评估数据标准化对现实世界数据的影响
Elizabeth M Garry1, Aidan Baglivo1, Priya Govil1
1Aetion, Inc., New York, New York, USA.
Pharmacoepidemiology and drug safety
|August 5, 2025
概括
将医疗保健数据标准化为Sentinel通用数据模型,可以改进数据,但可能会降低计数和分布. 了解这些影响对于准确的队列选择和分析COVID-19患者至关重要.
科学领域:
- 健康数据科学健康数据科学
- 生物统计学 生物统计学
- 流行病学 流行病学
背景情况:
- 管理医疗保健数据需要标准化,以便在多个地点进行研究.
- 哨兵计划使用一个共同的数据模型进行分布式研究.
- 标准化对队列特征和结果措施产生影响.
研究的目的:
- 评估将管理医疗保健数据标准化到Sentinel共同数据模型的效果.
- 评估对COVID-19患者队列选择和描述性发现的影响.
主要方法:
- 使用原生和标准化的HealthVerity数据,比较了门诊COVID-19诊断的患者 (2021年1月至2022年12月).
- 分析了队列消耗,样本大小,患者人口统计和医疗保健资源利用情况.
- 检查了COVID-19诊断后所选条件的发生率.
主要成果:
- 标准化的队列较小 (164,445 vs. 198,317),但年龄和性别分布相似.
- 由于将"其他"映射到"未知/缺失"的原因,种族分布有所不同.
- 在标准化队列中,并发症和SARS-CoV-2测试率略低一些;遭遇数量和发病率 (例如肝毒性) 显着较低.
结论:
- 数据标准化通过减少重复和错误来完善数据,可能降低异常值.
- 与原生数据相比,标准化可能会导致某些变量的分布和计数较低.
- 了解标准化的影响对于确保数据适合研究使用至关重要.
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