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. 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. 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. 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.

In temperate zones, all-cause mortality exhibits a marked seasonality, and one of the main causes of winter excess mortality is influenza. There is a tradition of using statistical models based on mortality from respiratory illnesses (Pneumonia and Influenza: PI) or all-cause mortality for estimating the number of deaths related to influenza. Different authors have applied different estimation methodologies. We estimated mortality related to influenza and periods with extreme temperatures in Denmark over the seasons 1994/95 to 2009/10.

Europe experienced excess mortality from February through June, 2020 due to the COVID-19 pandemic, with more COVID-19-associated deaths in males compared to females. However, a difference in excess mortality among females compared to among males may be a more general phenomenon, and should be investigated in none-COVID-19 situations as well. Based on death counts from Eurostat, separate excess mortalities were estimated for each of the sexes using the EuroMOMO model. Sex-differential excess mortality were expressed as differences in excess mortality incidence rates between the sexes. A general relation between sex-differential and overall excess mortality both during the COVID-19 pandemic and in preceding seasons were investigated. Data from 27 European countries were included, covering the seasons 2016/17 to 2019/20. In periods with increased excess mortality, excess was consistently highest among males. From February through May 2020 male excess mortality was 52.7 (95% PI: 56.29; 49.05) deaths per 100,000 person years higher than for females. Increased male excess mortality compared to female was also observed in the seasons 2016/17 to 2018/19. We found a linear relation between sex-differences in excess mortality and overall excess mortality, i.e., 40 additional deaths among males per 100 excess deaths per 100,000 population. This corresponds to an overall female/male mortality incidence ratio of 0.7. In situations with overall excess mortality, excess mortality increases more for males than females. We suggest that the sex-differences observed during the COVID-19 pandemic reflects a general sex-disparity in excess mortality.

Background: In temperate zones, all‐cause mortality exhibits a marked seasonality, and influenza represents a major cause of winter excess mortality. We present a statistical model, FluMOMO, which estimate influenza‐associated mortality from all‐cause mortality data and apply it to Danish data from 2010/11 to 2016/17.Methods: We applied a multivariable time series model with all‐cause mortality as outcome, influenza activity and extreme temperatures as explanatory variables while adjusting for time trend and seasonality. Three indicators of weekly influenza activity (IA) were explored: percentage of consultations for influenza‐like illness (ILI) at primary health care, national percentage of influenza‐positive samples, and the product of ILI percentage and percentage of influenza‐positive specimens in a given week, that is, the Goldstein index.Results: Independent of the choice of parameter to represent influenza activity, the estimated influenza‐associated mortality showed similar patterns with the Goldstein index being the most conservative. Over the 7 winter seasons, the median influenza‐associated mortality per 100 000 population was 17.6 (range: 0.0‐36.8), 14.1 (0.3‐31.6) and 8.3 (0.0‐25.0) for the 3 indicators, respectively, for all ages.Conclusion: The FluMOMO model fitted the Danish data well and has the potential to estimate all‐cause influenza‐associated mortality in near real time and could be used as a standardised method in other countries. We recommend using the Goldstein index as the influenza activity indicator in the FluMOMO model. Further work is needed to improve the interpretation of the estimated effects.

SUMMARY: 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.

Background: Invasive meningococcal disease (IMD) is a rare but severe bacterial infection, of which a high proportion of survivors are affected by sequelae. In Denmark, IMD is a notifiable disease and data collection on sequelae information has been automated, enabling studies of sequelae due to IMD diagnosed after discharge. The aim of this study was to examine possible determinants for sequelae after IMD and to describe the distribution of sequelae by age, serogroup and clinical presentation, for all cases in Denmark from 2005–2020. Methods: Data from The National Database for Notifiable Infectious Diseases was linked to data from The Danish National Patient Register and the Civil Registration System. Logistic regression models were used to study whether age, serogroup and/or clinical presentation were associated with sequelae. A descriptive analysis of the proportion of different types of sequelae across age groups, serogroups and clinical presentations was performed. Results: In total, 25% of IMD survivors experienced one or more sequelae. We found no significant association between sequelae and age. The five most common sequelae in decreasing order of incidence were hearing loss, epilepsy, learning disabilities, headache and visual defects/loss of vision, with rates ranging from 8.2 to 2.8% of IMD survivors. The proportion of survivors with hearing loss and visual defects/loss of vision was not significantly different between clinical presentations. Conclusions: We suggest revising IMD treatment guidelines, to include routine referral to hearing and vision tests, irrespective of clinical presentation. Furthermore, it is important to increase the awareness among parents of children who have had IMD of possible future learning disabilities to make sure that necessary measures are taken in a timely manner.