"General Addiction Liability" Revisited

Abstract

Although substance use disorders are widely known to be influenced by myriad etiologic factors, recent research promotes the notion that liability toward addiction broadly construed can be described by a single, unitary dimension that we term general addiction liability. Here, we revisit the concept of general addiction liability by placing it at greater theoretical and empirical risk. To do so, we used data from two epidemiologic samples (ns from 262-8552) and employed varied quantitative methods to examine the associations between alcohol, cannabis, tobacco, and opioid use disorders. We did not find strong evidence for general addiction liability. Nevertheless, consequence-based features (e.g., social/interpersonal harm, hazardous use) tended to form cross-substance connections. We contextualize our findings in the broader literature on addiction liability and offer several explanations for why we and others arrive at competing conclusions with respect to the robustness and nature of general addiction liability.

Figure 1.. Analytic plan and what evidence would support general addiction liability.

Note. CFA = confirmatory factor analysis; EFA = exploratory factor analysis; SUD = substance use disorder.

Figure 2.. A replication of Hatoum and colleagues’ (2021) examination of general addiction liability.

Note. We present standardized factor loadings with their standard errors in parentheses.

Figure 3.. Standardized factor loadings for confirmatory bifactor models.

Note. AUD = alcohol use disorder; CUD = cannabis use disorder; OUD = opioid use disorder; TUD = tobacco use disorder; CD = desire or failed attempts to cut down/quit; CR = craving; GU = gives up activities; LL = use for larger/longer than intended; HZ = recurrent hazardous use while under the influence of the substance; PP = continued use despite physical/psychological harm; RI = use interferes with roles; SI = continued use despite social/interpersonal harm; TL = tolerance; TS = time spent obtaining, using, and getting over the effects of the substance; WD = withdrawal. SUD 1 refers to the first SUD listed in each panel, whereas SUD 2 refers to the second. For instance, in the top right panel, for NESARC-III, SUD 1 corresponds to AUD and SUD 2 corresponds to CUD.

Figure 4.. Averaged Ising networks across samples and methods.

Note. AUD = alcohol use disorder; CUD = cannabis use disorder; OUD = opioid use disorder; TUD = tobacco use disorder; CD = desire or failed attempts to cut down/quit; CR = craving; GU = gives up activities; LL = use for larger/longer than intended; HZ = recurrent hazardous use while under the influence of the substance; PP = continued use despite physical/psychological harm; RI = use interferes with roles; SI = continued use despite social/interpersonal harm; TL = tolerance; TS = time spent obtaining, using, and getting over the effects of the substance; WD = withdrawal. Here, we present averaged networks in which we averaged the edge weight matrices across samples and methods while weighting all edges by sample size. Non-zero edges that replicated across sample (i.e., NESARC wave 1, NESARC-III) are depicted as solid lines. Non-zero edges that did not replicate are depicted as dotted lines. Edges with positive weights are depicted in green, whereas negative weights are depicted in red. All edges are summarized in Tables S7–12.