| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w25791 |
| 来源ID | Working Paper 25791 |
| Predicting High-Risk Opioid Prescriptions Before they are Given | |
| Justine S. Hastings; Mark Howison; Sarah E. Inman | |
| 发表日期 | 2019-04-29 |
| 出版年 | 2019 |
| 语种 | 英语 |
| 摘要 | 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. |
| 主题 | Microeconomics ; Welfare and Collective Choice ; Health, Education, and Welfare ; Health ; Other ; Culture |
| URL | https://www.nber.org/papers/w25791 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/583464 |
| 推荐引用方式 GB/T 7714 | Justine S. Hastings,Mark Howison,Sarah E. Inman. Predicting High-Risk Opioid Prescriptions Before they are Given. 2019. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w25791.pdf（519KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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