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Opioid Use Disorder and Prescribed Opioid Regimens: Evidence from Commercial and Medicaid Claims, 2005–2015

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Abstract

Introduction

In response to the US opioid crisis, interventions are being implemented to lower opioid prescribing to reduce opioid misuse and overdose. As opioid prescribing falls, opioid misuse may shift from prescriptions to other, possibly illicit, sources. We examined how the percentage of patients with an opioid use disorder (OUD) diagnosis in a given year without a current opioid prescription changed over a decade among commercially insured enrollees and Medicaid beneficiaries. We also examined how the percentages differed by enrollee demographic factors.

Methods

We used commercial and Medicaid claims from the IBM MarketScan® databases from 2005 to 2015 to identify enrollees with and without current opioid prescriptions who have been diagnosed with OUD. We measured the percentage of enrollees with OUD without a current opioid prescription by year and demographic factors.

Results

We identified 99,396 enrollee-years with OUD covered by commercial insurance and 60,492 enrollee-years with OUD covered by Medicaid. Among enrollees with OUD, the percentage without a current opioid prescription increased from 37% in 2005 to 49% in 2012 before falling back to 39% in 2015 in the commercial population, and increased from 32% in 2005 to 38% in 2015 in the Medicaid population. Differences in percentages were observed by age, sex, race, and region, particularly among young people where 70 to 89% had OUD without a current prescription.

Conclusions

Most enrollees with OUD in the data had current opioid prescriptions, suggesting that continuing efforts to reduce misuse of prescribed opioids among patients with prescriptions may be effective. However, a substantial percentage of enrollees with OUD may be obtaining opioids via other, likely illegitimate, channels, particularly younger people, which suggests an opportunity for targeted efforts to reduce opioid diversion.

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Acknowledgments

The authors wish to acknowledge Cory Pack of Truven Health Analytics, an IBM Company, for programming support.

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Correspondence to Mir M. Ali.

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None. This paper has not been subject to the Congressional Budget Office’s regular review and editing process. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Congressional Budget Office, Office of the Assistant Secretary for Planning & Evaluation, or the US Department of Health & Human Services.

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Appendix

Appendix

Table 2 Diagnosis codes to identify opioid use disorders: International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM).
Table 3 Attrition showing final sample for 2005, 2010, and 2015.
Fig. 8
figure 8

Percent of enrollees with a distinct instance of an OUD, by insurance status. Shaded regions around lines represent 95% pointwise confidence bands.

Fig. 9
figure 9

Percent of enrollees with a distinct instance of an OUD, by insurance status and age. Shaded regions around lines represent 95% pointwise confidence bands.

Fig. 10
figure 10

Percent of commercial enrollees with an opioid use disorder lacking a current opioid prescription, by sex and relationship to employee. Shaded regions around lines represent 95% pointwise confidence bands.

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Ali, M.M., Cutler, E., Mutter, R. et al. Opioid Use Disorder and Prescribed Opioid Regimens: Evidence from Commercial and Medicaid Claims, 2005–2015. J. Med. Toxicol. 15, 156–168 (2019). https://doi.org/10.1007/s13181-019-00715-0

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  • DOI: https://doi.org/10.1007/s13181-019-00715-0

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