Estimating weekly excess mortality at subnational level in Italy during the COVID-19 pandemic (preprint)

ABSTRACT

ObjectivesTo provide a sub-national analysis of excess mortality during the COVID-19 pandemic in Italy. DesignPopulation-based on all-cause mortality official data, available as counts by age and sex. SettingThe 7,904 municipalities in Italy. ParticipantsAll residents in Italy in the years 2016 to 2020. Main outcome measuresAll-cause mortality weekly rates for each municipality, based on the first four months of 2016 - 2019. Predicted all-cause weekly deaths and mortality rates at municipality level for 2020, based on the modelled spatio-temporal trends. ResultsThere was strong evidence of excess mortality for Northern Italy; Lombardia showed higher mortality rates than expected from the end of February, with 23,946 (23,013 to 24,786) total excess deaths. North-West and North-East regions showed higher mortality from the beginning of March, with 6,942 (6,142 to 7,667) and 8,033 (7,061 to 9,044) total excess deaths respectively. After discounting for the number of COVID-19-confirmed deaths, Lombardia still registered 10,197 (9,264 to 11,037) excess deaths, while regions in the North-West and North-East had 2,572 (1,772 to 3,297) and 2,047 (1,075 to 3,058) extra deaths, respectively. We observed marked geographical differences at municipality level. The city of Bergamo (Lombardia) showed the largest percent excess 88.9% (81.9% to 95.2%) at the peak of the pandemic. An excess of 84.2% (73.8% to 93.4%) was also estimated at the same time for the city of Pesaro (Central Italy), in stark contrast with the rest of the region, which does not show evidence of excess deaths. ConclusionsOur study gives a comprehensive picture of the evolution of all-cause mortality in Italy from 2016 to 2020 and describes the spatio-temporal differences in excess mortality during the COVID-19 pandemic. Our model shows heterogeneous impact of COVID-19, and it can be used to help policy- makers target measures to limit the burden on the health-care system as well as reducing social and economic consequences. Our probabilistic methodology is useful for real-time mortality surveillance, continuously monitoring local temporal trends and flagging where and when mortality rates deviate from the expected range, which might suggest a second wave of the pandemic.