Causal Network Modeling of the Determinants of Drinking Behavior in Comorbid Alcohol Use and Anxiety Disorder. (25th November 2018) - Anker, Justin J.; Kummerfeld, Erich; Rix, Alexander; Burwell, Scott J.; Kushner, Matt G.
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. Abstract : Using a machine‐learning algorithm (GFCI), we found that drinking to cope with negative affect served as a central hub for two distinct causal paths leading to drinking behavior in comorbid individuals, 1) a direct syndromic pathway originating with social anxiety and 2) an indirect stress pathway originating with perceived stress. This is the first application in psychopathology of GFCI, which enables the discovery of causal paths while inferring the influence of unmeasured variables in cross‐sectional data.