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1.
Med Lav ; 114(3): e2023028, 2023 Jun 12.
Article in English | MEDLINE | ID: covidwho-20232123

ABSTRACT

BACKGROUND: Italy had a persistent excess of total mortality up to July 2022. This study provides updated estimates of excess mortality in Italy until February 2023. METHODS: Mortality and population data from 2011 to 2019 were used to estimate the number of expected deaths during the pandemic. Expected deaths were obtained using over-dispersed Poisson regression models, fitted separately for men and women, including calendar year, age group, and a smoothed function of the day of the year as predictors. The excess deaths were then obtained by calculating the difference between observed and expected deaths and were computed at all ages and working ages (25-64 years). RESULTS: We estimated 26,647 excess deaths for all ages and 1248 for working ages from August to December 2022, resulting in a percent excess mortality of 10.2% and 4.7%, respectively. No excess mortality was detected in January and February 2023. CONCLUSIONS: Our study indicates substantial excess mortality beyond those directly attributed to COVID-19 during the BA.4 and BA.5 Omicron wave in the latter half of 2022. This excess could be attributed to additional factors, such as the heatwave during the summer of 2022 and the early onset of the influenza season.


Subject(s)
COVID-19 , Male , Humans , Female , Italy , Pandemics , Seizures
2.
Panminerva Med ; 2021 Apr 28.
Article in English | MEDLINE | ID: covidwho-2307208

ABSTRACT

BACKGROUND: Differences between total deaths registered during the Covid-19 pandemic and those registered in a previous reference period is a valid measure of the pandemic effect. However, this does not consider demographic changes and temporal trends in mortality. OBJECTIVE: To estimate the excess mortality in 2020 in Italy considering demographic changes and temporal trends in mortality. METHODS: We used daily mortality and population data for the 2011-2019 period to estimate the expected deaths in 2020. Expected deaths were estimated, separately by sex, through an over-dispersed Poisson regression model including calendar year and age group as covariates, a smooth function of the year's week, and the logarithm of the population as offset. The difference between observed and expected deaths was considered a measure of excess mortality. RESULTS: In 2020, 746,146 deaths occurred in Italy. We estimated an excess mortality of 90,725 deaths (95% CI: 86,503-94,914), which became 99,289 deaths after excluding January and February, when mortality was lower than expected. The excess was higher among men (49,422 deaths) than women (41,303 deaths) and it was mostly detected at ages ≥80 (60,224 deaths) and ages 65-79 (25,791 deaths), while among the population aged 25-49 and 50-64 we estimated an excess of 281 and 4764 deaths, respectively. CONCLUSIONS: After considering demographic changes and temporal improvement in mortality the excess deaths in 2020 still remains above 90,000 deaths. More important, considering these factors, the excess at ages <80 years is revised upwards, while the excess at older ages is revised downwards.

4.
Med Lav ; 113(5): e2022046, 2022 Oct 24.
Article in English | MEDLINE | ID: covidwho-2084881

ABSTRACT

BACKGROUND: The impact of new lineages and sub-lineages of Omicron on total and excess mortality is largely unknown. This study aims to provide estimates of excess mortality during the circulation of the Omicron variant in Italy updated to July 2022. METHODS: Over-dispersed Poisson regression models, fitted separately for men and women, on 2011-2019 mortality data were used to estimate the expected number of deaths during the Covid-19 pandemic. The excess deaths were then obtained by the difference between observed and expected deaths and computed at all ages and at working ages (25-64 years). RESULTS: Between April and June 2022, we estimated 9,631 excess deaths (+6.3%) at all ages (4,400 in April, 3,369 in May, 1,862 in June) and 12,090 in July 2022 (+23.4%). At working ages, the excess was 763 (+4.9%) in April-June 2022 and 679 (+13.0%) in July 2022. CONCLUSIONS: Excess total mortality persisted during the circulation of different lineages and sub-lineages of the Omicron variant in Italy. This excess was not limited to the elderly population but involved also working age individuals, though the absolute number of deaths was small. The substantial excess found in July 2022 is, however, largely attributable to high temperatures. At the end of the year, this may translate into 30 to 35,000 excess deaths, i.e. over 5% excess mortality. This reversed the long-term trend toward increasing life expectancy, with the relative implications in social security and retirement schemes.


Subject(s)
COVID-19 , Pandemics , Male , Humans , Female , Aged , Adult , Middle Aged , SARS-CoV-2 , Italy/epidemiology
5.
Med Lav ; 113(3): e2022030, 2022 Jun 28.
Article in English | MEDLINE | ID: covidwho-1912559

ABSTRACT

BACKGROUND: This study provides updated estimates of the excess deaths in Italy with a focus on the working-age population. METHODS: Over-dispersed Poisson regression models, fitted on 2011-2019 mortality data, and including terms for age, calendar year and a smooth function of the week of the year, were used to estimate the expected number of deaths during the Covid-19 pandemic. The excess deaths were then obtained by the difference between observed and expected deaths and reported according to the pandemic periods defined by the predominant circulating variant of SARS-CoV-2. RESULTS: Around 170,700 excess deaths at all ages were estimated between March 2020 and March 2022 in Italy with most of the excess occurring during the pre-Delta and Delta period, and 2930 excess deaths (+2.5%) during the Omicron wave. The excesses among the working age population were: 10,425 deaths (+11.8%) during the pre-Delta period, 2460 (+9.4%) during the Delta wave, 283 (+2.2%) during the transition period to Delta. Mortality was lower than expected during the Omicron wave (-6.1%). CONCLUSIONS: Over the periods preceding the Omicron wave, Covid-19 caused around 12,800 excess deaths among individuals of working age, accounting for over 10% excess death. This excess was no longer observed during the Omicron wave.


Subject(s)
COVID-19 , Humans , Italy/epidemiology , Pandemics , SARS-CoV-2 , Seizures
6.
Med Lav ; 113(2): e2022021, 2022 Apr 26.
Article in English | MEDLINE | ID: covidwho-1818999

ABSTRACT

BACKGROUND: New releases of daily mortality data are available in Italy; the last containing data up to 31 January 2022. This study revises previous estimates of the excess mortality in Italy during the Covid-19 pandemic. METHODS: Excess mortality was estimated as the difference between the number of registered deaths and the expected deaths. Expected deaths in March-December 2020, January-December 2021 and January 2022 were estimated separately by sex, through an over-dispersed Poisson regression model using mortality and population data for the period 2011-2019. The models included terms for calendar year, age group, a smooth function of week of the year and the natural logarithm of the population as offset term. RESULTS: We estimated 99,334 excess deaths (+18.8%) between March and December 2020, 61,808 deaths (+9.5%) in 2021 and 4143 deaths (+6.1%) in January 2022. Over the whole pandemic period, 13,039 excess deaths (+10.2%) were estimated in the age group 25-64 years with most of the excess observed among men [10,025 deaths (+12.6%) among men and 3014 deaths (+6.3%) among women]. CONCLUSIONS: Up to 31 January 2022, over 165 thousand excess deaths were estimated in Italy, of these about 8% occurred among the working age population. Despite high vaccination uptake, excess mortality is still observed in recent months.


Subject(s)
COVID-19 , Pandemics , Adult , Female , Humans , Italy/epidemiology , Male , Middle Aged
8.
Med Lav ; 112(6): 414-421, 2021 Dec 23.
Article in English | MEDLINE | ID: covidwho-1591099

ABSTRACT

BACKGROUND: Italy was severely hit by the Covid-19 pandemic with an excess of around 90,000 total deaths in 2020. Comparable data in 2021 are needed for monitoring the effects of the interventions adopted to control its spread and reduce the burden. This study estimates the excess mortality in Italy in the first eight months of 2021, with a focus on the working age population. METHODS: Excess mortality was estimated as difference between the number of registered deaths and the expected deaths. Expected deaths in March-December 2020 and January-August 2021 were estimated separately by sex, through an over-dispersed Poisson regression model using mortality and population data for the period 2011-2019 (before the Covid-19 outbreak). The models included terms for calendar year, age group, a smooth function of week of the year and the natural logarithm of the population as offset term.  Results: In the first eight months of 2021, we estimated 34,599 excess deaths (+7.9% of the expected deaths), of these 3667 were among individuals of working age (25-64 years). In this age group, mortality was 8.2% higher than expected with higher excesses among men (2972 deaths, +10.7%) than women (695 deaths, +4.1%). CONCLUSIONS: The excess deaths in the first eight months of 2021 account for about one third of that registered in 2020. Current data indicate that around 5000 excess deaths are expected by the end of the year, leading to a total excess for 2021 of around 40 thousand deaths. Despite the absence of influenza in January-March 2021, a relevant excess was also observed among the working age population.


Subject(s)
COVID-19 , Pandemics , Adult , Female , Humans , Italy/epidemiology , Male , Middle Aged , SARS-CoV-2
14.
BMC Pulm Med ; 20(1): 203, 2020 Jul 29.
Article in English | MEDLINE | ID: covidwho-684654

ABSTRACT

BACKGROUND: COVID-19 is an infectious disease caused by a novel coronavirus (SARS-CoV-2). The immunopathogenesis of the infection is currently unknown. Healthcare workers (HCWs) are at highest risk of infection and disease. Aim of the study was to assess the sero-prevalence of SARS-CoV-2 in an Italian cohort of HCWs exposed to COVID-19 patients. METHODS: A point-of-care lateral flow immunoassay (BioMedomics IgM-IgG Combined Antibody Rapid Test) was adopted to assess the prevalence of IgG and IgM against SARS-CoV-2. It was ethically approved ("Milano Area 1" Ethical Committee prot. n. 2020/ST/057). RESULTS: A total of 202 individuals (median age 45 years; 34.7% males) were retrospectively recruited in an Italian hospital (Milan, Italy). The percentage (95% CI) of recruited individuals with IgM and IgG were 14.4% (9.6-19.2%) and 7.4% (3.8-11.0%), respectively. IgM were more frequently found in males (24.3%), and in individuals aged 20-29 (25.9%) and 60-69 (30.4%) years. No relationship was found between exposure to COVID-19 patients and IgM and IgG positivity. CONCLUSIONS: The present study did show a low prevalence of SARS-CoV-2 IgM in Italian HCWs. New studies are needed to assess the prevalence of SARS-CoV-2 antibodies in HCWs exposed to COVID-19 patients, as well the role of neutralizing antibodies.


Subject(s)
Antibodies, Viral , Betacoronavirus/immunology , Clinical Laboratory Techniques , Coronavirus Infections , Health Personnel/statistics & numerical data , Infectious Disease Transmission, Patient-to-Professional , Pandemics , Pneumonia, Viral , Adult , Age Factors , Aged , Antibodies, Viral/analysis , Antibodies, Viral/classification , Betacoronavirus/isolation & purification , COVID-19 , COVID-19 Testing , Clinical Laboratory Techniques/methods , Clinical Laboratory Techniques/statistics & numerical data , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Coronavirus Infections/immunology , Female , Humans , Infectious Disease Transmission, Patient-to-Professional/prevention & control , Infectious Disease Transmission, Patient-to-Professional/statistics & numerical data , Italy/epidemiology , Male , Middle Aged , Occupational Exposure/statistics & numerical data , Pneumonia, Viral/diagnosis , Pneumonia, Viral/epidemiology , Pneumonia, Viral/immunology , SARS-CoV-2 , Seroepidemiologic Studies , Sex Factors
15.
Minerva Med ; 111(4): 308-314, 2020 Aug.
Article in English | MEDLINE | ID: covidwho-505753

ABSTRACT

BACKGROUND: To date, the European experience with COVID-19 mortality has been different to that observed in China and Asia. We aimed to forecast mortality trends in the 27 countries of the European Union (EU), plus Switzerland and the UK, where lockdown dates and confinement interventions have been heterogeneous, and to explore its determinants. METHODS: We have adapted our predictive model of COVID-19-related mortality, which rested on the observed mortality within the first weeks of the outbreak and the date of the respective lockdown in each country. It was applied in a training set of three countries (Italy, Germany and Spain), and then applied to the EU plus the UK and Switzerland. In addition, we explored the effects of timeliness and rigidity of the lockdown (on a five-step scale) and population density in our forecasts. We report r2, and percent variation of expected versus observed deaths, all following TRIPOD guidance. RESULTS: We identified a homogeneous distribution of deaths, and found a median of 24 days after lockdown adoption to reach the maximum daily deaths. Strikingly, cumulative deaths up to April 25th, 2020 observed in Europe separated countries in three waves, according to the time lockdown measures were adopted following the onset of the outbreak: after a week, within a week, or even prior to the outbreak (r2=0.876). In contrast, no correlation neither with lockdown rigidity nor population density were observed. CONCLUSIONS: The European experience confirms that early, effective interventions of lockdown are fundamental to minimizing the COVID-19 death toll.


Subject(s)
Coronavirus Infections/mortality , Coronavirus Infections/prevention & control , Pandemics/prevention & control , Pneumonia, Viral/mortality , Pneumonia, Viral/prevention & control , Population Density , Quarantine/statistics & numerical data , COVID-19 , Europe/epidemiology , European Union , Humans , Quarantine/standards , Switzerland/epidemiology , Time Factors , United Kingdom/epidemiology
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