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The coronavirus disease (COVID-19) global pandemic is ongoing, with Europe and the Americas the current epicentres of transmission. As at 1 January, 2021, over 82 million cases and 1.8 million deaths have been reported globally [1,2]. In Europe alone, the number of cases reported has now passed 26 million, with over 580,000 laboratory-confirmed COVID-19 deaths [3,4]. As many COVID-19 cases and deaths may not be confirmed by virological testing, and as the pandemic causes a range of indirect and collateral harms, the officially reported figures of laboratory-confirmed COVID-19 cases and deaths represent only a part of the total disease, mortality and overall public health burden associated with the pandemic. Here we report some noteworthy estimates of a marked increase in excess all-cause mortality in Europe coinciding with a steep second wave of COVID-19 in many countries since September 2020.

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. info:eu-repo/semantics/publishedVersion

Schatzungen der Anzahl der Verstorbenen infolge der Influenzapandemien des 20. und 21. Jahrhunderts (1918/1919, 1957/1958, 1968–1970 und 2009) sind weltweit und auch fur Deutschland eine Herausforderung. Die aufgrund einer systematischen Literaturrecherche erhobenen Ergebnisse sollen zusammengetragen, durch eigene Berechnungen erganzt und bewertet werden. Es wurde eine systematische Literaturrecherche unter Verwendung u. a. der Begriffe death, mortality, pandemic, epidemic, Germany, 1918, 1957, 1968, 2009 durchgefuhrt. Treffer wurden nach Titel bzw. Zusammenfassung gesichtet und bezuglich moglicher Relevanz selektiert. Eigene Schatzungen wurden, basierend auf Exzess-Mortalitats-Berechnungen, durchgefuhrt. Die verschiedenen Werte aus der Literatur und den gegebenenfalls eigenen Berechnungen wurden bezuglich Methodik und Qualitat der Datengrundlage bewertet, um den jeweils vertrauenswurdigsten zu identifizieren. Aus den Todesfallzahlen wurde fur jede Pandemie eine Letalitat berechnet und mit globalen Werten der Weltgesundheitsorganisation (WHO) verglichen. Fur die Pandemie 1918/1919 identifizierten wir 5, fur die Pandemien 1957/1958 und/oder 1968–1970 3 und fur die Pandemie 2009 ebenfalls 3 relevante Publikationen. Fur alle 4 Pandemien waren die plausibelsten Schatzungen diejenigen, welche mittels einer Zeitreihenanalyse (aus der Literatur entnommen oder aufgrund eigener Schatzungen) monatlicher oder wochentlicher Gesamttodesfallstatistiken ermittelt wurden. Diese waren fur die 4 Pandemien in chronologischer Reihenfolge 426.600 (1918/1919), 29.100 (1957/1958), 46.900 (1968–1970) bzw. 350 (2009) und entsprachen einer bevolkerungsbezogenen Exzess-Mortalitat zwischen 691/100.000 (0,69 %) infolge der Pandemie 1918/1919 und 0,43/100.000 (0,00043 %) fur 2009. Die resultierenden Letalitaten stimmten gut mit globalen, von der WHO angegebenen Werten uberein. Fur die letzten 4 Pandemien (1918 bis 2009) konnten plausible Schatzungen der Exzess-Todesfallzahlen identifiziert werden. Die Heterogenitat der entsprechenden Exzess-Mortalitat zwischen den Pandemien ist bei einem Faktor von mehr als 1000 enorm. Diese kann durch Eigenschaften des Virus oder des Wirts (Immunitat), die jeweiligen gesellschaftlichen Bedingungen, die Gesundheitsversorgung oder die medizinische Entwicklung bedingt sein.

SUMMARYWe investigated a cluster of shiga toxin-producingEscherichia coli(STEC) O104:H4 infections after a family party during a large STEC O104:H4 outbreak in Germany. To identify the vehicle we conducted a retrospective cohort study. Stool samples of party guests, and food and environmental samples from the catering company were tested for STEC. We defined cases as party guests with gastrointestinal symptoms and laboratory-confirmed STEC infection. We found 23 cases among 71 guests. By multivariable analysis consumption of salmon [odds ratio (OR) 15, 95% confidence interval (CI) 2·3–97], herb cream (OR 6·5, 95% CI 1·3–33) and bean salad (OR 6·1, 95% CI 1·4–26) were associated with STEC infection. STEC O104:H4 was detected in samples of bell pepper and salmon. The food handler developed STEC infection. Our results point towards transmission via several food items contaminated by a food handler. We recommend regular education of food handlers emphasizing their role in transmitting infectious diseases.

Die Erfassung von Todesfallen ist ein wichtiges epidemiologisches Instrument, um den Verlauf, das Ausmas, betroffene Altersgruppen und die Schwere von Gesundheitsgefahren einschatzen zu konnen. Dennoch sind in Deutschland weder auf Landes- noch auf Bundesebene geeignete Daten oder Auswertungen zeitnah zuganglich. Im vorliegenden Beitrag werden Aufbau und Struktur eines Surveillance-Systems zur zeitnahen Erfassung von Todesfallen in Hessen (ZETH) und erste Erfahrungen wahrend der Influenza-A/H1N1v-Pandemie 2009/2010 dargestellt. Wahrend des Aufbaus des ZETH wurde 2008 auf eine wochentliche Ubermittlung der bei den Standesamtern elektronisch erfassten Todesfalle (das heist etwa 95% aller Todesfalle) an das Statistische Landesamt umgestellt. Das Landesamt stellt nun – ebenfalls wochentlich – diese Daten dem Hessischen Landesprufungs- und Untersuchungsamt im Gesundheitswesen (HLPUG) in todesfallbasierter datenschutzkonformer Form zur Verfugung. Wahrend der Influenza-Pandemie gestattete das ZETH die zeitnahen Einschatzungen der Ubersterblichkeit nach Altersgruppen mit einer zweiwochigen Verzogerung. Es zeigte sich eine geringe Zunahme der Todesfalle uber das Erwartungsniveau in den Altersgruppen von 15 bis 34, 35 bis 49 und 50 bis 59 Jahren. Der Zusammenhang zum zeitlichen Verlauf und zur Intensitat der Erkrankungswelle war jedoch nicht stark ausgepragt, sodass eine Ubersterblichkeit, die offensichtlich uber die im Meldewesen erfassten Influenza-Todesfalle (21 fur Hessen) hinausgeht, nicht deutlich wurde.

In 2011, the Shiga toxin- and extended-spectrum β-lactamase (ESBL)-producing Escherichia coli O104:H4 caused a serious outbreak of gastroenteritis in Germany. This strain carried bla(CTX-M-15) and bla(TEM-1) on an IncI1-ST31 plasmid. During screening of individuals at risk for acquisition of the epidemic E. coli O104:H4, we isolated another ESBL-producing and Shiga toxin-positive E. coli belonging to serotype O91:H14 from feces of a human patient. Interestingly, the patient also carried a further ESBL-producing but Shiga toxin-negative E. coli. Both strains harbored bla(CTX-M-15) and bla(TEM-1) on an IncI1-ST31 plasmid, which was indistinguishable regarding size and plasmid restriction pattern from the plasmid of the epidemic E. coli O104:H4 strain. The patient had traveled to India 6 months prior to the isolation of the E. coli strains. This is the first report of an ESBL-producing, Shiga toxin-positive E. coli of serogroup O91. Our data suggest a high propensity of the IncI1-ST31 plasmid to spread in the human and/or animal population.

Die Influenzaaktivitat im Winter 2000/2001 wird anhand der Beobachtungen der AGI und der Nationalen Referenzzentren bewertet. Insgesamt war die Influenzawelle sehr kompakt mit starkem Anstieg der Morbiditat, spitzem Gipfel und raschem Ruckgang der Erkrankungen. Es waren fast gleichzeitig alle Regionen erfasst, und der auslosende Subtyp A/H1N1 entsprach der erwarteten Variante. Das Erkrankungsgeschehen war stark auf die jungeren Altersgruppen konzentriert. Zum Gipfel der Welle wurden bei Kindern und Jugendlichen ungewohnlich hohe Erkrankungszahlen erreicht, die meist leicht und unkompliziert verliefen. Hohere Altersgruppen waren weniger betroffen, und die uber 60-Jahrigen wurden kaum von der Erkrankungswelle erfasst. Die Influenzaaktivitat schien im Osten etwas hoher als im Westen.

Anhand der Influenza-Surveillancedaten von 1988/89 bis 1997/98 wurde fur Influenza B ein einfaches Modell fur das Wechselspiel von Populationsimmunitat und Influenzaaktivitat entwickelt. Dafur wurde ein Index fur die Populationsimmunitat aus der Verbreitung der Influenza B in den funf vorangegangenen Beobachtungsperioden bestimmt. Die Ermittlung der anzunehmenden Abnahme der Bedeutung der Durchseuchung mit der Zeit, aufgrund der Drift und sinkender Antikorpertiter, erfolgte anhand des Modells mit empirischen Methoden. Es konnte eine enge Korrelation zwischen Immunitatsindex und beobachteter Verbreitung der Influenza B innerhalb der sechs aus den Daten berechenbaren Beobachtungsperioden (1992/93 bis 1997/98) hergestellt werden.

OBJECTIVES--Before and during peak influenza periods there is increased morbidity from other respiratory tract disorders. Sentinel networks of primary care physicians can be very effective in the early detection of influenza epidemics and the German network, the Arbeitsgemeinschaft Influenza (AGI), began its work in this area in 1992. METHODS--Data are transmitted weekly from the doctor's computer via Btx to a central computer. The numerator is the weekly number of acute respiratory infections (ARI) in five age groups and the denominator is the weekly number of patient consultations. Data on hospitalisation, mortality, and days of sick leave from work or school are also collected. Swabs for influenza specimens are collected in 30 physicians' offices each Monday and sent to three reference centres. FINDINGS--During the last recording period, from week 46 1993 to week 15 1994, 411 physicians' offices participated in the network. For 16 to 22 weeks, more than 60% of the participants transmitted data. During both the 1992-93 and 1993-94 influenza seasons, peaks were observed in the rate of ARI. There was a corresponding increase in sick leave from work and school. Rates for hospitalisation and deaths due to influenza showed no peaks during either season. CONCLUSIONS--Although the German sentinel network for influenza experienced some technical problems in the first year, it was possible to solve these. Reporting rates were very satisfactory in the second year. The network will now be expanded to include 750 physicians in order to receive 600 weekly reports and obtain a solid baseline for an early warning system.

SUMMARYSeveral 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.

The paper describes weekly fluctuations of all-cause mortality observed in eight European countries during the period between week 27 and 51, 2009, in comparison with three previous years. Our preliminary data show that the mortality reported during the 2009 influenza pandemic did not reach levels normally seen during seasonal influenza epidemics. However, there was a cumulative excess mortality of 77 cases (1 per 100,000 population) in 5-14-year-olds, and possibly also among 0-4-year-olds.

For the next influenza season (winter 2009-10) the relative contributions to virus circulation and influenza-associated morbidity of the seasonal influenza viruses A(H3N2), A(H1N1) and B, and the new influenza A(H1N1)v are still unknown. We estimated the chances of seasonal influenza to circulate during the upcoming season using data of the German influenza sentinel scheme from 1992 to 2009. We calculated type and subtype-specific indices for past exposure and the corresponding morbidity indices for each season. For the upcoming season 2009-10 our model suggests that it is unlikely that influenza A(H3N2) will circulate with more than a low intensity, seasonal A(H1N1) with more than a low to moderate intensity, and influenza B with more than a low to median intensity. The probability of a competitive circulation of seasonal influenza A with the new A(H1N1)v is low, increasing the chance for the latter to dominate the next influenza season in Germany.

In 2005, a marked increase in hantavirus infections was observed in Germany. Large cities and areas where hantaviruses were not known to be endemic were affected. A case-control study identified the following independent risk factors for infection: occupational exposure for construction workers, living100 meter from forested areas, and exposure to mice.

A survey of directors and employees of 36 long-term care facilities in Hesse, Germany, revealed that influenza vaccine uptake among staff was less than 30% in 30 and greater than 50% in 6. The study identified policies and practices associated with vaccination uptake at long-term care facilities and factors associated with the decision of staff to get vaccinated.

During the autumn wave of the pandemic influenza virus A/(H1N1) 2009 (pIV) the German population was offered an AS03-adjuvanted vaccine. The authors compared results of two methods calculating the effectiveness of the vaccine (VE). The test-negative case-control method used data from virologic surveillance including influenza-positive and negative patients. An innovative case-series methodology explored data from all nationally reported laboratory-confirmed influenza cases. The proportion of reported cases occurring in vaccinees during an assumed unprotected phase after vaccination was compared with that occurring in vaccinees during their assumed protected phase. The test-negative case-control method included 1,749 pIV cases and 2,087 influenza test-negative individuals of whom 6 (0.3%) and 36 (1.7%), respectively, were vaccinated. The case series method included data from 73,280 cases. VE in the two methods was 79% (95% confidence interval (CI) = 35-93%; P = 0.007) and 87% (95% CI = 78-92%; P

Background In Germany, surveillance for infectious disease outbreaks is integrated into an electronic surveillance system. For 2007, the national surveillance database contains case-based information on 201,224 norovirus cases, three-quarters of which are linked to outbreaks. We evaluated the data quality of the national database in reflecting nosocomial norovirus outbreak (NNO) data available in 19 Hessian local public health authorities (LPHAs) and the influence of differences between LPHA's follow-up procedures for laboratory notifications of Norovirus positive stool samples on outbreak underascertainment. Methods Data on NNO beginning in 2007 and notified to the 19 LPHAs were extracted from the national database, investigated regarding internal validity and compared to data collected from LPHAs for a study on NNO control. LPHAs were questioned whether they routinely contacted all persons for whom a laboratory diagnosis of norovirus infection was notified. The number of outbreaks per 1,000 hospital beds and the number of cases within NNOs for acute care and rehabilitation hospitals were compared between counties with and without complete follow-up. Results The national database contained information on 155 NNOs, including 3,115 cases. Cases were missed in the national database in 58 (37%) of the outbreaks. Information on hospitalisation was incorrect for an estimated 47% of NNO cases. Information on county of infection was incorrect for 24% (199/820) of cases being forwarded between LPHAs for data entry. Reported NNO incidence and number of NNO cases in acute care hospitals was higher in counties with complete follow-up (incidence-rate ratio (IRR) 2.7, 95% CI 1.4–5.7, p-value 0.002 and IRR 2.1, 95% CI 1.9–2.4, p-value 0.001, respectively). Conclusions Many NNOs are not notified by hospitals and differences in LPHA procedures have an impact on the number of outbreaks captured in the surveillance system. Forwarding of case-by-case data on Norovirus outbreak cases from the local to the state and national level should not be required.

Influenza-associated excess mortality is widely used to assess the severity of influenza epidemics. In Germany, however, it is not yet established as a routine component of influenza surveillance. We therefore applied a simple method based on the annual distribution of monthly relative mortality (relative mortality distribution method, RMDM) to a time-series of German monthly all-cause mortality data from 1985–2001 to estimate influenza-associated excess mortality. Results were compared to those obtained by cyclical regression. Both methods distinguished stronger from milder influenza seasons, but RMDM gave the better fit (R2 = 0.80). For the years after reunification, i.e. 1990/91 through 2000/01, RMDM yielded an average of 6900 (conservative estimate) to13600 influenza-asssociated excess deaths per season (crude estimate). The most severe epidemics occurred during subtype A/H3N2 seasons. While German all-cause mortality declined over the study period, the number of excess deaths displayed an upward trend, coinciding with an increase of the proportion of the elderly population.

In February and March 2012, excess deaths among the elderly have been observed in 12 European countries that carry out weekly monitoring of all-cause mortality. These preliminary data indicate that the impact of influenza in Europe differs from the recent pandemic and post-pandemic seasons. The current excess mortality among the elderly may be related to the return of influenza A(H3N2) virus, potentially with added effects of a cold snap.