Citation

Conway CC, Mansolf M, Reise SP. Psychol. Assess. 2019; 31(6): 730-740.

Affiliation

Department of Psychology, University of California, Los Angeles.

Copyright

(Copyright © 2019, American Psychological Association)

DOI

10.1037/pas0000695

PMID

30667268

Abstract

Quantitative models of mental illness, such as the Hierarchical Taxonomy of Psychopathology (HiTOP), aim to optimize clinical assessment, which conventionally follows categorical diagnostic rubrics. The evidence base for these models is robust, but also uniform; available data come mostly from structured diagnostic interviews in nationally representative samples. It remains to be seen whether HiTOP adequately reflects mental illness as evaluated in routine clinical care, where diagnosis is often unsystematic and incomplete, relative to controlled research conditions. To test the generalizability of a quantitative nosology to real-world assessment contexts, we fit the HiTOP model to diagnoses in a large sample (N = 25,002) of treatment-seeking university students who were seen by health professionals in everyday practice. We then examined the criterion validity of model components in relation to clinically relevant outcomes (i.e., suicide attempts, self-injury, and binge drinking). Three related structures fit the data well: a correlated-factor model with internalizing, externalizing, and eating pathology dimensions; a higher-order model that added a general factor of psychopathology that spanned these 3 first-order factors; and a bifactor model that partitioned diagnostic (co)variance across a general factor and 3 orthogonal group factors. The first-order factors had expected patterns of criterion validity, and the general factor was a strong predictor of suicidality and self-injury, paralleling past research. Bifactor model group factors had interpretative problems, however. Across models, categorical diagnoses consistently offered minimal incremental validity relative to the transdiagnostic factors. We conclude that HiTOP is ecologically valid-explaining comorbidity patterns among diagnoses assigned "in the field"-and is poised to enhance clinical assessment and decision-making in routine care. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Language: en