Date of Award


Degree Type


Degree Name

Master of Science in Statistics


Computer Science and Statistics

First Advisor

Natallia V. Katenka


IMPORTANCE: Patients using prescription opioid are embedded in a network due to provider-sharing and living in the same community. As a result, they may exert influence on each other’s treatment preferences and share attitudes towards prescription opioid use and misuse.

OBJECTIVE: To determine patient characteristics associated with the observed pattern of shared prescribers in a network and identify influential patients in the network.

DESIGN, SETTING, AND PARTICIPANTS: We conducted a cross-sectional network-based study using the Rhode Island (RI) Prescription Drug Monitoring Program (PDMP) data for the 2015 calendar year. All patients who filled at least one opioid prescription at a retail pharmacy were eligible. The analysis was limited to patients who were on a stable opioid regimen and used only one source of payment, and filled only one type of opioid medication (oxycodone, hydrocodone or buprenorphine/naloxone) from ≥ 3 prescribers, and visited ≥ 3 pharmacies during the year. To minimize the influence of less relevant network connections, we excluded institutional providers and providers who issued opioid prescriptions to ≤ 6 patients. We applied social network analysis (SNA) methods to a sample of 372 patients connected to each other through provider-sharing. We used the exponential random graph model (ERGM) assuming conditional dyadic independence to examine the relationship between patient attributes and the likelihood of forming network ties. Homophily was defined as the tendency of patients to associate with others who have similar characteristics. Three centrality measures (degree, closeness, and betweenness) were used to identify patients with potential influence in the opioid prescription network.

MAIN OUTCOMES AND MEASURES: We provide a visual and descriptive characterization of the network, used centrality measures to identify influential patients, and ERGM to assess homophily and differential homophily.

RESULTS: The mean age of patients included in the analysis was 51 years; 53% were female; 57% took oxycodone, 34% took hydrocodone and 9% took buprenorphine/naloxone. On average, 53% of patients received less than 50 morphine milligram equivalents (MME) daily, and the mean (standard deviation [SD]) number of opioid prescriptions per patient was 14.4 (6.6). Sixty-four percent of patients had commercial insurance, 28% had Medicaid, 5% had Medicare, and almost 2.5% used cash payment only. All three centrality measures were in agreement on the identification of the most influential patient in the opioid prescription network but overall correlation between the measures was low. After controlling for the main effects in the ERGM model, homophily was associated with age group, method of payment, number and type of opioid prescription filled, mean daily MME, and number of providers seen.

CONSLUSIONS: Characteristics of patients in an opioid prescription network may influence which provider they choose and which patients they are connect to through provider sharing. Interventions targeted at influential patients in the network may have potential to influence social norms around the use and misuse of prescription opioids that may lead to reductions in prescription opioid-related overdose deaths.



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