Estimating Overall and Cause-Specific Excess Mortality during the Covid-19 Pandemic
Methodological Approaches Compared
Bibliographic Data
| ID | 15473439 |
|---|---|
| Authors | Claudio Barbiellini Amidei (0000-0002-7792-3781, Regione del Veneto), Ugo Fedeli (0000-0002-9939-3398, Regione del Veneto, corresponding author), Nicola Gennaro (0000-0003-2582-8069, Regione del Veneto), Laura Cestari (Regione del Veneto), Elena Schievano (0000-0002-9912-5107, Regione del Veneto), Manuel Zorzi (0000-0001-6025-5214, Regione del Veneto), Paolo Girardi (0000-0001-8330-9414, Ca' Foscari University of Venice), Veronica Casotto (0000-0001-5846-1377, Regione del Veneto) |
| Year | 2023 |
| Volume | 20 |
| Issue | 11 |
| Pages | 5941-5941 |
| Publication date | 2023-05-24 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph20115941 |
| PMID | 37297545 |
| OpenAlex | W4378228390 |
| Language | EN |
| References cited | 20 |
During the COVID-19 pandemic, excess mortality has been reported worldwide, but its magnitude has varied depending on methodological differences that hinder between-study comparability. Our aim was to estimate variability attributable to different methods, focusing on specific causes of death with different pre-pandemic trends. Monthly mortality figures observed in 2020 in the Veneto Region (Italy) were compared with those forecasted using: (1) 2018-2019 monthly average number of deaths; (2) 2015-2019 monthly average age-standardized mortality rates; (3) Seasonal Autoregressive Integrated Moving Average (SARIMA) models; (4) Generalized Estimating Equations (GEE) models. We analyzed deaths due to all-causes, circulatory diseases, cancer, and neurologic/mental disorders. Excess all-cause mortality estimates in 2020 across the four approaches were: +17.2% (2018-2019 average number of deaths), +9.5% (five-year average age-standardized rates), +15.2% (SARIMA), and +15.7% (GEE). For circulatory diseases (strong pre-pandemic decreasing trend), estimates were +7.1%, -4.4%, +8.4%, and +7.2%, respectively. Cancer mortality showed no relevant variations (ranging from -1.6% to -0.1%), except for the simple comparison of age-standardized mortality rates (-5.5%). The neurologic/mental disorders (with a pre-pandemic growing trend) estimated excess corresponded to +4.0%/+5.1% based on the first two approaches, while no major change could be detected based on the SARIMA and GEE models (-1.3%/+0.3%). The magnitude of excess mortality varied largely based on the methods applied to forecast mortality figures. The comparison with average age-standardized mortality rates in the previous five years diverged from the other approaches due to the lack of control over pre-existing trends. Differences across other methods were more limited, with GEE models probably representing the most versatile option
Coronavirus disease 2019 (COVID-19 · Disease · Excess mortality · Gee · Generalized estimating equation · Infectious disease (medical specialty · Mortality rate · Pandemic · Standardized mortality ratio · Statistics · Climate Change and Health Impacts · COVID-19 and healthcare impacts · COVID-19 epidemiological studies · Demography · Mathematics · Medicine · Internal Medicine
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| Citation velocity | historical |
|---|---|
| Highly cited | No |