科研之友
微信
新浪微博
Facebook
分享链接
摘要
Background: Granular clinical and laboratory data available in electronic health record (EHR) databases provide researchers the opportunity to conduct investigations that would not be possible in insurance claims databases; however, for pharmacoepidemiology studies, accurate classification of medication exposure is critical. Objective: The aim of this study was to evaluate the validity of classifying medication exposure using EHR prescribing (EHR-Rx) data. Methods: We conducted a retrospective cohort study among patients with linked claims and EHR data in OptumLabs (TM) Data Warehouse. The agreement between EHR-Rx data and pharmacy claims (PC-Rx) data (for 40 medications) was determined using the positive predictive value (PPV) and medication possession ratio (MPR)-calculated in 1- and 12-month medication exposure periods (MEPs). Secondary analyses were restricted to incident vs prevalent EHR-Rxs, age >= 65 vs < 65, white vs black race, males vs females, and number of EHR-Rxs. Results: The validity metrics varied substantially among the 40 medications assessed. Across all medications, the period PPV and MPR were 62% and 63% in the 1-month MEP. They were 78% and 43% in the 12-month MEP. Overall, PPV and MPR were higher for patients with a prevalent EHR-Rx and age < 65. Conclusions: Despite substantial variability among different medications, there was very good agreement between EHR-Rx data and PC-Rx data. To maximize the validity of classifying medication exposure with EHR prescribing data, researchers may consider using longer MEPs (eg, 12 months) and potentially require multiple EHR-Rxs to classify baseline medication exposure.
- 出版日期2017-8
- 单位rutgers
