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Autism Diagnostic Interview-Revised (ADI-R) Algorithms for Toddlers and Young Preschoolers: Application in a Non-US Sample of 1,104 Children

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Authors
de Bildt, A.
Sytema, S.
Zander, E.
Bölte, Sven
Sturm, H.
Yirmiya, N.
Yaari, M.
Charman, T.
Salomone, E.
LeCouteur, A.
Green, J.
Bedia, R.
Primo, P.
van Daalen, E.
de Jonge, M.
Guðmundsdóttir, E.
Jóhannsdóttir, S.
Raleva, M.
Boskovska, M.
Rogé, B.
Baduel, S.
Moilanen, I.
Yliherva, A.
Buitelaar, J.
Oosterling, I.
Date
2015
Type
Journal Article

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de Bildt, A. and Sytema, S. and Zander, E. and Bölte, S. and Sturm, H. and Yirmiya, N. and Yaari, M. et al. 2015. Autism Diagnostic Interview-Revised (ADI-R) Algorithms for Toddlers and Young Preschoolers: Application in a Non-US Sample of 1,104 Children. Journal of Autism and Developmental Disorders. 45 (7): pp. 2076-2091.
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Journal of Autism and Developmental Disorders
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School of Occupational Therapy and Social Work
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Abstract

The current study aimed to investigate the Autism Diagnostic Interview-Revised (ADI-R) algorithms for toddlers and young preschoolers (Kim and Lord, J Autism Dev Disord 42(1):82–93, 2012) in a non-US sample from ten sites in nine countries (n = 1,104). The construct validity indicated a good fit of the algorithms. The diagnostic validity was lower, with satisfactorily high specificities but moderate sensitivities. Young children with clinical ASD and lower language ability were largely in the mild-to-moderate or moderate-to-severe concern ranges of the ADI-R, nearly half of the older and phrase speech ASD-group fell into the little-to-no concern range. Although broadly the findings support the toddler algorithms, further work is required to understand why they might have different properties in different samples to further inform research and clinical use.