Schneeweiss Sebastian, Desai Rishi J, Ball Robert
Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02120, United States.
Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20993, United States.
Am J Epidemiol. 2025 Feb 5;194(2):315-321. doi: 10.1093/aje/kwae226.
Electronic health record (EHR) data are seen as an important source for pharmacoepidemiology studies. In the US health-care system, EHR systems often identify only fragments of patients' health information across the care continuum, including primary care, specialist care, hospitalizations, and pharmacy dispensing. This leads to unobservable information in longitudinal evaluations of medication effects, causing unmeasured confounding, misclassification, and truncated follow-up times. A remedy is to link EHR data with longitudinal health insurance claims data, which record all encounters during a defined enrollment period across all care settings. Here we evaluate EHR and claims data sources in 3 aspects relevant to etiological studies of medical products: data continuity, data granularity, and data chronology. Reflecting on the strengths and limitations of EHR and insurance claims data, it becomes obvious that they complement each other. The combination of both will improve the validity of etiological studies and expand the range of questions that can be answered. As the research community transitions towards a future state with access to large-scale combined EHR + claims data, we outline analytical templates to improve the validity and broaden the scope of pharmacoepidemiology studies in the current environment where EHR data are available only for a subset of patients with claims data. This article is part of a Special Collection on Pharmacoepidemiology.
电子健康记录(EHR)数据被视为药物流行病学研究的重要来源。在美国医疗保健系统中,EHR系统通常只能识别患者在整个医疗连续过程中的部分健康信息,包括初级保健、专科护理、住院治疗和药房配药。这导致在药物疗效的纵向评估中出现无法观察到的信息,从而造成未测量的混杂因素、错误分类以及随访时间缩短。一种补救方法是将EHR数据与纵向健康保险理赔数据相链接,后者记录了在规定参保期内所有医疗机构的所有诊疗情况。在此,我们从与医疗产品病因学研究相关的三个方面评估EHR和理赔数据源:数据连续性、数据粒度和数据时间顺序。通过思考EHR和保险理赔数据的优势与局限性,很明显它们相互补充。两者结合将提高病因学研究的有效性,并扩大可回答问题的范围。随着研究界向能够获取大规模EHR + 理赔数据的未来状态转变,我们概述了分析模板,以在当前仅能获取部分患者EHR数据和理赔数据的环境中提高药物流行病学研究的有效性并拓宽其范围。本文是药物流行病学特刊的一部分。