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.

中文翻译：

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在给予高风险阿片类药物处方之前进行预测。

滥用处方阿片类药物是美国过早死亡的主要原因。我们使用州政府行政数据和机器学习方法来检查是否可以在初始阿片类药物处方之前预测未来阿片类药物依赖、滥用或中毒的风险。我们的模型准确预测这些结果，并确定特定的既往非阿片类药物处方、病史、监禁和人口统计数据作为强有力的预测因素。根据我们的估计，我们模拟了一项假设政策，该政策将新的阿片类药物处方仅限于预测风险较低的患者。即使对于“高风险”的宽松定义，该政策的潜在收益也可能超过人口亚群体的成本。我们的研究结果提出了利用国家行政数据进行预防的新途径，这可以帮助提供者在权衡阿片类药物治疗的医疗益处和风险时做出更好的、基于数据的决策。