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2009-2021 年多元宇宙分析得出的超额死亡估计数。

Excess death estimates from multiverse analysis in 2009-2021.

机构信息

Department of Structural Biology, Stanford University, Stanford, CA, 94305, USA.

Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, 201210, China.

出版信息

Eur J Epidemiol. 2023 Nov;38(11):1129-1139. doi: 10.1007/s10654-023-00998-2. Epub 2023 Apr 12.

DOI:10.1007/s10654-023-00998-2
PMID:37043153
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10090741/
Abstract

Excess death estimates have great value in public health, but they can be sensitive to analytical choices. Here we propose a multiverse analysis approach that considers all possible different time periods for defining the reference baseline and a range of 1 to 4 years for the projected time period for which excess deaths are calculated. We used data from the Human Mortality Database on 33 countries with detailed age-stratified death information on an annual basis during the period 2009-2021. The use of different time periods for reference baseline led to large variability in the absolute magnitude of the exact excess death estimates. However, the relative ranking of different countries compared to others for specific years remained largely unaltered. The relative ranking of different years for the specific country was also largely independent of baseline. Averaging across all possible analyses, distinct time patterns were discerned across different countries. Countries had declines between 2009 and 2019, but the steepness of the decline varied markedly. There were also large differences across countries on whether the COVID-19 pandemic years 2020-2021 resulted in an increase of excess deaths and by how much. Consideration of longer projected time windows resulted in substantial shrinking of the excess deaths in many, but not all countries. Multiverse analysis of excess deaths over long periods of interest can offer an approach that better accounts for the uncertainty in estimating expected mortality patterns, comparative mortality trends across different countries, and the nature of observed mortality peaks.

摘要

超额死亡估计在公共卫生方面具有重要价值,但它们可能对分析选择敏感。在这里,我们提出了一种多宇宙分析方法,该方法考虑了定义参考基线的所有可能不同时间段,以及计算超额死亡的预测时间段为 1 到 4 年的范围。我们使用了来自人类死亡率数据库的数据,该数据库包含了 33 个国家在 2009-2021 年期间按年度详细的年龄分层死亡信息。使用不同的时间段作为参考基线会导致精确超额死亡估计的绝对值发生很大变化。然而,与其他国家相比,特定年份的不同国家的相对排名基本保持不变。特定国家不同年份的相对排名也基本独立于基线。对所有可能的分析进行平均,不同国家之间存在明显的时间模式差异。各国在 2009 年至 2019 年期间有所下降,但下降的幅度差异很大。在 2020 年至 2021 年的 COVID-19 大流行期间,是否会导致超额死亡增加以及增加多少,各国之间也存在很大差异。考虑更长的预测时间窗口会导致许多国家(但不是所有国家)的超额死亡大幅减少。对长期感兴趣的超额死亡进行多宇宙分析可以提供一种方法,更好地考虑到估计预期死亡率模式、不同国家之间的比较死亡率趋势以及观察到的死亡率峰值的性质的不确定性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f49c/10663196/fba2f01b2a4a/10654_2023_998_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f49c/10663196/58e054e68db8/10654_2023_998_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f49c/10663196/fba2f01b2a4a/10654_2023_998_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f49c/10663196/58e054e68db8/10654_2023_998_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f49c/10663196/fba2f01b2a4a/10654_2023_998_Fig2_HTML.jpg

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