Autism Spectrum Disorder