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

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Using shape and weight overvaluation to empirically differentiate severity of other specified feeding or eating disorder.

BACKGROUND: Although no severity specifiers are noted in the Diagnostic and Statistical Manual of Mental Disorders - 5 for other specified feeding or eating disorder (OSFED), shape/weight overvaluation is a proposed eating disorder (ED) severity specifier. We used structural equation modeling (SEM) Trees to empirically determine values of shape/weight overvaluation that differentiate OSFED severity. We additionally tested whether the SEM Tree-defined thresholds or a clinical cutoff for shape/weight overvaluation differentiated severity more meaningfully. METHODS: Participants were 690 females with OSFED presenting to residential ED treatment. SEM Tree analyses specified an outcome model of OSFED severity and then recursively partitioning the outcome model into severity groups. The SEM Tree-defined and clinical cutoff severity groups were compared on clinical characteristics. RESULTS: SEM Trees identified one split that occurred at value 5.12 on our shape/weight overvaluation items from the Eating Disorder Examination Questionnaire. The subgroup with higher overvaluation had significantly greater intensity of ED and depressive symptoms and longer lengths of stay. The subgroups created from the shape/weight overvaluation clinical-cut off value of 4 differed on the same clinical characteristics as the SEM Tree-derived groups, with the exception of laxative use frequency. Effect sizes were larger for the clinical cutoff as compared to the SEM Tree severity specification scheme. LIMITATIONS: These cross-sectional data were used from a predominately white and female residential treatment sample; this likely skewed the subgroups and may limit generalizability. CONCLUSIONS: Shape/weight overvaluation can meaningfully differentiate OSFED severity. The clinical cutoff slightly outperformed the empirically determined thresholds for shape/weight overvaluation.