通过模拟来评估在测量停电暴露中的偏差
Heather McBrien1, Daniel Mork2, Marianthi-Anna Kioumourtzoglou1
1Department of Environmental Health Sciences, Columbia Mailman School of Public Health, New York, New York.
Environmental epidemiology (Philadelphia, Pa.)
|June 16, 2025
概括
研究人员可以使用新的停电数据进行健康研究,但缺失的数据和未知的停电持续时间可能会导致偏差. 仔细分析和持续时间的选择可以为停电健康结果提供可靠的结果.
科学领域:
- 环境流行病学环境流行病学
- 公共卫生 数据科学 数据科学
背景情况:
- 自2020年以来的新的国家停电数据可以帮助流行病学研究.
- 挑战包括缺少数据和未知的健康相关停电时间.
- 由于数据的限制,偏见可能会影响研究结果.
研究的目的:
- 评估现有数据集是否能够产生可用的效果估计,尽管缺少数据.
- 从对健康相关的停电持续时间的错误假设中量化偏差.
主要方法:
- 模拟的县级研究使用PowerOutage.us数据.
- 估计了每日停电暴露对住院率的影响.
- 测量偏差来自错误的持续时间假设和缺失的暴露数据.
主要成果:
- 低估与健康相关的停电持续时间导致了对零的大幅偏差 (平均偏差:-64.7%).
- 过高估计的持续时间导致偏差较小 (平均偏差: -6.7%).
- 在80%的县缺少80%以上的数据,严重偏差结果向零 (平均偏差:54.4%).
结论:
- 在使用可用的停电数据时,可能会有一些偏差.
- 敏感性分析和仔细的持续时间选择可以减轻偏差.
- 研究人员可以产生低偏差估计的停电健康结果.
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