Causal Network Modeling of the Determinants of Drinking Behavior in Comorbid Alcohol Use and Anxiety Disorder

Justin J. Anker, Erich Kummerfeld, Alexander Rix, Scott J. Burwell, Matt G. Kushner

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Background: Anxiety and depression disorders (internalizing psychopathology) occur in approximately 50% of patients with alcohol use disorder (AUD) and mark a 2-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 INTP and drinking in comorbid individuals. Here, we extend this approach to a causal framework. Methods: Measures of INTP, 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: DTC with negative affect served as a central hub for 2 distinct causal paths leading to drinking behavior, (i) a direct syndromic pathway originating with social anxiety and (ii) 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.

Original languageEnglish (US)
Pages (from-to)91-97
Number of pages7
JournalAlcoholism: Clinical and Experimental Research
Volume43
Issue number1
DOIs
StatePublished - Jan 2019

Keywords

  • Alcohol Use Disorder
  • Anxiety
  • Comorbidity
  • Machine Learning
  • Network Analysis

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