[Factors Influencing Results of Mortality Measurement in the Corona Pandemic: Analyses of Mortality in Germany in 2020]. / Einflussfaktoren auf Ergebnisse der Sterblichkeitsmessung in der Corona-Pandemie: Auswertungen zur Sterblichkeit in Deutschland im Jahr 2020.

INTRODUCTION: (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. METHODOLOGY: 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. RESULTS: 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. CONCLUSION: 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.

Calculating the number of undetected active SARS-CoV-2 infections from results of population-wide antigen tests.

Current European research estimates the number of undetected active SARS-CoV-2 infections (dark figure) to be two- to 130-fold the number of detected cases. We revisited the population-wide antigen tests in Slovakia and South Tyrol and calculated the dark figure of active cases in the vulnerable populations and the number of undetected active cases per detected active case at the time of the population-wide tests. Our analysis follows three steps: using the sensitivities and specificities of the used antigen tests, we first calculated the number of test-positive individuals and the proportion of actual positives in those who participated in the antigen tests. We then calculated the dark figure in the total population of Slovakia and South Tyrol, respectively. Finally, we calculated the ratio of the dark figure in the vulnerable population to the number of newly detected infections through PCR tests. Per one positive PCR result, another 0.15 to 0.71 cases must be added in South Tyrol and 0.01 to 1.25 cases in Slovakia. The dark figure was in both countries lower than assumed by earlier studies.

Excess mortality due to Covid-19? A comparison of total mortality in 2020 with total mortality in 2016 to 2019 in Germany, Sweden and Spain.

INTRODUCTION: Excess mortality is a suitable indicator of health consequences of COVID-19 because death from any cause is clearly defined contrary to death from Covid-19. We compared the overall mortality in 2020 with the overall mortality in 2016 to 2019 in Germany, Sweden and Spain. Contrary to other studies, we also took the demographic development between 2016 and 2020 and increasing life expectancy into account. METHODS: Using death and population figures from the EUROSTAT database, we estimated weekly and cumulative Standardized Mortality Ratios (SMR) with 95% confidence intervals (CI) for the year 2020. We applied two approaches to calculate weekly numbers of death expected in 2020: first, we used mean weekly mortality rates from 2016 to 2019 as expected mortality rates for 2020, and, second, to consider increasing life expectancy, we calculated expected mortality rates for 2020 by extrapolation from mortality rates from 2016 to 2019. RESULTS: In the first approach, the cumulative SMRs show that in Germany and Sweden there was no or little excess mortality in 2020 (SMR = 0.976 (95% CI: 0.974-0.978), and 1.030 (1.023-1.036), respectively), while in Spain the excess mortality was 14.8% (1.148 (1.144-1.151)). In the second approach, the corresponding SMRs for Germany and Sweden increased to 1.009 (1.007-1.011) and 1.083 (1.076-1.090), respectively, whereas results for Spain were virtually unchanged. CONCLUSION: In 2020, there was barely any excess mortality in Germany for both approaches. In Sweden, excess mortality was 3% without, and 8% with consideration of increasing life expectancy.