Predicting high-risk opioid prescriptions before they are given

Justine Hastings, Mark Howison and Sarah E. Inman
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Mark Howison: Research Improving People’s Lives, Providence, RI02903; Watson Institute for International and Public Affairs, Brown University, Providence, RI02912
Sarah E. Inman: Research Improving People’s Lives, Providence, RI02903; School of International and Public Affairs, Columbia University, New York, NY10027

Proceedings of the National Academy of Sciences, 2020, vol. 117, issue 4, 1917-1923

Abstract: Misuse of prescription opioids is a leading cause of premature death in the United States. We use 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 nonopioid prescriptions, medical history, incarceration, and demographics as strong predictors. Using our 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.

Keywords: opioids; evidence-based policy; predictive modeling; machine learning; administrative data (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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Related works:
Working Paper: Predicting High-Risk Opioid Prescriptions Before they are Given (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:nas:journl:v:117:y:2020:p:1917-1923

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