Applying network analysis to investigate substance use symptoms associated with drug overdose

Background: Drug overdose deaths have been increasing over the last several decades. While single substance classes, such as opioids, have been implicated in this rise, less is known about the contributions of polysubstance use (PSU) and other combinations of specific substances and symptoms that may be a risk factor for drug overdose. Methods: Symptoms of alcohol, cannabis, and other drug use disorders, as well as co-substance use indicators, were assessed and then examined via network analysis in a sample of young adults (N = 1540). Features of the estimated symptom network were investigated, including topology and node centrality, as well as bridge centrality, which further examines node centrality while accounting for the nodes belonging to discrete communities. Results: Individual symptoms were more strongly associated with other symptoms within the same substance class than across substance classes. Tolerance and withdrawal symptoms were the most central items in the network. However, when accounting for symptoms belonging to discrete substance classes, drug overdose emerged as a strong bridge symptom, among others. Conclusions: As a strong bridge symptom, drug overdose had many connections with a variety of substances and symptoms, which might suggest that risk for drug overdose may be a function of overall substance use severity. Altogether, examining alcohol and substance use symptoms using a network analytic framework provided novel insights into the role PSU might play in conferring risk for drug overdose.

Automated summary

Using network analysis, this study examined alcohol and substance use symptoms in a sample of young adults. The results showed that symptoms within the same substance class were more strongly associated than symptoms across substance classes. Drug overdose emerged as a strong bridge symptom, indicating its connections with various substances and symptoms.