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A computational network perspective on pediatric anxiety symptoms
Published online by Cambridge University Press: 13 August 2020
Abstract
While taxonomy segregates anxiety symptoms into diagnoses, patients typically present with multiple diagnoses; this poses major challenges, particularly for youth, where mixed presentation is particularly common. Anxiety comorbidity could reflect multivariate, cross-domain interactions insufficiently emphasized in current taxonomy. We utilize network analytic approaches that model these interactions by characterizing pediatric anxiety as involving distinct, inter-connected, symptom domains. Quantifying this network structure could inform views of pediatric anxiety that shape clinical practice and research.
Participants were 4964 youths (ages 5–17 years) from seven international sites. Participants completed standard symptom inventory assessing severity along distinct domains that follow pediatric anxiety diagnostic categories. We first applied network analytic tools to quantify the anxiety domain network structure. We then examined whether variation in the network structure related to age (3-year longitudinal assessments) and sex, key moderators of pediatric anxiety expression.
The anxiety network featured a highly inter-connected structure; all domains correlated positively but to varying degrees. Anxiety patients and healthy youth differed in severity but demonstrated a comparable network structure. We noted specific sex differences in the network structure; longitudinal data indicated additional structural changes during childhood. Generalized-anxiety and panic symptoms consistently emerged as central domains.
Pediatric anxiety manifests along multiple, inter-connected symptom domains. By quantifying cross-domain associations and related moderation effects, the current study might shape views on the diagnosis, treatment, and study of pediatric anxiety.
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- As a work owned by the United States Government, this Contribution is not subject to copyright within the United States. Outside of the United States, Cambridge University Press is the non-exclusively licensed publisher of the Contribution.
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- Copyright © NIMH 2020 outside of the United States of America
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