Heterogeneity in Autism Spectrum Disorder Case-Finding Algorithms in United States Health Administrative Database Analyses / Scott D. GROSSE in Journal of Autism and Developmental Disorders, 52-9 (September 2022)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 52-9 (September 2022) . - p.4150-4163 Titre : Heterogeneity in Autism Spectrum Disorder Case-Finding Algorithms in United States Health Administrative Database Analyses Type de document : Texte imprimé et/ou numérique Auteurs : Scott D. GROSSE, Auteur ; Phyllis NICHOLS, Auteur ; Kwame NYARKO, Auteur ; Matthew MAENNER, Auteur ; Melissa L. DANIELSON, Auteur ; Lindsay SHEA, Auteur Article en page(s) : p.4150-4163 Langues : Anglais (eng) Mots-clés : Algorithms  Autism Spectrum Disorder/diagnosis/epidemiology  Databases, Factual  Humans  Insurance Coverage  United States/epidemiology  Autism spectrum disorder  Case-finding algorithms  Claims data  Health services research  article to disclose. Index. décimale : PER Périodiques Résumé : 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. En ligne : http://dx.doi.org/10.1007/s10803-021-05269-1 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=486 [article] Heterogeneity in Autism Spectrum Disorder Case-Finding Algorithms in United States Health Administrative Database Analyses [Texte imprimé et/ou numérique] / Scott D. GROSSE, Auteur ; Phyllis NICHOLS, Auteur ; Kwame NYARKO, Auteur ; Matthew MAENNER, Auteur ; Melissa L. DANIELSON, Auteur ; Lindsay SHEA, Auteur . - p.4150-4163. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 52-9 (September 2022) . - p.4150-4163 Mots-clés : Algorithms  Autism Spectrum Disorder/diagnosis/epidemiology  Databases, Factual  Humans  Insurance Coverage  United States/epidemiology  Autism spectrum disorder  Case-finding algorithms  Claims data  Health services research  article to disclose. Index. décimale : PER Périodiques Résumé : 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. En ligne : http://dx.doi.org/10.1007/s10803-021-05269-1 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=486

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