如何在COVID-19调查中减轻选择偏见:来自五个国家队列的证据
Martina K Narayanan1, Brian Dodgeon2, Michail Katsoulis3
1Centre for Longitudinal Studies, UCL Social Research Institute, University College London, London, UK. m.narayanan@ucl.ac.uk.
European journal of epidemiology
|November 20, 2024
概括
为了应对COVID-19期间的调查非响应偏见,研究人员使用了现有的英国队列数据. 权重和归算方法成功恢复了样本的代表性,改善了流行病研究的数据质量.
科学领域:
- 社会科学 社会科学 社会科学
- 流行病学 流行病学
- 统计 统计 统计 统计
背景情况:
- 调查不响应是一个重大挑战,由COVID-19社会距离措施加剧.
- 不同的受访者和非受访者特征可以在研究结果中引入关键偏见.
- 纵向队列研究提供了丰富的流行前数据,有价值的解决非响应偏差.
研究的目的:
- 评估使用历史队列数据的有效性,以减少COVID-19调查中的偏见.
- 通过减轻选择偏差,恢复大流行时代调查中的样本代表性.
- 提高对大流行中长期影响的研究可靠性.
主要方法:
- 在COVID-19大流行期间利用了五项英国纵向队列研究 (NSHD,NCDS,BCS70,Next Steps,MCS) 的数据.
- 应用非响应加权和多重归算技术来解决已识别的偏差.
- 比较了受访者与非受访者的响应率和特征.
主要成果:
- COVID-19调查响应率低于之前的波,特别是在年轻的队列中.
- 检测到有系统的非响应偏差,有利于来自较高社会阶层和具有更强的儿童认知能力的受访者.
- 非响应权重和多重归算显著减少了父母社会阶级和儿童认知能力的偏见.
结论:
- 在现有的纵向队列研究中嵌入的调查有利于在流行病期间减轻选择偏差.
- 应用的统计方法成功恢复了样本的代表性,提高了数据质量.
- 这种方法提高了调查大流行病持续影响的研究的可靠性.
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