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

Understanding excess mortality in 2022: The dual impact of COVID-19 and heatwaves on the Italian elderly population

In this study, we employ a comprehensive approach to model the concurrent effects of the COVID-19 epidemic and heatwaves on all-cause excess mortality. Our investigation uncovers distinct peaks in excess mortality, notably among individuals aged 80 years and older, revealing a strong positive correlation with excess temperatures (ET) during the summer of 2022 in Italy. Furthermore, we identify a notable role played by COVID-19 hospitalizations, exhibiting regional disparities, particularly during the winter months. Leveraging functional data regression, we offer robust and coherent insights into the excess mortality trends observed in Italy throughout 2022.

SARS-CoV-2 Infection, Vaccination and Risk of Death in People with An Oncological Disease in Northeast Italy

People with a history of cancer have a higher risk of death when infected with SARS-CoV-2. COVID-19 vaccines in cancer patients proved safe and effective, even if efficacy may be lower than in the general population. In this population-based study, we compare the risk of dying of cancer patients diagnosed with COVID-19 in 2021, vaccinated or non-vaccinated against SARS-CoV-2 and residing in Friuli Venezia Giulia or in the province of Reggio Emilia. An amount of 800 deaths occurred among 6583 patients; the risk of death was more than three times higher among unvaccinated compared to vaccinated ones [HR 3.4; 95% CI 2.9-4.1]. The excess risk of death was stronger in those aged 70-79 years [HR 4.6; 95% CI 3.2-6.8], in patients with diagnosis made <1 year [HR 8.5; 95% CI 7.3-10.5] and in all cancer sites, including hematological malignancies. The study results indicate that vaccination against SARS-CoV-2 infection is a necessary tool to be included in the complex of oncological therapies aimed at reducing the risk of death.

The Effect of COVID-19 Pandemic on Overall and Cause-Specific Mortality in Pavia, Northern Italy: Updated Estimates for the Year 2021

Excess mortality estimates are considered relevant indicators of direct and indirect pandemic effects on the population. Scant data have been published on cause-specific excess mortality. Using individual-level administrative data covering the Pavia province of Italian northern Lombardy region, we provided all-cause and cause-specific raw (RMR) and age-standardized (ASMR) mortality rates in 2021 and 2015-2019, the rate ratio, and 95% confidence intervals, overall and by sex. We obtained the excess deaths in 2021 as the difference between the number of observed and expected deaths from all causes and the two leading causes of death (all neoplasms and circulatory system diseases) by fitting over-dispersed quasi-Poisson regression models, accounting for temporal, seasonal and demographic changes. The total ASMR in 2021 was 972.4/100,000 (6836 certified deaths), with the highest ASMRs for circulatory system diseases (272.6/100,000) and all neoplasms (270.3/100,000), followed by COVID-19 (94.8/100,000 and 662 deaths). Compared to the expected, we estimated a total of 6.2% excess deaths in 2021 (7.2% in males and 5.4% in females), with no excess deaths from all neoplasms and a 6.2% reduction from circulatory system diseases. COVID-19 continued to affect total mortality in 2021, albeit to a lesser extent than in 2020, consistently with national patterns.

Assessment of Excess Mortality in Italy in 2020-2021 as a Function of Selected Macro-Factors

BACKGROUND

Excess mortality (EM) can reliably capture the impact of a pandemic, this study aims at assessing the numerous factors associated with EM during the COVID-19 pandemic in Italy.

METHODS

Mortality records (ISTAT 2015-2021) aggregated in the 610 Italian Labour Market Areas (LMAs) were used to obtain the EM P-scores to associate EM with socioeconomic variables. A two-step analysis was implemented: (1) Functional representation of EM and clustering. (2) Distinct functional regression by cluster.

RESULTS

The LMAs are divided into four clusters: 1 low EM; 2 moderate EM; 3 high EM; and 4 high EM-first wave. Low-Income showed a negative association with EM clusters 1 and 4. Population density and percentage of over 70 did not seem to affect EM significantly. Bed availability positively associates with EM during the first wave. The employment rate positively associates with EM during the first two waves, becoming negatively associated when the vaccination campaign began.

CONCLUSIONS

The clustering shows diverse behaviours by geography and time, the impact of socioeconomic characteristics, and local governments and health services' responses. The LMAs allow to draw a clear picture of local characteristics associated with the spread of the virus. The employment rate trend confirmed that essential workers were at risk, especially during the first wave.