Citation

Hastings JS, Howison M, Inman SE. Proc. Natl. Acad. Sci. U. S. A. 2020; ePub(ePub): ePub.

Copyright

(Copyright © 2020, National Academy of Sciences)

DOI

10.1073/pnas.1905355117

PMID

31937665

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.

Language: en

Keywords

machine learning; administrative data; evidence-based policy; opioids; predictive modeling