Abstract
Objectives: It is important to quantify the true burden of coronavirus disease 2019 (COVID-19) in different countries, to enable informed decisions about imposing and relaxing control measures. COVID-19 surveillance data fails in this respect, as it is influenced by different definitions, control policies and capacities. This article aims to quantify excess mortality and estimate the distribution between COVID-19 and non-COVID-19 causes of death.
Study design: Observational study and mathematical modelling.
Methods: Publicly available data from multiple institutional sources were used and an in-depth analysis was carried out of deaths from all causes between 2015 and 2020 in Italy at the national, regional and local level. Excess mortality over time and space was first explored, followed by an assessment of how this related to COVID-19 surveillance and, ultimately, assuming a fixed male:female ratio, a model was developed and applied to estimate the proportions of COVID-19 and non-COVID-19 excess mortality in 2020.
Results: In Italy, the mortality rate doubled in March and April 2020 compared with data from 2015 to 2019 (+109%, when considering municipalites with >10.000 inhabitants), with excess mortality reaching >600% in large municipalities in northern areas. Notified COVID-19 deaths accounted for only 43.5% (regional range: 43-62%) of excess mortality. It is estimated that more than two-thirds of excess deaths that were not captured by surveillance are non-COVID-19 deaths, which could be a result of the excess burden on the health systems, in addition to reduced demand and supply of other non-COVID healthcare services.
Conclusions: The impact of COVID-19 during the early stages of the pandemic is much larger than official figures have reported. Monitoring excess mortality helps to capture the full effect of the COVID-19 pandemic, which differs between regions in Italy and which might have resulted in significant indirect effects on the well-being of the population. In addition, the COVID-19 pandemic has also resulted in significant indirect effects on the well-being of the population.
Keywords: COVID-19; Excess mortality; Healthcare service response; Mathematical modelling.
【저자키워드】 COVID-19, Mathematical modelling, excess mortality, Healthcare service response, 【초록키워드】 coronavirus disease, pandemic, Mortality, COVID-19 pandemic, health systems, Local, Italy, observational study, Region, Measures, Surveillance, Mathematical modelling, death, monitoring, mortality rate, distribution, early stage, deaths, Non-COVID-19, available data, excess, in-depth analysis, help, surveillance data, National, healthcare services, carried, proportion, reported, addition, applied, reduced, accounted, were used, indirect effect, cause, fixed, 【제목키워드】 COVID-19 pandemic, Surveillance, mortality rate,