Dataset Information

Historically High Excess Mortality During the COVID-19 Pandemic in Switzerland, Sweden, and Spain.

ABSTRACT:

Background

Excess mortality quantifies the overall mortality impact of a pandemic. Mortality data have been accessible for many countries in recent decades, but few continuous data have been available for longer periods.

Objective

To assess the historical dimension of the COVID-19 pandemic in 2020 for 3 countries with reliable death count data over an uninterrupted span of more than 100 years.

Design

Observational study.

Setting

Switzerland, Sweden, and Spain, which were militarily neutral and not involved in combat during either world war and have not been affected by significant changes in their territory since the end of the 19th century.

Participants

Complete populations of these 3 countries.

Measurements

Continuous series of recorded deaths (from all causes) by month from the earliest available year (1877 for Switzerland, 1851 for Sweden, and 1908 for Spain) were jointly modeled with annual age group-specific death and total population counts using negative binomial and multinomial models, which accounted for temporal trends and seasonal variability of prepandemic years. The aim was to estimate the expected number of deaths in a pandemic year for a nonpandemic scenario and the difference in observed and expected deaths aggregated over the year.

Results

In 2020, the number of excess deaths recorded per 100 000 persons was 100 (95% credible interval [CrI], 60 to 135) for Switzerland, 75 (CrI, 40 to 105) for Sweden, and 155 (CrI, 110 to 195) for Spain. In 1918, excess mortality was 6 to 7 times higher. In all 3 countries, the peaks of monthly excess mortality in 2020 were greater than most monthly excess mortality since 1918, including many peaks due to seasonal influenza and heat waves during that period.

Limitation

Historical vital statistics might be affected by minor completeness issues before the beginning of the 20th century.

Conclusion

In 2020, the COVID-19 pandemic led to the second-largest infection-related mortality disaster in Switzerland, Sweden, and Spain since the beginning of the 20th century.

Primary funding source

Foundation for Research in Science and the Humanities at the University of Zurich, Swiss National Science Foundation, and National Institute of Allergy and Infectious Diseases.

SUBMITTER: Staub K

PROVIDER: S-EPMC8803137 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Excess mortality is an important measure of the scale of the coronavirus-2019 pandemic. It includes both deaths caused directly by the pandemic, and deaths caused by the unintended consequences of containment such as delays to accessing care or postponements of healthcare provision in the population. In 2020 and 2021, in England, multiple groups have produced measures of excess mortality during the pandemic. This paper describes the data and methods used in five different approaches to estimating excess mortality and compares their estimates.The fundamental principles of estimating excess mortality are described, as well as the key commonalities and differences between five approaches. Two of these are based on the date of registration: a quasi-Poisson model with offset and a 5-year average; and three are based on date of occurrence: a Poisson model without offset, the European monitoring of excess mortality model and a synthetic controls model. Comparisons between estimates of excess mortality are made for the period March 2020 through March 2021 and for the two waves of the pandemic that occur within that time-period.Model estimates are strikingly similar during the first wave of the pandemic though larger differences are observed during the second wave. Models that adjusted for reduced circulation of winter infection produced higher estimates of excess compared with those that did not. Models that do not adjust for reduced circulation of winter infection captured the effect of reduced winter illness as a result of mobility restrictions during the period. None of the estimates captured mortality displacement and therefore may underestimate excess at the current time, though the extent to which this has occurred is not yet identified. Models use different approaches to address variation in data availability and stakeholder requirements of the measure. Variation between estimates reflects differences in the date of interest, population denominators and parameters in the model relating to seasonality and trend.