Spatial variation in excess mortality across Europe: a cross-sectional study of 561 regions in 21 countries

Objective To measure the burden of the COVID-19 pandemic in 2020 at the subnational level by estimating excess mortality, defined as the increase in all-cause mortality relative to an expected baseline mortality level. Design Statistical and demographic analyses of regional all-cause mortality data. Setting The vital statistics systems of 21 European countries. Participants The entire population of 561 spatial units in 21 European countries. Main Outcome Measures Losses of life expectancy at ages 0 and 60 for males and females. Results We found evidence of a loss in life expectancy in 391 regions, while only three regions exhibit notable gains in life expectancy in 2020. For 12 regions, losses of life expectancy amounted to more than 2 years, and three regions showed losses greater than 3 years. We highlight geographic clusters of high mortality in Northern Italy, Spain and Poland, while clusters of low mortality were found in Western France, Germany/Denmark and Norway/Sweden. Conclusions Regional differences of loss of life expectancy are impressive, ranging from a loss of more than 4 years to a gain of 8 months. These findings provide a strong rationale for regional analysis, as national estimates hide significant regional disparities. ‘What is already known’ Reported numbers of covid-19 deaths are subject to changes within and across countries due to inaccuracy, and incompleteness. Excess mortality measured by loss in life expectancy is widely considered a relevant indicator for assessing the total mortality impact of the COVID-19 pandemic. Whereas national estimates has been largely provided, few scattered regional studies for specific countries have been conducted. ‘What this study adds’ This study provides the first coherent analysis of excess mortality at regional level covering a large number of European countries. It allows to properly map COVID-19 pandemic using official mortality data routinely collected by vital registration systems, which are less sensitive to misclassification. How this study might affect research, practice or policy’ This study provides a strong rationale for regional analysis, as national estimates hide significant regional disparities