Assessing excess mortality in times of pandemics based on principal component analysis of weekly mortality data-the case of COVID-19

Patrizio Vanella; Ugofilippo Basellini; Berit Lange

doi:10.1186/s41118-021-00123-9

Assessing excess mortality in times of pandemics based on principal component analysis of weekly mortality data-the case of COVID-19

Genus. 2021;77(1):16. doi: 10.1186/s41118-021-00123-9. Epub 2021 Aug 9.

Authors

Patrizio Vanella^{1

2}, Ugofilippo Basellini^{3

4}, Berit Lange^{1

5}

Affiliations

¹ Department of Epidemiology, Helmholtz Centre for Infection Research (HZI), Inhoffenstr. 7, DE-38124 Brunswick, Germany.
² Chair of Empirical Methods in Social Science and Demography, University of Rostock, Ulmenstr. 69, DE-18057 Rostock, Germany.
³ Laboratory of Digital and Computational Demography, Max Planck Institute for Demographic Research (MPIDR), Konrad-Zuse-Str. 1, DE-18057 Rostock, Germany.
⁴ Institut National d'Etudes Démographiques (INED), 9 cours des Humanités, FR-93322 Aubervilliers, Cedex, France.
⁵ German Center for Infection Research (DZIF), Inhoffenstr. 7, DE-38124 Brunswick, Germany.

Abstract

The COVID-19 outbreak has called for renewed attention to the need for sound statistical analyses to monitor mortality patterns and trends over time. Excess mortality has been suggested as the most appropriate indicator to measure the overall burden of the pandemic in terms of mortality. As such, excess mortality has received considerable interest since the outbreak of COVID-19 began. Previous approaches to estimate excess mortality are somewhat limited, as they do not include sufficiently long-term trends, correlations among different demographic and geographic groups, or autocorrelations in the mortality time series. This might lead to biased estimates of excess mortality, as random mortality fluctuations may be misinterpreted as excess mortality. We propose a novel approach that overcomes the named limitations and draws a more realistic picture of excess mortality. Our approach is based on an established forecasting model that is used in demography, namely, the Lee-Carter model. We illustrate our approach by using the weekly age- and sex-specific mortality data for 19 countries and the current COVID-19 pandemic as a case study. Our findings show evidence of considerable excess mortality during 2020 in Europe, which affects different countries, age, and sex groups heterogeneously. Our proposed model can be applied to future pandemics as well as to monitor excess mortality from specific causes of death.

Keywords: COVID-19 pandemic; Cross-country mortality trends; Demography; Epidemiology; Excess mortality assessment; Monte Carlo simulation; Mortality forecasting; Principal component analysis; Stochasticity; Time series analysis.