Network-based Analysis of Prescription Opioids Dispensing Using Exponential Random Graph Models (ERGMs)

The United States has been experiencing an unprecedented level of opioid overdose-related mortality due in part to excessive use of prescription opioids. Peer-driven network interventions may be beneficial. A key assumption of social network interventions is that of some members of the network act as key players and can influence the behavior of others in the network. We used opioid prescription records to create a social network of patients who use prescription opioid in the state of Rhode Island. The study population was restricted to patients on stable opioid regimens who used one source of payment and received the same opioid medication from ≥ 3 prescribers and pharmacies. An exponential random graph model (ERGM) was employed to examine the relationship between patient attributes and the likelihood of tie formation and modularity was used to assess for homophily (the tendency of individuals to associate with similar people). We used multivariable logistic regression to assess predictors of high betweenness centrality, a measure of influence within the network. 372 patients were included in the analysis; average age was 51 years; 53% were female; 57% were prescribed oxycodone, 34% were prescribed hydrocodone and 9% were prescribed buprenorphine/naloxone. After controlling for the main effects in the ERGM model, homophily was associated with age group, method of payment, number and type of opioid prescriptions filled, mean daily dose, and number of providers seen. Type of opioid and number of prescribers were identified as significant predictors of high betweenness centrality. We conclude that patients who use multiple prescribers or have a diagnosis of opioid use disorder may help promote positive health behaviors or disrupt harmful behaviors in an opioid prescription network.