Integrating “Lumpers” Versus “Splitters” Perspectives: Toward a Hierarchical Dimensional Taxonomy of Eating Disorders From Clinician Ratings

Abstract

In this study, we describe a hierarchical dimensional model of eating-disorder (ED) classification based on the Hierarchical Taxonomy of Psychopathology. Participants were community-recruited adults with an ED ( N = 252; 81.9% female). We used a modified version of Goldberg’s method, which involved sequentially extracting latent factors using exploratory structural equation modeling, resulting in a 10-factor hierarchical-dimensional model. Dimensions predicted 92.4% and 58.7% of the variance in recovery outcomes at 6 months and 1 year, respectively. Compared with other illness indicators (e.g., Diagnostic and Statistical Manual of Mental Disorders [ DSM] diagnoses, dimensional ED impairment scores, weight/shape overvaluation, and DSM ED-severity specifiers), hierarchical dimensions predicted 0.88 to 334 times more variance in ED behaviors at baseline and 1.95 to 80.8 times more variance in psychiatric impairment at 1-year follow-up. Results suggest that reducing within-disorder heterogeneity for EDs within the broader context of internalizing symptoms provides a powerful framework from which to predict outcomes and understand symptoms experienced by people with EDs.

Examining associations between disordered eating and harmful substance use in a nationally representative sample of US veterans

“…We retained body dissatisfaction in the PCA because whether it was included did not change the three-factor PCA structure and did not change the statistical significance of results in subsequent analyses. Results of the PCA were consistent with our past research in sample with clinically significant EDs (Forbush et al, 2023), where binge eating, purging, and body dissatisfaction also formed a higherorder factor. Negative attitudes toward obesity was included as a separate predictor in the regression models.…”

State of the Science: The Hierarchical Taxonomy of Psychopathology (HiTOP)

“…HiTOP offers an ideal framework for initial assessment and routine outcomes measurement to help clinicians select tailored modules and inform decisions about when to "pivot" to different modules. For example, Forbush et al (2023) found that HiTOP measures prospectively predicted more variance in relevant outcomes than all other traditional disorder-specific indicators, a finding that is consistent with several retrospective studies (see Kotov et al, 2022, for a review). These results have led to an innovative clinical trial in which Forbush et al are using weekly HiTOP assessments in conjunction with machine learning to provide clinicians with a "stop-light tool" that generates a visual depiction of when clients are at risk for poor outcomes (ClinicalTrials.gov NCT05190926).…”

“…Despite the shift in terminology, the same issues remain, and these diagnoses provide limited information for treatment planning or prognosis (Forbush et al, 2023;Kotov et al, 2022).…”