鉴别开阿片类镇痛药止痛者的方法比较

Reem Farjo, Hsou-Mei Hu, Jennifer F Waljee, Michael J Englesbe, Chad M Brummett, Mark C Bicket
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摘要

导言:尽管从报销数据中识别阿片类药物处方有助于为最佳实践提供依据,但尚未有研究对某些识别阿片类药物处方的方法是否能产生更好的结果进行评估。我们比较了在具有全国代表性的大型数据库中识别阿片类药物处方的三种常用方法。方法 我们进行了一项回顾性队列研究,分析了 MarketScan、Optum 和医疗保险报销单,比较了三种阿片类药物分类方法:报销单数据库特定分类、美国疾病控制和预防中心 (CDC) 的国家药品代码 (NDC) 或过量预防参与网络 (OPEN) 的 NDC。主要结果是曲线下面积(AUC)的区分度,次要结果包括专家识别出但每种方法未识别出的阿片类药物处方数量。结果 所有方法的识别率都很高(AUC>0.99)。对于 MarketScan(n=70,162,157),CDC 列表中未识别的处方总数为 42,068 个(0.06%),数据库特定类别为 2,067,613 个(2.9%),OPEN 列表为 0 个(0%)。对于 Optum(n=61,554,852),CDC 列表中未识别的阿片类药物处方共计 9,774 个(0.02%),数据库特定类别中为 83,700 个(0.14%),OPEN 列表中为 0 个(0%)。在医疗保险报销单(n=92,781,299)中,CDC 文件未识别的阿片类药物处方总数为 8,694 张(0.01%),OPEN 列表为 0 张(0%)。讨论 本分析发现,使用 CDC 和 OPEN 的方法识别阿片类药物处方的效果与预先指定的特定数据库类别的效果相似且更优。总之,本研究表明,在调查索赔数据时,谨慎选择识别阿片类药物处方的方法非常重要。数据可能来自第三方,不对外公开。所有与研究相关的数据均包含在文章中或作为补充信息上传。本研究中的数据集(MarketScan、Optum 和医疗保险索赔)可通过这些第三方获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of methods to identify individuals prescribed opioid analgesics for pain
Introduction While identifying opioid prescriptions in claims data has been instrumental in informing best practises, studies have not evaluated whether certain methods of identifying opioid prescriptions yield better results. We compared three common approaches to identify opioid prescriptions in large, nationally representative databases. Methods We performed a retrospective cohort study, analyzing MarketScan, Optum, and Medicare claims to compare three methods of opioid classification: claims database-specific classifications, National Drug Codes (NDC) from the Centers for Disease Control and Prevention (CDC), or NDC from Overdose Prevention Engagement Network (OPEN). The primary outcome was discrimination by area under the curve (AUC), with secondary outcomes including the number of opioid prescriptions identified by experts but not identified by each method. Results All methods had high discrimination (AUC>0.99). For MarketScan (n=70,162,157), prescriptions that were not identified totalled 42,068 (0.06%) for the CDC list, 2,067,613 (2.9%) for database-specific categories, and 0 (0%) for the OPEN list. For Optum (n=61,554,852), opioid prescriptions not identified totalled 9,774 (0.02%) for the CDC list, 83,700 (0.14%) for database-specific categories, and 0 (0%) for the OPEN list. In Medicare claims (n=92,781,299), the number of opioid prescriptions not identified totalled 8,694 (0.01%) for the CDC file and 0 (0%) for the OPEN list. Discussion This analysis found that identifying opioid prescriptions using methods from CDC and OPEN were similar and superior to prespecified database-specific categories. Overall, this study shows the importance of carefully selecting the approach to identify opioid prescriptions when investigating claims data. Data may be obtained from a third party and are not publicly available. All data relevant to the study are included in the article or uploaded as supplementary information. Data sets in this study (MarketScan, Optum, and Medicare claims) are available through those third parties.
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