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Journal of Autism and Developmental Disorders

Gepubliceerd in:

28-09-2021 | Original Paper

Heterogeneity in Autism Spectrum Disorder Case-Finding Algorithms in United States Health Administrative Database Analyses

Auteurs: Scott D. Grosse, Phyllis Nichols, Kwame Nyarko, Matthew Maenner, Melissa L. Danielson, Lindsay Shea

Gepubliceerd in: Journal of Autism and Developmental Disorders | Uitgave 9/2022

Abstract

Strengthening systems of care to meet the needs of individuals with autism spectrum disorder (ASD) is of growing importance. Administrative data provide advantages for research and planning purposes, including large sample sizes and the ability to identify enrollment in insurance coverage and service utilization of individuals with ASD. Researchers have employed varying strategies to identify individuals with ASD in administrative data. Differences in these strategies can limit the comparability of results across studies. This review describes implications of the varying strategies that have been employed to identify individuals with ASD in US claims databases, with consideration of the strengths and limitations of each approach.

vorige artikel Using Two Formats of a Social Story to Increase the Verbal Initiations and On-Topic Responses of Two Adolescents with Autism Spectrum Disorders

volgende artikel Brief Report: Suspected Cannabis-Induced Mania and Psychosis in Young Adult Males with Autism Spectrum Disorder

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Titel: Heterogeneity in Autism Spectrum Disorder Case-Finding Algorithms in United States Health Administrative Database Analyses
Auteurs: Scott D. Grosse
Phyllis Nichols
Kwame Nyarko
Matthew Maenner
Melissa L. Danielson
Lindsay Shea
Publicatiedatum: 28-09-2021
Uitgeverij: Springer US
Gepubliceerd in: Journal of Autism and Developmental Disorders / Uitgave 9/2022
Print ISSN: 0162-3257
Elektronisch ISSN: 1573-3432
DOI: https://doi.org/10.1007/s10803-021-05269-1

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