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1.
Nat Commun ; 13(1): 482, 2022 01 25.
Article in English | MEDLINE | ID: covidwho-1655580

ABSTRACT

The impact of the COVID-19 pandemic on excess mortality from all causes in 2020 varied across and within European countries. Using data for 2015-2019, we applied Bayesian spatio-temporal models to quantify the expected weekly deaths at the regional level had the pandemic not occurred in England, Greece, Italy, Spain, and Switzerland. With around 30%, Madrid, Castile-La Mancha, Castile-Leon (Spain) and Lombardia (Italy) were the regions with the highest excess mortality. In England, Greece and Switzerland, the regions most affected were Outer London and the West Midlands (England), Eastern, Western and Central Macedonia (Greece), and Ticino (Switzerland), with 15-20% excess mortality in 2020. Our study highlights the importance of the large transportation hubs for establishing community transmission in the first stages of the pandemic. Here, we show that acting promptly to limit transmission around these hubs is essential to prevent spread to other regions and countries.


Subject(s)
Bayes Theorem , COVID-19/mortality , Pandemics/statistics & numerical data , SARS-CoV-2/isolation & purification , Adult , Aged , Aged, 80 and over , COVID-19/epidemiology , COVID-19/virology , Cause of Death , England/epidemiology , Female , Geography , Greece/epidemiology , Humans , Italy/epidemiology , Male , Middle Aged , Pandemics/prevention & control , SARS-CoV-2/physiology , Spain/epidemiology , Survival Rate , Switzerland/epidemiology
2.
Spat Spatiotemporal Epidemiol ; 39: 100443, 2021 11.
Article in English | MEDLINE | ID: covidwho-1459135

ABSTRACT

The study of the impacts of air pollution on COVID-19 has gained increasing attention. However, most of the existing studies are based on a single country, with a high degree of variation in the results reported in different papers. We attempt to inform the debate about the long-term effects of air pollution on COVID-19 by conducting a multi-country analysis using a spatial ecological design, including Canada, Italy, England and the United States. The model allows the residual spatial autocorrelation after accounting for covariates. It is concluded that the effects of PM2.5 and NO2 are inconsistent across countries. Specifically, NO2 was not found to be an important factor affecting COVID-19 infection, while a large effect for PM2.5 in the US is not found in the other three countries. The Population Attributable Fraction for COVID-19 incidence ranges from 3.4% in Canada to 45.9% in Italy, although with considerable uncertainty in these estimates.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollutants/toxicity , Air Pollution/analysis , Air Pollution/statistics & numerical data , Environmental Exposure/analysis , Environmental Exposure/statistics & numerical data , Humans , Particulate Matter/analysis , Particulate Matter/toxicity , SARS-CoV-2 , United States/epidemiology
3.
PLoS One ; 15(10): e0240286, 2020.
Article in English | MEDLINE | ID: covidwho-841410

ABSTRACT

In this study we present the first comprehensive analysis of the spatio-temporal differences in excess mortality during the COVID-19 pandemic in Italy. We used a population-based design on all-cause mortality data, for the 7,904 Italian municipalities. We estimated sex-specific weekly mortality rates for each municipality, based on the first four months of 2016-2019, while adjusting for age, localised temporal trends and the effect of temperature. Then, we predicted all-cause weekly deaths and mortality rates at municipality level for the same period in 2020, based on the modelled spatio-temporal trends. 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 one week lag, with higher mortality from the beginning of March and 6,942 (6,142 to 7,667) and 8,033 (7,061 to 9,044) total excess deaths respectively. We observed marked geographical differences also at municipality level. For males, 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 males in the city of Pesaro (Central Italy), in stark contrast with the rest of the region, which does not show evidence of excess deaths. We provided a fully probabilistic analysis of excess mortality during the COVID-19 pandemic at sub-national level, suggesting a differential direct and indirect effect in space and time. Our model can be used to help policy-makers target measures locally to contain the burden on the health-care system as well as reducing social and economic consequences. Additionally, this framework can be used for real-time mortality surveillance, continuous monitoring of local temporal trends and to flag where and when mortality rates deviate from the expected range, which might suggest a second wave of the pandemic.


Subject(s)
Cause of Death/trends , Coronavirus Infections/epidemiology , Databases, Factual , Pneumonia, Viral/epidemiology , Bayes Theorem , Betacoronavirus/isolation & purification , COVID-19 , Coronavirus Infections/mortality , Coronavirus Infections/virology , Female , Humans , Italy/epidemiology , Male , Models, Theoretical , Pandemics , Pneumonia, Viral/mortality , Pneumonia, Viral/virology , SARS-CoV-2
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