The Use of Primary Care Big Data in Understanding the Pharmacoepidemiology of COVID-19: A Consensus Statement From the COVID-19 Primary Care Database Consortium
The Use of Primary Care Big Data in Understanding the Pharmacoepidemiology of COVID-19: A Consensus Statement From the COVID-19 Primary Care Database Consortium
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DOI：
10.1370/afm.2658
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发表时间：
2021-03-01
影响因子：
4.4
通讯作者：
Hippisley-Cox, Julia
中科院分区：
文献类型：
--
作者：
Dambha-Miller, Hajira;Griffin, Simon J.;Hippisley-Cox, Julia
使用包含数百万份基层医疗记录的大数据为快速研究提供了机会，有助于在2019冠状病毒病（COVID-19）大流行的第一波和后续浪潮中为患者护理和政策决策提供信息。美国收集的初级保健数据以前曾用于国家大流行监测，量化暴露与结果之间的关联，识别高风险人群，并检查大规模干预措施的效果，但对于如何有效地为COVID-19研究进行或报告这些数据，尚未达成共识。2020年4月成立了COVID-19初级保健数据库联盟，其研究人员正在进行COVID-19项目，这些项目涉及英国超过4，000万份初级保健记录的重叠数据集，这些记录与公共卫生、二级保健和生命状态记录有不同的联系。该共识协议旨在促进方法学方法的透明度和严谨性，以及定义和报告与COVID-19药物流行病学相关的病例、暴露、混杂因素、分层变量和结局的一致性。这将有助于对大流行期间和之后的研究进行比较、验证和荟萃分析。
The use of big data containing millions of primary care medical records provides an opportunity for rapid research to help inform patient care and policy decisions during the first and subsequent waves of the coronavirus disease 2019 (COVID-19) pandemic. Routinely collected primary care data have previously been used for national pandemic surveillance, quantifying associations between exposures and outcomes, identifying high risk populations, and examining the effects of interventions at scale, but there is no consensus on how to effectively conduct or report these data for COVID-19 research. A COVID-19 primary care database consortium was established in April 2020 and its researchers have ongoing COVID-19 projects in overlapping data sets with over 40 million primary care records in the United Kingdom that are variously linked to public health, secondary care, and vital status records. This consensus agreement is aimed at facilitating transparency and rigor in methodological approaches, and consistency in defining and reporting cases, exposures, confounders, stratification variables, and outcomes in relation to the pharmacoepidemiology of COVID-19. This will facilitate comparison, validation, and meta-analyses of research during and after the pandemic.