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Original research
Comparison of methods to identify individuals prescribed opioid analgesics for pain
  1. Reem Farjo1,
  2. Hsou-Mei Hu2,3,
  3. Jennifer F Waljee3,4,
  4. Michael J Englesbe3,4,
  5. Chad M Brummett2,3 and
  6. Mark C Bicket2,3,5
  1. 1Columbia University Vagelos College of Physicians and Surgeons, New York, New York, USA
  2. 2Department of Anesthesiology, University of Michigan-Ann Arbor, Ann Arbor, Michigan, USA
  3. 3Overdose Prevention Engagement Network, Institute for Healthcare Policy and Innovation, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
  4. 4Department of Surgery, University of Michigan Health System, Ann Arbor, Michigan, USA
  5. 5Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
  1. Correspondence to Dr Mark C Bicket, Anesthesiology, University of Michigan Michigan Medicine, Ann Arbor, Michigan, USA; mbicket{at}med.umich.edu

Abstract

Introduction While identifying opioid prescriptions in claims data has been instrumental in informing best practises, studies have not evaluated whether certain methods of identifying opioid prescriptions yield better results. We compared three common approaches to identify opioid prescriptions in large, nationally representative databases.

Methods We performed a retrospective cohort study, analyzing MarketScan, Optum, and Medicare claims to compare three methods of opioid classification: claims database-specific classifications, National Drug Codes (NDC) from the Centers for Disease Control and Prevention (CDC), or NDC from Overdose Prevention Engagement Network (OPEN). The primary outcome was discrimination by area under the curve (AUC), with secondary outcomes including the number of opioid prescriptions identified by experts but not identified by each method.

Results All methods had high discrimination (AUC>0.99). For MarketScan (n=70,162,157), prescriptions that were not identified totalled 42,068 (0.06%) for the CDC list, 2,067,613 (2.9%) for database-specific categories, and 0 (0%) for the OPEN list. For Optum (n=61,554,852), opioid prescriptions not identified totalled 9,774 (0.02%) for the CDC list, 83,700 (0.14%) for database-specific categories, and 0 (0%) for the OPEN list. In Medicare claims (n=92,781,299), the number of opioid prescriptions not identified totalled 8,694 (0.01%) for the CDC file and 0 (0%) for the OPEN list.

Discussion This analysis found that identifying opioid prescriptions using methods from CDC and OPEN were similar and superior to prespecified database-specific categories. Overall, this study shows the importance of carefully selecting the approach to identify opioid prescriptions when investigating claims data.

  • Methods
  • Analgesics, Opioid
  • Pain Management

Data availability statement

Data may be obtained from a third party and are not publicly available. All data relevant to the study are included in the article or uploaded as supplementary information. Data sets in this study (MarketScan, Optum, and Medicare claims) are available through those third parties.

https://doi.org/10.1136/rapm-2023-105164

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    Data availability statement

    Data may be obtained from a third party and are not publicly available. All data relevant to the study are included in the article or uploaded as supplementary information. Data sets in this study (MarketScan, Optum, and Medicare claims) are available through those third parties.

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    Footnotes

    • Twitter @drchadb, @MarkBicket

    • Contributors MCB, CMB, MJE, JFW, and H-MH all participated in the conceptualization and study design of this project. H-MH was responsible primarily for the data collection and statistical analysis. RF and MCB were primarily responsible for data analysis, figure creation, and drafting the manuscript. All authors contributed to significant editing of the manuscript. MCB is the guarantor.

    • Funding Research reported in this publication was supported by funding from the Centers for Medicare and Medicaid Services under subcontract award number 253001. The University of Michigan Institute for Healthcare Policy and Innovation provided access to Optum’s database without charge.

    • Disclaimer The content is solely the responsibility of the authors and does not necessarily represent the official views of the Department of Health and Human Services.

    • Competing interests CMB serves as a consultant for Vertex Pharmaceuticals and Merck Pharmaceuticals. In addition, CMB provides expert medicolegal testimony unrelated to this analysis.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.