Assessing COVID-19-Related Excess Mortality Using Multiple Approaches—Italy, 2020–2021

Emiliano Ceccarelli (), Maria Dorrucci, Giada Minelli, Giovanna Jona Lasinio, Sabrina Prati, Marco Battaglini, Gianni Corsetti, Antonino Bella, Stefano Boros, Daniele Petrone, Flavia Riccardo, Antonello Maruotti and Patrizio Pezzotti
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Emiliano Ceccarelli: Statistical Service, Istituto Superiore di Sanità, 00161 Rome, Italy
Maria Dorrucci: Department of Infectious Diseases, Istituto Superiore di Sanità, 00161 Rome, Italy
Giada Minelli: Statistical Service, Istituto Superiore di Sanità, 00161 Rome, Italy
Giovanna Jona Lasinio: Department of Statistical Sciences, La Sapienza University, 00185 Rome, Italy
Sabrina Prati: Division of Population Register, Demographic and Living Conditions Statistics, Italian National Institute of Statistics, 00184 Rome, Italy
Marco Battaglini: Division of Population Register, Demographic and Living Conditions Statistics, Italian National Institute of Statistics, 00184 Rome, Italy
Gianni Corsetti: Division of Population Register, Demographic and Living Conditions Statistics, Italian National Institute of Statistics, 00184 Rome, Italy
Antonino Bella: Department of Infectious Diseases, Istituto Superiore di Sanità, 00161 Rome, Italy
Stefano Boros: Department of Infectious Diseases, Istituto Superiore di Sanità, 00161 Rome, Italy
Daniele Petrone: Department of Infectious Diseases, Istituto Superiore di Sanità, 00161 Rome, Italy
Flavia Riccardo: Department of Infectious Diseases, Istituto Superiore di Sanità, 00161 Rome, Italy
Antonello Maruotti: Dipartimento GEPLI, Libera Università Maria Ss Assunta, 00193 Rome, Italy
Patrizio Pezzotti: Department of Infectious Diseases, Istituto Superiore di Sanità, 00161 Rome, Italy

IJERPH, 2022, vol. 19, issue 24, 1-13

Abstract: Introduction: Excess mortality (EM) is a valid indicator of COVID-19’s impact on public health. Several studies regarding the estimation of EM have been conducted in Italy, and some of them have shown conflicting values. We focused on three estimation models and compared their results with respect to the same target population, which allowed us to highlight their strengths and limitations. Methods: We selected three estimation models: model 1 (Maruotti et al.) is a Negative-Binomial GLMM with seasonal patterns; model 2 (Dorrucci et al.) is a Negative Binomial GLM epidemiological approach; and model 3 (Scortichini et al.) is a quasi-Poisson GLM time-series approach with temperature distributions. We extended the time windows of the original models until December 2021, computing various EM estimates to allow for comparisons. Results: We compared the results with our benchmark, the ISS-ISTAT official estimates. Model 1 was the most consistent, model 2 was almost identical, and model 3 differed from the two. Model 1 was the most stable towards changes in the baseline years, while model 2 had a lower cross-validation RMSE. Discussion: Presently, an unambiguous explanation of EM in Italy is not possible. We provide a range that we consider sound, given the high variability associated with the use of different models. However, all three models accurately represented the spatiotemporal trends of the pandemic waves in Italy.

Keywords: COVID-19; coronavirus; all-cause mortality; excess deaths; statistical models (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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