对因传染病干预导致的避免和可避免结果分析的因果估计
Katherine M Jia1, Christopher B Boyer1, Jacco Wallinga2,3
1From the Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.
Epidemiology (Cambridge, Mass.)
|January 24, 2025
概括
估计疫苗影响需要正式规范避免和可避免的结果. 假设直接影响是疫苗总体影响的下限,当疗效减弱或参数随着时间的推移而改变时,假设就失败了.
科学领域:
- 流行病学 流行病学
- 因果推理因果推理
- 公共卫生建模公共卫生建模
背景情况:
- 在COVID-19大流行期间量化疫苗接种活动对公共卫生的影响涉及估计避免和可避免的结果.
- 这些分析的具体估计值以前没有正式制定,它们与干扰下的直接,间接,总和整体因果作用的关系尚不清楚.
研究的目的:
- 在因果推理框架内正式确定疫苗避免和可避免结果的估计值.
- 调查假设直接疫苗效应作为总体疫苗影响的下限.
- 确定下限假设成立或失败的条件.
主要方法:
- 使用潜在结果标记来调整直接和整体影响,用于对避免和可避免结果的分析.
- 采用受感染恢复死亡 (SIRD) 模型,按疫苗接种状态分层.
- 模拟场景,疫苗的疗效下降,传播或死亡的参数随时间变化而变化.
主要成果:
- 假设直接的疫苗影响是总体影响的下限,当疫苗的疗效减弱或随着时间的推移传播/致命性参数增加时,这种假设失败了.
- 这一下限仅在最简单的情况下保持,即疫苗的疗效,传播和死亡率保持不变.
- 在常见违反时间不变假设的情况下证明了下限的失败.
结论:
- 常见的疫苗影响下限假设不是普遍有效的,在现实条件下可能会失败,例如免疫力下降或病原体动态的演变.
- 仅仅估计疫苗的直接影响可能不足以捕捉整体公共卫生影响,需要考虑间接影响.
- 在分析真实世界的疫苗数据时,仔细检查间接影响至关重要,以避免低估或误解整体影响.
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