在英国全国医疗保健队列中为膨胀的成年人口分母进行校正:数据库分析研究研究
Sudhir Venkatesan1, Mark Joy2, Gavin Jamie2
1BPM Evidence Statistics, BioPharmaceuticals Medical, AstraZeneca, Cambridge, United Kingdom.
JMIR public health and surveillance
|October 27, 2025
概括
不活跃的健康记录会膨胀电子健康数据库. 一种权重方法纠正了英格兰国家卫生服务 (NHS) 数据中的人口差异,减轻了流行病学研究中的选择偏差.
科学领域:
- 流行病学 流行病学
- 医疗信息学 医疗信息学
- 人口健康 人口健康
背景情况:
- 电子健康数据库对于流行病学研究至关重要.
- 不活跃的患者记录是一个挑战,看起来没有暴露,没有结果.
- 随着COVID-19的爆发,人们需要解决像英格兰国家卫生服务 (NHS) 这样的大型健康数据集中的不活跃记录.
研究的目的:
- 为了比较通用实践中成年人口的大小,为流行病规划和研究 (GDPPR) 提取服务数据与人口普查估计.
- 应用一种方法来纠正因不活跃的健康记录造成的差异.
主要方法:
- 将GDPPR中的成年患者人数与英国国家统计局 (ONS) 人口普查数据进行比较.
- 采用加权方法,对自2019年1月以来没有最近的医疗保健活动的个人进行下加权.
主要成果:
- GDPPR最初显示,有61,194,033名注册患者的过度代表性,相比之下,ONS的估计为56,550,138.
- 经过脱重复和调整后,分析了46,835,968名成年人,其中2.4%因不活动而下调.
- 权重方法显著减少了NHS和ONS人口数据之间的差异.
结论:
- 在GDPPR和人口普查人口估计之间存在显著差异,因社会人口统计因素而有所不同.
- 基于权重的方法有效地纠正了大型健康数据集中膨胀的分母.
- 如果不对不活跃的记录进行纠正,可能会在使用医疗保健数据的流行病学研究中引入选择偏差.
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