Estimating excess mortality in French and Spanish regions during the first wave of the COVID-19 pandemic: An application of the later/earlier method
Présenté par : Ainhoa Elena LEGER et Silvia RIZZI (Interdisciplinary Centre on Population Dynamics de l’Université du Danemark du Sud) ; Discutant-e : à venir
Estimates of excess deaths have been widely used to measure the overall impact of the COVID-19 pandemic on mortality. We investigate the validity of a new method—the later/earlier method—developed for forecasting the number of deaths one would expect if no shock occurs. We apply this method to estimate excess mortality during the first COVID-19 wave in France and Spain, stratified by age, sex, and region. Although both countries reported similar numbers of confirmed COVID-19 deaths, Spain recorded a higher excess death risk. The results confirm differences in COVID-19 vulnerability for population subgroups and spatial areas: adults aged 75–85 were the hardest hit; Île-de-France in France and Comunidad de Madrid in Spain registered the highest excess mortality. Applicable to other demographic phenomena, the later/earlier method is simple, requires fewer assumptions than other forecasting methods, and is less biased and more accurate than the 5-year average method.
Biographie de Ainhoa Elena LEGER
Ainhoa Elena LEGER is a PhD student at the Interdisciplinary Centre on Population Dynamics at the University of Southern Denmark. She received her Master in Statistics from the University of Padua (Italy), and she has pursued graduate training at the European Doctoral School of Demography. She is interested in developing statistical methods for demographic research, and her current work focuses on short-term mortality forecasting.
Biographie de Silvia Rizzi
Silvia RIZZI is Assistant Professor at the Interdisciplinary Centre on Population Dynamics at the University of Southern Denmark, where she obtained her PhD in 2018 working in the Department of Epidemiology, Biostatistics, and Biodemography. Her research focuses on mortality forecasting, health inequalities, cause-of-death analysis, and modelling of aggregated and limited data. During the COVID-19 pandemic, she has been estimating excess mortality and the harvested population.