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1.
Ann Ist Super Sanita ; 58(1): 25-33, 2022.
Article in English | MEDLINE | ID: covidwho-1761028

ABSTRACT

AIMS: To assess the impact of the COVID-19 pandemic on all-cause mortality in Italy during the first wave of the epidemic, taking into consideration the geographical heterogeneity of the spread of COVID-19. METHODS: This study is a retrospective, population-based cohort study using national statistics throughout Italy. Survival analysis was applied to data aggregated by day of death, age groups, sex, and Italian administrative units (107 provinces). We applied Cox models to estimate the relative hazards (RH) of excess mortality, comparing all-cause deaths in 2020 with the expected deaths from all causes in the same time period. The RH of excess deaths was estimated in areas with a high, moderate, and low spread of COVID-19. We reported the estimate also restricting the analysis to the period of March-April 2020 (first peak of the epidemic). RESULTS: The study population consisted of 57,204,501 individuals living in Italy as of January 1, 2020. The number of excess deaths was 36,445, which accounts for 13.4% of excess mortalities from all causes during January-May 2020 (i.e., RH = 1.134; 95% confidence interval (CI): 1.129-1.140). In the macro-area with a relatively higher spread of COVID-19 (i.e., incidence rate, IR): 450-1,610 cases per 100,000 residents), the RH of excess deaths was 1.375 (95% CI: 1.364-1.386). In the area with a relatively moderate spread of COVID-19 (i.e., IR: 150-449 cases) it was 1.049 (95% CI: 1.038-1.060). In the area with a relatively lower spread of COVID-19 (i.e., IR: 30-149 cases), it was 0.967 (95% CI: 0.959-0.976). Between March and April (peak months of the first wave of the epidemic in Italy), we estimated an excess mortality from all causes of 43.5%. The RH of all-cause mortality for increments of 500 cases per 100,000 residents was 1.352 (95% CI: 1.346-1.359), corresponding to an increase of about 35%. CONCLUSIONS: Our analysis, making use of a population-based cohort model, estimated all-cause excess mortality in Italy taking account of both time period and of COVID-19 geographical spread. The study highlights the importance of a temporal/geographic framework in analyzing the risk of COVID-19-epidemy related mortality.


Subject(s)
COVID-19 , Cohort Studies , Humans , Italy/epidemiology , Pandemics , Retrospective Studies
2.
Epidemiol Prev ; 45(6): 463-469, 2021.
Article in English | MEDLINE | ID: covidwho-1629663

ABSTRACT

BACKGROUND: there is increasing concern that the COVID-19 pandemic has disproportionately affected the most vulnerable individuals. OBJECTIVES: to determine whether education inequalities have widened during the first wave of the COVID-19 pandemic in Italy. DESIGN: historic cohort study based on administrative databases. SETTING AND PARTICIPANTS: the study was based on subjects registered in the Base Register of Individuals on 01.01.2019, aged >=35 years, and followed-up until 30.06.2020. MAIN OUTCOME MEASURES: education inequalities in mortality before, during the first phase (March-April), and during the second phase (May-June) of the first pandemic wave in Italy were measured through the mortality rate ratios (MRRs). MMRs were estimated through negative binomial models. The interaction term between period and education was tested through the likelihood ratio test. RESULTS: the cohort included 37,976,670 individuals, and 719,665 of them died over the follow-up. In high pandemic areas, the MRR among less educated men were: 1.48 (95%CI 1.42-1.55) in the pre-pandemic period, 1.45 (95%CI 1.36-1.55) in the first phase and 1.42 (95%CI 1.30-1.56) in the second phase of the pandemic (p-value: 0.92). Corresponding figures among women were: 1.26 (95%CI 1.21-1.32), 1.39 (95%CI 1.30-1.49), and 1.35 (95%CI 1.23-1.48); p-value: 0.03. The MRRs substantially increased in the first pandemic phase among women aged 35-64 years (from 1.48 to 1.98; p-value; 0.011) and 65-79 years (from 1.22 to 1.51; p-value: 0.017). During the second phase, the MRRs returned to the values observed before the pandemic. CONCLUSIONS: in Italy, education inequality in mortality widened during the COVID-19 pandemic among working-age women and those aged 65-79 years.


Subject(s)
COVID-19 , Pandemics , Aged , Cohort Studies , Female , Humans , Italy/epidemiology , Male , Mortality , SARS-CoV-2
3.
Front Public Health ; 9: 669209, 2021.
Article in English | MEDLINE | ID: covidwho-1337690

ABSTRACT

COVID-19 dramatically influenced mortality worldwide, in Italy as well, the first European country to experience the Sars-Cov2 epidemic. Many countries reported a two-wave pattern of COVID-19 deaths; however, studies comparing the two waves are limited. The objective of the study was to compare all-cause excess mortality between the two waves that occurred during the year 2020 using nationwide data. All-cause excess mortalities were estimated using negative binomial models with time modeled by quadratic splines. The models were also applied to estimate all-cause excess deaths "not directly attributable to COVD-19", i.e., without a previous COVID-19 diagnosis. During the first wave (25th February-31st May), we estimated 52,437 excess deaths (95% CI: 49,213-55,863) and 50,979 (95% CI: 50,333-51,425) during the second phase (10th October-31st December), corresponding to percentage 34.8% (95% CI: 33.8%-35.8%) in the second wave and 31.0% (95%CI: 27.2%-35.4%) in the first. During both waves, all-cause excess deaths percentages were higher in northern regions (59.1% during the first and 42.2% in the second wave), with a significant increase in the rest of Italy (from 6.7% to 27.1%) during the second wave. Males and those aged 80 or over were the most hit groups with an increase in both during the second wave. Excess deaths not directly attributable to COVID-19 decreased during the second phase with respect to the first phase, from 10.8% (95% CI: 9.5%-12.4%) to 7.7% (95% CI: 7.5%-7.9%), respectively. The percentage increase in excess deaths from all causes suggests in Italy a different impact of the SARS-CoV-2 virus during the second wave in 2020. The decrease in excess deaths not directly attributable to COVID-19 may indicate an improvement in the preparedness of the Italian health care services during this second wave, in the detection of COVID-19 diagnoses and/or clinical practice toward the other severe diseases.


Subject(s)
COVID-19 , COVID-19 Testing , Europe , Humans , Italy/epidemiology , Male , Pandemics , RNA, Viral , SARS-CoV-2
5.
Nat Med ; 26(12): 1919-1928, 2020 12.
Article in English | MEDLINE | ID: covidwho-872715

ABSTRACT

The Coronavirus Disease 2019 (COVID-19) pandemic has changed many social, economic, environmental and healthcare determinants of health. We applied an ensemble of 16 Bayesian models to vital statistics data to estimate the all-cause mortality effect of the pandemic for 21 industrialized countries. From mid-February through May 2020, 206,000 (95% credible interval, 178,100-231,000) more people died in these countries than would have had the pandemic not occurred. The number of excess deaths, excess deaths per 100,000 people and relative increase in deaths were similar between men and women in most countries. England and Wales and Spain experienced the largest effect: ~100 excess deaths per 100,000 people, equivalent to a 37% (30-44%) relative increase in England and Wales and 38% (31-45%) in Spain. Bulgaria, New Zealand, Slovakia, Australia, Czechia, Hungary, Poland, Norway, Denmark and Finland experienced mortality changes that ranged from possible small declines to increases of 5% or less in either sex. The heterogeneous mortality effects of the COVID-19 pandemic reflect differences in how well countries have managed the pandemic and the resilience and preparedness of the health and social care system.


Subject(s)
COVID-19/mortality , Demography , Developed Countries/statistics & numerical data , Mortality , Pandemics , Population Dynamics , COVID-19/epidemiology , Cause of Death/trends , Female , Geography , Humans , Industrial Development/statistics & numerical data , Male , Mortality/trends , Population Density , Population Dynamics/statistics & numerical data , Population Dynamics/trends , Public Policy , SARS-CoV-2/physiology , Time Factors
6.
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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