The 'DAWBA bands' as an ordered-categorical measure of child mental health: description and validation in British and Norwegian samples

Goodman, Anna, Heiervang, Einar, Collishaw, Stephan ORCID: https://orcid.org/0000-0002-4296-820X and Goodman, Robert 2010. The 'DAWBA bands' as an ordered-categorical measure of child mental health: description and validation in British and Norwegian samples. Social Psychiatry and Psychiatric Epidemiology 46 (6) , pp. 521-532. 10.1007/s00127-010-0219-x

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Abstract

Purpose To describe and validate the ‘DAWBA bands’. These are novel ordered-categorical measures of child mental health, based on the structured sections of the Development and Well-Being Assessment (DAWBA). Methods We developed computer algorithms to generate parent, teacher, child and multi-informant DAWBA bands for individual disorders and for groups of disorder (e.g. ‘any emotional disorder’). The top two (out of 6) levels of the DAWBA bands were used as computer-generated DAWBA diagnoses. We validated these DAWBA bands in 7,912 British children (7–19 years) and 1,364 Norwegian children (11–13 years), using clinician-rated DAWBA diagnoses as a gold standard. Results In general, the prevalence of clinician-rated diagnosis increased monotonically across all levels of the DAWBA bands, and also showed a dose–response association with service use and risk factors. The prevalence estimates of the computer-generated DAWBA diagnoses were of roughly comparable magnitude to the prevalence estimates from the clinician-generated diagnoses, but the estimates were not always very close. In contrast, the estimated effect sizes, significance levels and substantive conclusions regarding risk factor associations were very similar or identical. The multi-informant and parent DAWBA bands performed especially well in these regards. Conclusion Computer-generated DAWBA bands avoid the cost and delay occasioned by clinical rating. They may, therefore, sometimes provide a useful alternative to clinician-rated diagnoses, when studying associations with risk factors, generating rough prevalence estimates or implementing routine mental health screening.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Subjects:	R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry R Medicine > RJ Pediatrics > RJ101 Child Health. Child health services
Uncontrolled Keywords:	computer-generated diagnoses, diagnostic interview, child mental health, prevalence, associations
Publisher:	Springer
ISSN:	0933-7954
Last Modified:	19 Oct 2022 10:03
URI:	https://orca.cardiff.ac.uk/id/eprint/23076

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