Network analysis of substance abuse and dependence symptoms. (1st April 2016) - Rhemtulla, Mijke; Fried, Eiko I.; Aggen, Steven H.; Tuerlinckx, Francis; Kendler, Kenneth S.; Borsboom, Denny
Graphical abstract: Highlights: Abuse and dependence symptoms are modeled as directly interacting variables in a network. Network analysis reveals pairwise symptom interactions that traditional analyses obscure. Abuse and dependence symptoms do not function equivalently across 6 illicit substance classes. Abstract: Background: The DSM uses one set of abuse and dependence criteria to assess multiple substance use disorders (SUDs). Most SUD research aggregates across these symptoms to study the behavior of SUD as a static construct. We use an alternative approach that conceptualizes symptoms as directly interacting variables in psychopathological networks. We apply network models to symptom-level data to investigate the unique roles of individual symptoms and their interactions in SUD. Methods: We analyzed 11 DSM III-R/IV abuse and dependence criteria in a sample of 2405 adult twins who reported use of at least one illicit substance six or more times from the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders (VATSPSUD). We estimated a symptom network for each substance class as well as a global network collapsed across all substance classes. We examined similarities and differences across the 6 networks in terms of symptom-to-symptom connections and symptom centrality. Results: The global network model revealed several interesting symptom connections, such as a strong predictive relation between tolerance and more-than-planned substance use. The most central symptom was using a drug more than planned. In addition, several interesting differences across substances emerged, both in the strength of symptom connections as well as the centrality of symptoms to each network. Conclusions: When analyzed as networks, abuse and dependence symptoms do not function equivalently across illicit substance classes. These findings suggest the value of analyzing individual symptoms and their associations to gain new insight into the mechanisms of SUD.