Implications of the Symptom-Level Overlap Among DSM Diagnoses for Dimensions of Psychopathology
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Keywords

Hierarchical Taxonomy of Psychopathology (HiTOP)
DSM-5
psychopathology
classification
diagnosis

Abstract

Research on the patterns of covariation among mental disorders has proliferated, as summarized in the Hierarchical Taxonomy of Psychopathology (HiTOP). The aim of this brief descriptive study was to examine whether the repetition of symptoms among DSM-5 diagnoses is likely to be inflating the surface similarity of diagnoses in a way that artificially reinforces the dimensions that emerge when modelling patterns of disorder covariation or comorbidity. Specifically, the symptoms comprising the DSM-5 diagnostic criteria for all disorders covered by the HiTOP framework were examined for patterns of overlap that mirror the patterns of disorder covariation captured in HiTOP dimensions. I found that 358 pairs of the DSM-5 diagnoses covered by the HiTOP framework had one or more overlapping symptoms in their diagnostic criteria, and that a third (n = 130; 34%) of the unique constituent symptoms reinforced the higher-order structure of HiTOP through repetition within dimensions and/or between dimensions in the same superspectrum. By contrast, 86% of the possible pairs of diagnoses did not have any shared symptoms, and the majority of the symptoms (n = 222; 58%) did not influence the structure through repetition. Further, a fifth (n = 71; 19%) of the symptoms worked against the HiTOP structure by increasing the surface similarity of diagnoses under different subfactors, spectra, and superspectra. Overall, while patterns of symptom-level overlap do not appear strong enough to account for the emergence of HiTOP dimensions, these patterns do seem likely to inflate the similarity and consequent covariation of some DSM-5 diagnoses. Research on the statistical structure of psychopathology that uses DSM-5 diagnostic constructs should account for this potential source of bias.

https://doi.org/10.55913/joep.v1i1.6
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Copyright (c) 2023 Miriam K. Forbes