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Aims: Excess mortality during the SARS-CoV-2 pandemic has been studied in many countries. Accounting for population aging has important implications for excess mortality estimates. We show the importance of adjustment for age trends in a small-scale mortality analysis as well as the importance of analysing different pandemic phases for mortality in an urban population. Methods: Population data for Frankfurt/Main for 2016-2021 were obtained from the Municipal Office of Statistics, City of Frankfurt/Main. Mortality data from 2016 to 2021 were provided by the Hessian State Authority. For standardized mortality ratios (SMR=observed number of deaths divided by the expected number of deaths), the expected number of deaths was calculated in two ways: For SMRcrude, the mean mortality rate from the years 2016-2019 was multiplied by the total number of residents in 2020 and 2021 separately. For SMRadjusted, this procedure was performed separately for five age groups, and the numbers of expected deaths per age group were added. Results: SMRcrude was 1.006 (95% CI: 0.980-1.031) in 2020, and 1.047 (95% CI: 1.021-1.073) in 2021. SMRadjusted was 0.976 (95% CI: 0.951-1.001) in 2020 and 0.998 (95% CI: 0.973-1.023) in 2021. Excess mortality was observed during pandemic wave 2, but not during pandemic waves 1 and 3. Conclusion: Taking the aging of the population into account, no excess mortality was observed in Frankfurt/Main in 2020 and 2021. Without adjusting for population aging trends in Frankfurt /Main, mortality would have been greatly overestimated., Ziele: Die Übersterblichkeit während der SARS-CoV-2-Pandemie wurde in vielen Ländern untersucht. Die Berücksichtigung der Alterung der Bevölkerung hat wichtige Auswirkungen auf die Schätzung der Übersterblichkeit. Wir zeigen, wie wichtig die Berücksichtigung von Alterstrends in einer kleinmaßstäblichen Mortalitätsanalyse ist, und wie wichtig es ist, verschiedene Pandemiephasen für die Mortalität in einer städtischen Bevölkerung zu analysieren. Methoden: Die Bevölkerungsdaten für Frankfurt am Main für die Jahre 2016 bis 2021 wurden vom Bürgeramt Statistik und Wahlen der Stadt Frankfurt am Main erhalten. Die Mortalitätsdaten 2016 bis 2021 wurden vom Hessischen Landesamt zur Verfügung gestellt. Für die Standardisierte Mortalitätsratio (SMR=beobachtete Zahl der Sterbefälle geteilt durch die erwartete Zahl der Sterbefälle) wurde die erwartete Zahl der Sterbefälle auf zwei Arten berechnet: Für die SMRroh wurde die mittlere Mortalitätsrate aus den Jahren 2016-2019 mit der Gesamtzahl der Bürger im Jahr 2020 bzw. 2021 multipliziert. Für die SMRadjustiert wurde dieses Verfahren für fünf Altersgruppen getrennt durchgeführt und die Zahlen der erwarteten Todesfälle pro Altersgruppe wurden addiert. Ergebnisse: Die SMRroh betrug 1,006 (95% CI: 0,980-1,031) im Jahr 2020 und 1,047 (95% CI: 1,021-1,073) im Jahr 2021. Die SMRadjustiert betrug 0,976 (95% CI: 0,951-1,001) und 0,998 (95% CI: 0,973-1,023) im Jahr 2021. Eine Übersterblichkeit wurde während der Pandemiewelle 2 beobachtet, nicht jedoch während der Pandemiewellen 1 und 3. Schlussfolgerung: Unter Berücksichtigung der Alterung der Bevölkerung wurde in den Jahren 2020 und 2021 in Frankfurt am Main keine Übersterblichkeit beobachtet. Ohne Anpassung an die Alterung der Bevölkerung in Frankfurt am Main wäre die Sterblichkeit stark überschätzt worden.

Objectives: Weekly monitoring of European all-cause excess mortality, the EuroMOMO network, observed high excess mortality during the influenza B/Yamagata dominated 2017/18 winter season, especially among elderly. We describe all-cause excess and influenza-attributable mortality during the season 2017/18 in Europe. Methods: Based on weekly reporting of mortality from 24 European countries or sub-national regions, representing 60% of the European population excluding the Russian and Turkish parts of Europe, we estimated age stratified all-cause excess morality using the EuroMOMO model. In addition, age stratified all-cause influenza-attributable mortality was estimated using the FluMOMO algorithm, incorporating influenza activity based on clinical and virological surveillance data, and adjusting for extreme temperatures. Results: Excess mortality was mainly attributable to influenza activity from December 2017 to April 2018, but also due to exceptionally low temperatures in February-March 2018. The pattern and extent of mortality excess was similar to the previous A(H3N2) dominated seasons, 2014/15 and 2016/17. The 2017/18 overall all-cause influenza-attributable mortality was estimated to be 25.4 (95%CI 25.0-25.8) per 100,000 population; 118.2 (116.4-119.9) for persons aged 65. Extending to the European population this translates into over-all 152,000 deaths. Conclusions: The high mortality among elderly was unexpected in an influenza B dominated season, which commonly are considered to cause mild illness, mainly among children. Even though A(H3N2) also circulated in the 2017/18 season and may have contributed to the excess mortality among the elderly, the common perception of influenza B only having a modest impact on excess mortality in the older population may need to be reconsidered.

Several European countries have timely all-cause mortality monitoring. However, small changes in mortality may not give rise to signals at the national level. Pooling data across countries may overcome this, particularly if changes in mortality occur simultaneously. Additionally, pooling may increase the power of monitoring populations with small numbers of expected deaths, e.g. younger age groups or fertile women. Finally, pooled analyses may reveal patterns of diseases across Europe. We describe a pooled analysis of all-cause mortality across 16 European countries. Two approaches were explored. In the ‘summarized' approach, data across countries were summarized and analysed as one overall country. In the ‘stratified' approach, heterogeneities between countries were taken into account. Pooling using the ‘stratified' approach was the most appropriate as it reflects variations in mortality. Excess mortality was observed in all winter seasons albeit slightly higher in 2008/09 than 2009/10 and 2010/11. In the 2008/09 season, excess mortality was mainly in elderly adults. In 2009/10, when pandemic influenza A(H1N1) dominated, excess mortality was mainly in children. The 2010/11 season reflected a similar pattern, although increased mortality in children came later. These patterns were less clear in analyses based on data from individual countries. We have demonstrated that with stratified pooling we can combine local mortality monitoring systems and enhance monitoring of mortality across Europe

Rapid identification of agronomically important genes is of pivotal interest for crop breeding. One source of such genes are crop wild relative (CWR) populations. Here we used a CWR population of <200 wild beets (B. vulgaris ssp. maritima), sampled in their natural habitat, to identify the sugar beet (Beta vulgaris ssp. vulgaris) resistance gene Rz2 with a modified version of mapping-by-sequencing (MBS). For that, we generated a draft genome sequence of the wild beet. Our results show the importance of preserving CWR in situ and demonstrate the great potential of CWR for rapid discovery of causal genes relevant for crop improvement. The candidate gene for Rz2 was identified by MBS and subsequently corroborated via RNA interference (RNAi). Rz2 encodes a CC-NB-LRR protein. Access to the DNA sequence of Rz2 opens the path to improvement of resistance towards rhizomania not only by marker-assisted breeding but also by genome editing.