Abstract

(Excess) mortality and years of life lost are important measures of health risks from the Corona pandemic. The aim of this paper was to identify methodological factors that affect the calculation of mortality and further to point out possible misinterpretations of years of life lost. Standardized mortality ratios (SMRs) can be used to compare mortalities (e. g., an SMR of 1.015 means excess mortality of 1.5%, an SMR of 0.990 means that mortality is reduced by 1.0%). In this study, SMRs as a measure of association for mortality in Germany were calculated for 2020 using different methods. In particular, the influence of different data sources and reference periods was examined. Furthermore, its influence on the calculated mortality was also examined to take into account increasing life expectancy. In addition, published results on years of life lost were critically analyzed. Using January 2022 data from the Federal Statistical Office on mortality for 5-year age groups resulted in higher SMR values than using preliminary data from February 2021 with 20-year age groups (SMR=0.997, 95% confidence interval (CI): 0.995-0.999 versus SMR=0.976 (95% CI: 0.974-0.978)). The choice of the reference period had a large impact on calculated mortality (for men, SMR=1.024 (95% CI: 1.022-1.027) with 2019 as the reference year versus SMR=0.998 (95% CI: 0.996-1.001) with 2016 to 2019 as the reference period). Analyses in which declining mortality in 2016 to 2019 was carried forward into 2020 when calculating expected deaths resulted in significantly higher SMR values (for men SMR=1.024 (95% CI: 1.021-1.026) with, and SMR=0.998 (95% CI: 0.996-1.001) without carrying forward declining mortality). Figures for pandemic-related years of life lost per person who died from COVID-19 should be interpreted with caution: Calculation from remaining life reported in mortality tables can lead to misleading results. When calculating mortality and years of life lost during the pandemic, a number of methodological assumptions must be made that have a significant impact on the results and must be considered when interpreting the results.

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