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

Area of Science:

• Social Network Analysis
• Public Health
• Pharmacology

Background:

• The United States faces a critical opioid overdose crisis, with prescription opioids contributing significantly to mortality.
• Peer-driven network interventions show promise for addressing this public health issue.
• Understanding social network structures is key to designing effective interventions.

Purpose of the Study:

• To construct and analyze a social network of patients using prescription opioids in Rhode Island.
• To identify patient attributes associated with network tie formation and homophily.
• To determine predictors of influence (betweenness centrality) within the patient network.

Main Methods:

• Utilized opioid prescription records to build a social network of patients.
• Applied exponential random graph models (ERGM) to analyze network formation and homophily.
• Employed multivariable logistic regression to identify predictors of high betweenness centrality.

Main Results:

• Homophily in the network was linked to age, payment method, opioid type/quantity, dosage, and number of providers.
• Prescription opioid type and the number of prescribers were significant predictors of high betweenness centrality.
• 372 patients were analyzed, with an average age of 51 and 53% female.

Conclusions:

• Patients with multiple prescribers or opioid use disorder may act as influential nodes in prescription opioid networks.
• These individuals could be leveraged to promote positive health behaviors or mitigate harmful ones.
• Network analysis provides valuable insights for designing targeted public health interventions for opioid use.