Empirically determined severity levels for binge-eating disorder outperform existing severity classification schemes.

Abstract

Eating-disorder severity indicators should theoretically index symptom intensity, impairment, and level of needed treatment. Two severity indicators for binge-eating disorder (BED) have been proposed (categories of binge-eating frequency and shape/weight overvaluation) but have mixed empirical support including modest clinical utility. This project uses structural equation model (SEM) trees - a form of exploratory data mining - to empirically determine the precise levels of binge-eating frequency and/or shape/weight overvaluation that most significantly differentiate BED severities. Participants were 788 adults with BED enrolled in BED treatment studies. Participants completed interviews and self-report measures assessing eating-disorder and comorbid symptoms. SEM Tree analyses were performed by specifying an outcome model of BED severity and then recursively partitioning the outcome model into subgroups. Subgroups were split based on empirically determined values of binge-eating frequency and/or shape/weight overvaluation. SEM Forests also quantified which variable contributed more improvement in model fit. SEM Tree analyses yielded five subgroups, presented in ascending order of severity: overvaluation <1.25, overvaluation = 1.25-2.74, overvaluation = 2.75-4.24, overvaluation ⩾4.25 with weekly binge-eating frequency <4.875, and overvaluation ⩾4.25 with weekly binge-eating frequency ⩾4.875. SEM Forest analyses revealed that splits that occurred on shape/weight overvaluation resulted in much more improvement in model fit than splits that occurred on binge-eating frequency. Shape/weight overvaluation differentiated BED severity more strongly than binge-eating frequency. Findings indicate a nuanced potential BED severity indicator scheme, based on a combination of cognitive and behavioral eating-disorder symptoms. These results inform BED classification and may allow for the provision of more specific and need-matched treatment formulations.