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Diagnosing autism spectrum disorder in community settings using the Development and Well-Being Assessment: Validation in a UK population-based twin sample

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Pages (from-to)161-170
JournalJournal of Child Psychology and Psychiatry
Issue number2
Early online date15 Jul 2015
Accepted/In press8 Jun 2015
E-pub ahead of print15 Jul 2015
PublishedFeb 2016


  • McEwen_et_al_2015_Diagnosing_ASD_in_community_settings_using_the_DAWBA_Validation_in_a_UK_population_based_twin_sample

    McEwen_et_al_2015_Diagnosing_ASD_in_community_settings_using_the_DAWBA_Validation_in_a_UK_population_based_twin_sample.pdf, 870 KB, application/pdf

    Uploaded date:03 Aug 2015

    Version:Final published version

    Licence:CC BY

    Diagnosing autism spectrum disorder in community settings using the Development and Well-Being Assessment: validation in a UK population-based twin sample/Fiona S. McEwen et al./Journal of Child Psychology and Psychiatry/DOI: 10.1111/jcpp.12447. Copyright (c) 2015 The Authors.

    This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.


King's Authors


Background. Increasing numbers of people are being referred for the assessment of autism spectrum disorder (ASD). The NICE (UK) and the American Academy of Pediatrics recommend gathering a developmental history using a tool that operationalises ICD/DSM criteria. However, the best-established diagnostic interview instruments are time consuming, costly and rarely used outside national specialist centres. What is needed is a brief, cost-effective measure validated in community settings. We tested the Development and Well-Being Assessment (DAWBA) for diagnosing ASD in a sample of children/adolescents representative of those presenting in community mental health settings.Methods. A general population sample of twins (TEDS) was screened and 276 adolescents were selected as at low (CAST score < 12; n = 164) or high risk for ASD (CAST score ≥ 15 and/or parent reported that ASD suspected/previously diagnosed; n = 112). Parents completed the ASD module of the DAWBA interview by telephone or online. Families were visited at home: the ADI-R and autism diagnostic observation schedule (ADOS) were completed to allow a best-estimate research diagnosis of ASD to be made.Results. Development and Well-Being Assessment ASD symptom scores correlated highly with ADI-R algorithm scores (ρ = .82, p < .001). Good sensitivity (0.88) and specificity (0.85) were achieved using DAWBA computerised algorithms. Clinician review of responses to DAWBA questions minimally changed sensitivity (0.86) and specificity (0.87). Positive (0.82–0.95) and negative (0.90) predictive values were high. Eighty-six per cent of children were correctly classified. Performance was improved by using it in conjunction with the ADOS.Conclusions. The DAWBA is a brief structured interview that showed good sensitivity and specificity in this general population sample. It requires little training, is easy to administer (online or by interview) and diagnosis is aided by an algorithm. It holds promise as a tool for assisting with assessment in community settings and may help services implement the recommendations made by NICE and the American Academy of Pediatrics regarding diagnosis of young people on the autism spectrum.

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