空气污染和健康的队列研究中的潜在过度调整偏差:一项方法论研究
Han Luo1, Yinyan Gao1, Weijia Xu2
1Department of Epidemiology and Biostatistics, Xiangya School of Public Health, Central South University, Changsha, China.
International journal of hygiene and environmental health
|February 11, 2026
概括
空气污染健康研究中的过度调整是常见的,可能会对结果产生偏见. 使用定向非循环图 (DAG) 可以显著减少这个问题,提高研究有效性.
科学领域:
- 环境健康 环境健康
- 流行病学 流行病学
- 因果推理因果推理
背景情况:
- 混调整对于环境健康研究的因果推断至关重要.
- 过度调整,或过度的统计调整,可以在因果效应估计中引入偏差.
- 这个问题很普遍,但在长期空气污染暴露和健康结果的研究中没有得到充分的解决.
研究的目的:
- 调查目前在长期空气污染暴露和健康研究中过度调整的现行做法.
- 评估过度调整在高影响环境健康研究中的普遍性.
- 评估定向非循环图 (DAG) 在缓解过度调整方面的作用.
主要方法:
- 对空气污染和健康的队列研究进行PubMed (2021年1月至2023年10月) 的系统搜索.
- 包括在具有影响力的期刊上发表的研究.
- 通过修改的离合性原因标准识别潜在的过度调整.
- 提取数据的描述性分析,包括混调整实践和DAG的使用.
主要成果:
- 包括175项研究;69.7%显示潜在的过度调整.
- 只有14.9%的研究使用定向环形图 (DAG) 进行变量选择.
- 过度调整通常是由于调整了诸如体重指数 (56.0%) 和高血压 (14.3%) 等调解媒介.
- 使用DAG的研究显示过度调整率明显较低 (42.3%与74.5%相比).
结论:
- 相当多的空气污染和健康研究显示了潜在的过度调整.
- 使用DAG进行混选择并不常见,但与减少过度调整有关.
- 正确应用DAG对于提高环境健康研究中因果推理的有效性至关重要.
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