Pooling European all-cause mortality: methodology and findings for the seasons 2008/2009 to 2010/2011

Abstract

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.

Excess mortality among the elderly in European countries, December 2014 to February 2015

“…Nonetheless, it indicates that it is likely that influenza is an important contributor to the observed excess mortality among the elderly given previous evidence of attribution [2,9,10,18,19]. Pooling of data, in this case both data on number of deaths and virological influenza data across Europe, provides more statistical power than country-specific analyses and may enhance patterns that are not apparent in individual countries or populations [4]. On the other hand, details may be lost when pooling data (e.g.…”

“…This resulted in data from 14 countries (Belgium, Denmark, Estonia, Finland, France, Hungary, the Netherlands, Portugal, Spain, Sweden, Switzerland and the UK (England, Wales, and Scotland)) being included for such analyses. We used the stratified method as previously described [4]. The time-series in the pooled analysis included data from week 23, 2010 to week 9, 2015.…”

Excess mortality

“…1 These often quoted statistics are compiled by Johns Hopkins University based on a range of national and international sources. 2…”

Excess mortality in Guadeloupe and Martinique, islands of the French West Indies, during the chikungunya epidemic of 2014

“…A situation of this nature occurs with influenza in which only a small part of the deaths is diagnosed as such. Most influenza-related deaths result from secondary bacterial pneumonia or from decompensation of underlying diseases caused by viral infection and death is not classified as due to influenza, thus, the impact of influenza on mortality is estimated by assessing the excess of influenza mortality [30,31]. Using similar criteria of present work the excess of deaths caused by influenza among those over 65 years is 73 deaths per 100,000 per year [30], with a simple calculation with the data of the present study we conclude that the excess of deaths associated with a CHIKV epidemic is 324 per 100,000 inhabitants over 60 years or 4.4 times higher than the annual average of mortality associated with influenza[30].…”

“…Using the excess mortality criteria, the mortality associated with the CHIKV epidemic in a single year would be 13 times greater than that accumulated in 10 years of dengue epidemics in the same locality, and the fatality rate would be 7.7 times greater than dengue. Although it is not possible to make the etiological diagnosis of all the cases of deaths associated with CHIKV infection, already well-known statistical tools can contribute to an evaluation of the impact of this virus on the mortality in the different age groups, as in the deaths caused by extreme weather phenomena, seasonal and pandemic influenza[31,33,34].…”