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

Autor/inn/en	Grosse, Scott D.; Nichols, Phyllis; Nyarko, Kwame; Maenner, Matthew; Danielson, Melissa L.; Shea, Lindsay
Titel	Heterogeneity in Autism Spectrum Disorder Case-Finding Algorithms in United States Health Administrative Database Analyses
Quelle	In: Journal of Autism and Developmental Disorders, 52 (2022) 9, S.4150-4163 (14 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Grosse, Scott D.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0162-3257
DOI	10.1007/s10803-021-05269-1
Schlagwörter	Autism; Pervasive Developmental Disorders; Data Use; Databases; Health Services; Identification + Suchen Sie Ihr Suchwort? Autismus; Datenbank; Health service; Gesundheitsdienst; Gesundheitswesen; Identifikation; Identifizierung
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01