A data-driven method for identifying shorter symptom criteria sets: the case for DSM-5 alcohol use disorder

Although the DSM is a widely used diagnostic guide, lengthy criteria sets can be problematic and provide the primary motivation to identify short-forms. Using the 11 diagnostic criteria provided by the DSM-5 for alcohol use disorder (AUD), the present study develops a data-driven method to systematically identify subsets and associated cut-offs that yield diagnoses as similar as possible to use all 11 criteria.

Relying on data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC-III), our methodology identifies diagnostic short-forms for AUD by: (1) maximizing the association between the sum scores of all 11 criteria with newly constructed subscales from subsets of criteria; (2) optimizing the similarity of AUD prevalence between the current DSM-5 rule and newly constructed diagnostic short-forms; (3) maximizing sensitivity and specificity of the short-forms against the current DSM-5 rule; and (4) minimizing differences in the accuracy of the short-form across chosen covariates. Replication is shown using NESARC-Wave 2.

More than 11 000 diagnostic short-forms for DSM-5 AUD can be created and our method narrows down the optimal choices to eight. Results found that 'Neglecting major roles' and 'Activities given up' could be dropped with practically no change in who is diagnosed (specificity = 100%, sensitivity ⩾ 99.6%) or the severity of those diagnosed (κ = 0.97).

With a continuous improvement model adopted by the APA for DSM revisions, we offer a data-driven tool (a SAS Macro) that identifies diagnostic short-forms in a systematic and reproducible way to help advance potential improvements in future DSM revisions.