County-Level Variation in the Prevalence of Medicaid-Enrolled Children with Autism Spectrum Disorders

Mandell, David S.; Morales, Knashawn H.; Xie, Ming; Polsky, Daniel; Stahmer, Aubyn C.; Marcus, Steven C.

doi:10.1007/s10803-010-0982-2

Cited by 24 publications

(16 citation statements)

References 17 publications

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“…[4–9] In the United States, there is considerable variability in the utilization of these classifications and, because their primary intended purposes are to ensure appropriate service provision to individuals rather than to classify disabilities, these systems do not uniformly identify all individuals that meet ASD criteria in the population. [10–12]…”

Section: Introductionmentioning

confidence: 99%

Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder

et al. 2016

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The Autism and Developmental Disabilities Monitoring (ADDM) Network conducts population-based surveillance of autism spectrum disorder (ASD) among 8-year old children in multiple US sites. To classify ASD, trained clinicians review developmental evaluations collected from multiple health and education sources to determine whether the child meets the ASD surveillance case criteria. The number of evaluations collected has dramatically increased since the year 2000, challenging the resources and timeliness of the surveillance system. We developed and evaluated a machine learning approach to classify case status in ADDM using words and phrases contained in children’s developmental evaluations. We trained a random forest classifier using data from the 2008 Georgia ADDM site which included 1,162 children with 5,396 evaluations (601 children met ADDM ASD criteria using standard ADDM methods). The classifier used the words and phrases from the evaluations to predict ASD case status. We evaluated its performance on the 2010 Georgia ADDM surveillance data (1,450 children with 9,811 evaluations; 754 children met ADDM ASD criteria). We also estimated ASD prevalence using predictions from the classification algorithm. Overall, the machine learning approach predicted ASD case statuses that were 86.5% concordant with the clinician-determined case statuses (84.0% sensitivity, 89.4% predictive value positive). The area under the resulting receiver-operating characteristic curve was 0.932. Algorithm-derived ASD “prevalence” was 1.46% compared to the published (clinician-determined) estimate of 1.55%. Using only the text contained in developmental evaluations, a machine learning algorithm was able to discriminate between children that do and do not meet ASD surveillance criteria at one surveillance site.

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Section: Introductionmentioning

confidence: 99%

Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder

et al. 2016

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“…Whether or not a child truly has an ASD based on gold standard research measures, such as the Autism Diagnostic Observation Schedule (ADOS) or Autism Diagnostic Interview-Revised (ADI-R), is a separate question that has been addressed in a different research study (Lee et al 2010 Mandell et al 2010) or parent-report (Kogan et al 2007). This study conducted a record review to firmly establish that children taking part in IAN Research have a professional diagnosis of ASD and to authenticate the identity of IAN Research participants.…”

Section: Introductionmentioning

confidence: 99%

Verification of Parent-Report of Child Autism Spectrum Disorder Diagnosis to a Web-Based Autism Registry

Daniels

Rosenberg

Anderson

et al. 2011

J Autism Dev Disord

238

174

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Growing interest in autism spectrum disorder (ASD) research requires increasingly large samples to uncover epidemiologic trends; such a large dataset is available in a national, web-based autism registry, the Interactive Autism Network (IAN). The objective of this study was to verify parent-report of professional ASD diagnosis to the registry's database via a medical record review on a sample of IAN Research participants. Sixty-one percent of families agreed to participate; 98% (n = 116) of whom provided documentation verifying a professionally diagnosed ASD. Results of this study suggest that information collected from parents participating in IAN Research is valid, participants can be authenticated, and that scientists can both confidently use IAN data and recruit participants for autism research.

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“…Given that recent estimates suggest that approximately 45 % of children diagnosed with ASD are enrolled in Medicaid (Mandell et al 2010a) genetic testing and genetic counseling practices in this system have important policy and practice implications. We had two specific aims within this study.…”

Section: Introductionmentioning

confidence: 99%

Genetic testing and genetic counseling among medicaid-enrolled children with autism spectrum disorder in 2001 and 2007

et al. 2013

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The rise in the prevalence of autism spectrum disorder (ASD) has resulted in increased efforts to understand the causes of this complex set of disorders that emerge early in childhood. Although research in this area is underway and yielding useful, but complex information about ASD, guidelines for the use of genetic testing and counseling among children with ASD conflict. The purpose of this study was to determine the frequency of use of genetic testing and counseling before the widespread implementation of clinical chromosomal microarray (CMA) to establish a baseline for the use of both services and to investigate potential disparities in the use of both services among children with ASD. We found that about two-thirds of children with ASD received genetic testing or counseling and the use of both services is increasing with time, even in the pre-CMA era. Being female and having a comorbid intellectual disability diagnosis both increased the likelihood of receiving genetic testing and genetic counseling. Initial discrepancies in the use of both services based on race/ethnicity suggest that troubling disparities observed in other services delivered to children with ASD and other mental health disorders persist in genetic testing and counseling as well. These results should incentivize further investigation of the impact of genetic testing and counseling on children with ASD and their families, and should drive efforts to explore and confront disparities in the delivery of these services, particularly with the advancing scientific research on this topic.

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County-Level Variation in the Prevalence of Medicaid-Enrolled Children with Autism Spectrum Disorders

Cited by 24 publications

References 17 publications

Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder

Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder

Verification of Parent-Report of Child Autism Spectrum Disorder Diagnosis to a Web-Based Autism Registry

Genetic testing and genetic counseling among medicaid-enrolled children with autism spectrum disorder in 2001 and 2007

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