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Network-based Analysis of Prescription Opioids Dispensing Using ERGM

Abstract Number:

3030 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Hilary Aroke (1), Natallia Katenka (1), Ashley Buchanan (2), Stephen Kogut (3)

Institutions:

(1) University of Rhode Island, N/A, (2) N/A, N/A, (3) University of Rhode Island, Kingston, RI

Co-Author(s):

Natallia Katenka  
University of Rhode Island
Ashley Buchanan  
N/A
Stephen Kogut  
University of Rhode Island

First Author:

Hilary Aroke  
University of Rhode Island

Presenting Author:

Natallia Katenka  
University of Rhode Island

Abstract Text:

The US has an unprecedented level of opioid overdose-related mortality due to excessive use of prescription opioids. Peer-driven network interventions may be beneficial. A key assumption is some opioid users act as key players and can influence the behavior of others. We used opioid prescription records to create a social network in 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. An exponential random graph model (ERGM) was used to examine the relationship between patient attributes and the likelihood of tie formation and logistic regression to assess predictors of high betweenness centrality. 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, mean daily dose, and number of providers seen. The type of opioid and number of prescribers were significant predictors of high betweenness centrality. We conclude that patients who use multiple prescribers with opioid use disorder may promote positive health or disrupt harmful behaviors in networks.

Keywords:

social network |betweenness centrality|prescription opioid|exponential random graph model | |

Sponsors:

Section on Statistics in Epidemiology

Tracks:

Pharmacoepidemiology

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