Citation

Anker JJ, Kummerfeld E, Rix A, Burwell SJ, Kushner MG. Alcohol Clin. Exp. Res. 2019; 43(1): 91-97.

Affiliation

University of Minnesota, Department of Psychiatry, Minneapolis, Minnesota.

Copyright

(Copyright © 2019, John Wiley and Sons)

DOI

10.1111/acer.13914

PMID

30371947

Abstract

BACKGROUND: Anxiety and depression ("internalizing") disorders occur in approximately 50% of patients with alcohol use disorder (AUD) and mark a two-fold increase in the rate of relapse in the months following treatment. In a previous study using network modeling, we found that perceived stress and drinking to cope (DTC) with negative affect were central to maintaining network associations between internalizing psychopathology (anxiety and depression) and drinking in comorbid individuals. Here, we extend this approach to a causal framework.

METHODS: Measures of internalizing psychopathology, drinking urges/behavior, abstinence self-efficacy, and DTC were obtained from 362 adult AUD treatment patients who had a co-occurring anxiety disorder. Data were analyzed using a machine-learning algorithm ("Greedy Fast Causal Inference; GFCI) that infers paths of causal influence while identifying potential influences associated with unmeasured ("latent") variables.

RESULTS: Drinking to cope with negative affect served as a central hub for two distinct causal paths leading to drinking behavior, 1) a direct syndromic pathway originating with social anxiety and 2) an indirect stress pathway originating with perceived stress.

CONCLUSIONS: Findings expand the field's knowledge of the paths of influence that lead from internalizing disorder to drinking in AUD as shown by the first application in psychopathology of a powerful network analysis algorithm (GFCI) to model these causal relationships. This article is protected by copyright. All rights reserved.

This article is protected by copyright. All rights reserved.

Language: en

Keywords

Comorbidity; alcohol use disorder; anxiety; machine learning; network analysis