Full length articleNetwork analysis of substance abuse and dependence symptoms
Graphical abstract
Introduction
Drug abuse and dependence is a common and increasing worldwide public health concern (World Health Organization, 2010). In the US, life-time prevalence estimates of substance use disorders (SUD) range from 2–3% for illicit substances to 8% for alcohol use, and 12-month rates of substance abuse or dependence increase from 7% to 20% during adolescence (Merikangas and McClair, 2012).
Recent research in psychopathology indicates that the analysis of individual symptoms can reveal crucial insights obfuscated by other analytic strategies (Fried and Nesse, 2015, Smeets et al., 2014). A central tenet of symptom-based approaches is that interactions among symptoms may be central to understanding how disorders arise, sustain themselves, and are cured (Borsboom and Cramer, 2013; Buu et al., 2012; Cullen et al., 2013; Fergus et al., 2015; Fried, 2015; Jacobsen et al., 2001). A useful way to examine such symptom-level effects is to apply a network model, which uses pairwise interactions among symptoms to represent a disorder as a web of mutually influencing symptoms (Borsboom and Cramer, 2013). These models have been successfully applied to a number of disorders such as posttraumatic stress disorder (McNally et al., 2015) and major depression (Fried et al., 2015).
The network framework is an appropriate and useful conceptual approach to analyzing data whenever relations among symptoms can be plausibly interpreted as interacting directly with each other. Similar to other disorders, there is evidence that SUD symptoms may arise in a causal sequence; for example, drinking more alcohol than planned is frequently the first symptom of alcohol use disorder to arise (Buu et al., 2012), which aligns with the finding that impaired control over alcohol use is an important predictor of problem drinking in adolescents (Leeman et al., 2012). To date, no research has investigated such symptom interactions. A network model of SUD can give an overview of the connection patterns among symptoms, revealing which symptoms are most closely related to each other, and which symptoms are most central to the disorder. In addition, network analyses allow us to compare networks across several substance classes, and to locate important differences in the symptom-to-symptom pathways that may exist due to distinct pharmacologic and psychological properties of the substance and/or different patterns of use (Degenhardt et al., 2001, Koob and Le Moal, 2006).
In the remainder of the paper, we present and interpret three cross-sectional network analyses of substance abuse and dependence symptoms. First, we examine a psychopathological network of symptom data averaged over 6 illicit substance classes (cannabis, sedatives, stimulants, cocaine, opioids, and hallucinogens) in 2,405 individuals. We investigate the pairwise connections among 11 symptoms, and estimate measures of symptom centrality to identify which symptoms may be most important in the maladaptive behavior patterns of SUD. Second, we compute symptom networks for each of the substance classes separately. Our aim here is to explore the important differences and similarities of substance classes based on a network representation, and what these differences can tell us about the interconnectivity patterns of SUD symptoms. Finally, we estimate the variance of symptom-to-symptom connections across substance classes (i.e., how much does the strength of the association between symptom pairs vary across the six classes) to identify which of these connections vary most widely across substances.
Section snippets
Sample
Data for the analyses carried out in this study come from twins who participated in the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders (VATSPSUD). Initial eligibility was determined through successful matching of birth records, if twin members were Caucasian and born between 1940 and 1974 in Virginia, USA. Detailed information about substance use and related behaviors were obtained for 2 data collection samples. Female–female twins participating in the third follow-up
Demographics
2,405 participants were included in the final sample. Of these, all were Caucasian, 35% were female, and the average age was 34.7 (SD = 7.3, range = 20–57). 58% of participants used a single substance class, while 20% used two substance classes, 9% three, 5% four, 4% five, and 3% all six.
Cross-substance network
Fig. 1 depicts the results of the global SUD network. Each node in the network depicts a symptom, and each edge represents bidirectional partial relations between symptoms, controlling for all other associations in
Discussion
Most research on SUD uses aggregated symptom-level data: symptom scores are transformed into a diagnostic category, a sum-score (e.g., Grant et al., 2015), or a latent variable (e.g., Gillespie et al., 2007). But theorists and clinicians also recognize that there are important differences among individual symptoms; symptoms may behave differently, they may be indicative of different developmental stages of a disorder, and they may have direct effects on other symptoms, leading a disordered
Funding source
This work was supported by the European Research Council (MR; FP7/2007-2013 no. 631145, and DB; consolidator grant no. 647209), the Research Foundation Flanders (EIF; G.0806.13), the Belgian Federal Science Policy within the framework of the Interuniversity Attraction Poles program (EIF; IAP/P7/06), a grant from the University of Leuven (EIF; GOA/15/003), and the National Institutes of Health (KSK; grants RO1DA037558 and R01DA03005).
Contributors
Mijke Rhemtulla, Department of Psychology, University of Amsterdam, The Netherlands
Eiko I. Fried, Faculty of Psychology and Educational Sciences, University of Leuven, Belgium
Steven H. Aggen, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA; Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
Francis Tuerlinckx, Faculty of Psychology and Educational Sciences, University of Leuven, Belgium
Kenneth S. Kendler,
Conflict of interest
None.
Acknowledgements
We would like to express our gratitude to all participants of the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders study.
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2023, Drug and Alcohol DependenceCitation Excerpt :However, exploratory analyses assessing the association of comorbid mental health problems with CUD symptoms did reveal gender differences; while the presence of anxiety and mood disorders were associated with each other in both men and women, the way they connected to the CUD symptom network was different. The estimated CUD symptom network was dense, in line with a previous study assessing the DSM-IV CUD symptom network (Rhemtulla et al., 2016), and consistent between men and women. This density might theoretically affect the developmental trajectory of CUD; in denser networks, when one symptom occurs (e.g., craving) the pathology can more easily spread (i.e., other symptoms develop) through the network because the initial symptom is connected to many other symptoms (e.g. Borsboom and Cramer, 2013).
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Both the authors contributed equally to this work.