New means, new measures: assessing prescription drug-seeking indicators over 10 years of the opioid epidemic

Brea L. Perry, Meltem Odabaş, Kai Cheng Yang, Byungkyu Lee, Patrick Kaminski, Brian Aronson, Yong Yeol Ahn, Carrie B. Oser, Patricia R. Freeman, Jeffrey C. Talbert

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Background and aims: Prescription drug-seeking (PDS) from multiple prescribers is a primary means of obtaining prescription opioids; however, PDS behavior has probably evolved in response to policy shifts, and there is little agreement about how to operationalize it. We systematically compared the performance of traditional and novel PDS indicators. Design: Longitudinal study using a de-identified commercial claims database. Setting: United States, 2009–18. Participants: A total of 318 million provider visits from 21.5 million opioid-prescribed patients. Measurements: We applied binary classification and generalized linear models to compare predictive accuracy and average marginal effect size predicting future opioid use disorder (OUD), overdose and high morphine milligram equivalents (MME). We compared traditional indicators of PDS to a network centrality measure, PageRank, that reflects the prominence of patients in a co-prescribing network. Analyses used the same data and adjusted for patient demographics, region, SES, diagnoses and health services. Findings: The predictive accuracy of a widely used traditional measure (N + unique doctors and N + unique pharmacies in 90 days) on OUD, overdose and MME decreased between 2009 and 2018, and performed no better than chance (50% accuracy) after 2015. Binarized PageRank measures however exhibited higher predictive accuracy than the traditional binary measures throughout 2009-2018. Continuous indicators of PDS performed better than binary thresholds, with days of Rx performing best overall with 77–93% predictive accuracy. For example, days of Rx had the highest average marginal effects on overdose and OUD: a 1 standard deviation increase in days of Rx was associated with a 6–8% [confidence intervals (CIs) = 0.058–0.061 and 0.078–0.082] increase in the probability of overdose and a 4–5% (CIs = 0.038–0.043 and 0.047–0.053) increase in the probability of OUD. PageRank performed nearly as well or better than traditional indicators of PDS, with predictive performance increasing after 2016. Conclusions: In the United States, network-based measures appear to have increasing promise for identifying prescription opioid drug-seeking behavior, while indicators based on quantity of providers or pharmacies appear to have decreasing utility.

Original languageEnglish
Pages (from-to)195-204
Number of pages10
JournalAddiction
Volume117
Issue number1
DOIs
StatePublished - Jan 2022

Bibliographical note

Funding Information:
This research was funded by the National Institute on Drug Abuse (R01 DA039928), an Indiana University Addictions Grand Challenge Grant, and a University of Kentucky Interdisciplinary Research Collaborations Seed Grant. We would like to thank the information technology team at the Indiana University Network Science Institute for their technical support.

Publisher Copyright:
© 2021 Society for the Study of Addiction.

Keywords

  • Co-prescription networks
  • drug dependence
  • opiates
  • opioid use disorder
  • overdose
  • prescription drug-seeking
  • prescription opioids

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Psychiatry and Mental health

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