Co-prescription network reveals social dynamics of opioid doctor shopping

Autoři: Brea L. Perry aff001;  Kai Cheng Yang aff003;  Patrick Kaminski aff002;  Meltem Odabas aff002;  Jaehyuk Park aff003;  Michelle Martel aff004;  Carrie B. Oser aff005;  Patricia R. Freeman aff006;  Yong-Yeol Ahn aff001;  Jeffery Talbert aff006
Působiště autorů: Network Science Institute, Indiana University, 1001 45/46 Bypass, Bloomington, IN, United States of America aff001;  Department of Sociology, Indiana University, Bloomington, IN, United States of America aff002;  School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States of America aff003;  Department of Psychology, University of Kentucky, Lexington, KY, United States of America aff004;  Department of Sociology, University of Kentucky, Lexington, KY, United States of America aff005;  Department of Pharmacy Practice and Science, University of Kentucky, Lexington, KY, United States of America aff006
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article


This paper examines network prominence in a co-prescription network as an indicator of opioid doctor shopping (i.e., fraudulent solicitation of opioids from multiple prescribers). Using longitudinal data from a large commercially insured population, we construct a network where a tie between patients is weighted by the number of shared opioid prescribers. Given prior research suggesting that doctor shopping may be a social process, we hypothesize that active doctor shoppers will occupy central structural positions in this network. We show that network prominence, operationalized using PageRank, is associated with more opioid prescriptions, higher predicted risk for dangerous morphine dosage, opioid overdose, and opioid use disorder, controlling for number of prescribers and other variables. Moreover, as a patient’s prominence increases over time, so does their risk for these outcomes, compared to their own average level of risk. Results highlight the importance of co-prescription networks in characterizing high-risk social dynamics.

Klíčová slova:

Algorithms – Cancer detection and diagnosis – Centrality – Dose prediction methods – Insurance – Medical doctors – Opioids – Social networks


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