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The Value of Shorter Initial Opioid Prescriptions: A Simulation Evaluation

  • Margrét V. BjarnadóttirEmail author
  • David R. Anderson
  • Kislaya Prasad
  • Ritu Agarwal
  • D. Alan Nelson
Original Research Article

Abstract

Background

During the period from 1999 to 2016, more than 350,000 Americans died from overdoses related to the use of prescription opioids. To the extent that supply is directly related to overprescribing, policy interventions aimed at changing prescriber behavior, such as the recent Centers for Disease Control and Prevention guideline, are clearly warranted. Although these could plausibly reduce the prevalence of opioid overuse and dependency, little is known about their economic and health-related impacts.

Objective

The aim of this study was to quantify the efficacy of a policy intervention aimed at reducing the length of initial opioid prescriptions.

Study Design and Methods

A Markov decision process model was fitted on a retrospective cohort of 827,265 patients, and patient cost and health trajectories were simulated over a 24-month period. The model’s parameters were based on patients who received short (≤ 3 days) or long (> 7 days) initial opioid prescriptions, matched using propensity score methods.

Study Population

All active-duty US Army soldiers from 2011 to 2014; the data contained detailed medical and administrative information on over 11 million soldier-months corresponding to 827,265 individual soldiers.

Main Outcome Measure

Overall costs of a policy change, quality-adjusted life-years (QALYs) gained, and $/QALY gained.

Results

Over a 2-year horizon, a reassignment of 10,000 patients to short initial duration would generate a cost saving in the vicinity of $3.1 million (excluding program costs), and would also lead to an estimated 4451 additional opioid-free months, i.e. months without any opioid prescriptions.

Conclusion

The analysis found that efforts to change prescriber behavior can be cost effective, and further studies into the implementation of such policies are warranted.

Notes

Acknowledgements

The authors gratefully acknowledge the support of the National Institute for Healthcare Management (NIHCM) Foundation to the Principal Investigator, Ritu Agarwal.

Author Contributions

The interpretation and reporting of the results are the sole responsibility of the authors. Dr. Bjarnadottir acts as guarantor of the work presented in this paper. Dr. Bjarnadottir contributed to the study design, data extraction, interpretation of the analysis, and writing of the manuscript. Dr. Anderson contributed to the study design, data analysis, interpretation of the results, and writing of the manuscript. Dr. Prasad built the simulation model and ran the analysis, and contributed to the study design, data analysis, and writing of the manuscript. Dr. Agarwal contributed to the study design, interpretation of the results, and writing of the manuscript. Dr. Nelson conducted the data collection and the initial structure of the study dataset, and contributed to writing of the manuscript.

Compliance with Ethical Standards

Conflict of interest

Margret V. Bjarnadottir, David Anderson, Kislaya Prasad, Al Nelson, and Ritu Agarwal have no other conflicts of interest to declare.

Supplementary material

40273_2019_847_MOESM1_ESM.pdf (533 kb)
Supplementary material 1 (PDF 533 kb)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Robert H. Smith School of Business, Decision, Operations, and Information TechnologiesUniversity of MarylandCollege ParkUSA
  2. 2.School of Business, Management and OperationsVillanova UniversityVillanovaUSA
  3. 3.Division of Primary Care and Population Health, Department of Medicine, School of MedicineStanford UniversityStanfordUSA

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