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两种互补方法估计超额死亡率:瑞士 2022 年的案例。

Two complementary approaches to estimate an excess of mortality: The case of Switzerland 2022.

机构信息

Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland.

出版信息

PLoS One. 2023 Aug 15;18(8):e0290160. doi: 10.1371/journal.pone.0290160. eCollection 2023.

DOI:10.1371/journal.pone.0290160
PMID:37582109
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10426989/
Abstract

OBJECTIVE

During the COVID-19 pandemic, excess mortality has generally been estimated comparing overall mortality in a given year with either past mortality levels or past mortality trends, with different results. Our objective was to illustrate and compare the two approaches using mortality data for Switzerland in 2022, the third year of the COVID-19 pandemic.

METHODS

Using data from the Swiss Federal Statistical Office, standardized mortality rates and life expectancies in 2022 were compared with those of the last pre-pandemic year 2019 (first approach), as well as with those that would be expected if the pre-pandemic downward trend in mortality had continued during the pandemic (second approach). The pre-pandemic trend was estimated via a Poisson log-linear model on age-specific mortality over the period 2010-19.

RESULTS

Using the first approach, we estimated in Switzerland in 2022 an excess mortality of 2.6% (95%CI: 1.0%-4.1%) for men and 2.5% (95%CI: 1.0%-4.0%) for women, while the excess mortality rose to 8.4% (95%CI: 6.9%-9.9%) for men and 6.0% (95%CI: 4.6%-7.5%) for women using the second approach. Age classes over 80 were the main responsible for the excess mortality in 2022 for both sexes using the first approach, although a significant excess mortality was also found in most age classes above 30 using the second approach. Life expectancy in 2022 has been reduced by 2.7 months for men and 2.4 months for women according to the first approach, whereas it was reduced by respectively 8.8 and 6.0 months according to the second approach.

CONCLUSIONS

The excess mortality and loss of life expectancy in Switzerland in 2022 are around three times greater if the pre-pandemic trend is taken into account than if we simply compare 2022 with 2019. These two different approaches, one being more speculative and the other more factual, can also be applied simultaneously and provide complementary results. In Switzerland, such a dual-approach strategy has shown that the pre-pandemic downward trend in mortality is currently halted, while pre-pandemic mortality levels have largely been recovered by 2022.

摘要

目的

在 COVID-19 大流行期间,通常通过将特定年份的总死亡率与过去的死亡率水平或过去的死亡率趋势进行比较来估计超额死亡率,结果有所不同。我们的目的是使用 2022 年瑞士的死亡率数据来说明和比较这两种方法,这是 COVID-19 大流行的第三年。

方法

使用瑞士联邦统计局的数据,将 2022 年的标准化死亡率和预期寿命与前一年(2019 年,即大流行前的最后一年)进行比较(第一种方法),以及与如果大流行期间继续保持大流行前死亡率下降趋势(第二种方法)所预期的死亡率进行比较。使用特定于年龄的死亡率的泊松对数线性模型来估计大流行前的趋势,该模型的时间段为 2010-19 年。

结果

使用第一种方法,我们估计瑞士 2022 年男性的超额死亡率为 2.6%(95%CI:1.0%-4.1%),女性为 2.5%(95%CI:1.0%-4.0%),而使用第二种方法,男性的超额死亡率上升至 8.4%(95%CI:6.9%-9.9%),女性为 6.0%(95%CI:4.6%-7.5%)。对于男性,80 岁以上年龄组是导致 2022 年超额死亡率的主要原因,而对于女性,使用第二种方法,30 岁以上的大多数年龄组也发现了显著的超额死亡率。根据第一种方法,男性的预期寿命在 2022 年减少了 2.7 个月,女性减少了 2.4 个月,而根据第二种方法,男性减少了 8.8 个月,女性减少了 6.0 个月。

结论

如果考虑大流行前的趋势,瑞士 2022 年的超额死亡率和预期寿命损失是单纯将 2022 年与 2019 年进行比较的三倍左右。这两种方法,一种更具推测性,另一种更具事实性,也可以同时使用,并提供互补的结果。在瑞士,这种双重方法策略表明,死亡率的大流行前下降趋势目前已经停止,而到 2022 年,大流行前的死亡率水平已经基本恢复。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/10426989/563549cbaca5/pone.0290160.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/10426989/f2e46a70546b/pone.0290160.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/10426989/1fb9663e2816/pone.0290160.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/10426989/88c2f51394fd/pone.0290160.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/10426989/563549cbaca5/pone.0290160.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/10426989/f2e46a70546b/pone.0290160.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/10426989/1fb9663e2816/pone.0290160.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/10426989/88c2f51394fd/pone.0290160.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66f3/10426989/563549cbaca5/pone.0290160.g004.jpg

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