Predicting High-Risk Opioid Prescriptions Before they are Given

Justine Hastings, Mark Howison and Sarah E. Inman

No 25791, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Misuse of prescription opioids is a leading cause of premature death in the United States. We use new state government administrative data and machine learning methods to examine whether the risk of future opioid dependence, abuse, or poisoning can be predicted in advance of an initial opioid prescription. Our models accurately predict these outcomes and identify particular prior non-opioid prescriptions, medical history, incarceration, and demographics as strong predictors. Using our model estimates, we simulate a hypothetical policy which restricts new opioid prescriptions to only those with low predicted risk. The policy’s potential benefits likely outweigh costs across demographic subgroups, even for lenient definitions of “high risk.” Our findings suggest new avenues for prevention using state administrative data, which could aid providers in making better, data-informed decisions when weighing the medical benefits of opioid therapy against the risks.

JEL-codes: D61 I1 I12 I18 Z18 (search for similar items in EconPapers)
Date: 2019-04
New Economics Papers: this item is included in nep-big and nep-hea
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Published as Justine S. Hastings & Mark Howison & Sarah E. Inman, 2020. "Predicting high-risk opioid prescriptions before they are given," Proceedings of the National Academy of Sciences, vol 117(4), pages 1917-1923.

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Journal Article: Predicting high-risk opioid prescriptions before they are given (2020)
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