Increased mortality in socioeconomic disadvantaged municipalities during the first phase of the COVID-19 pandemic in Lombardy region.

BACKGROUND: Lombardy was the first European region most severely affected by the coronavirus disease 2019 pandemic in the spring of 2020. During that period, a substantial increase in socioeconomic inequality in total mortality was observed. This study aims to evaluate mortality data in the region up to September 2023 to verify whether the increased disparities between the poorest and the wealthiest municipalities persisted in the subsequent phases of the pandemic. METHODS: This study analyzed mortality data from January 2019 to September 2023 in Lombardy's municipalities by month and pandemic phases characterized by the predominance of the different severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Municipalities were grouped according to the average income or pension of their residents. Age-standardized mortality rates (ASMRs) and the ASMR ratio between the poorest and the wealthiest municipalities were compared throughout the study period. RESULTS: In the pre-pandemic period (January 2019 - February 2020), the ASMR ratio at all ages between the poorest and the wealthiest municipalities fluctuated between 1.12 [95% confidence interval (CI): 1.07-1.16] and 1.29 (95% CI: 1.25-1.34). In March 2020, the ASMR ratio increased to 1.49 (95% CI: 1.45-1.52 95%) and returned to values registered before the pandemic thereafter. A similar pattern was observed in the analysis of mortality ≥ 65, using the average pension for group municipalities. CONCLUSIONS: During the dramatic circumstances that the region faced in March 2020, pre-existing socioeconomic inequalities substantially widened. With the reorganization of the health system and the availability of vaccines, these disparities returned to the levels recorded before the pandemic.

No Excess Total Mortality in Italy in the First Semester of 2023 at All Ages and in the Working Age Population.

BACKGROUND: Italy experienced a sustained excess in total mortality between March 2020 and December 2022, resulting in approximately 226,000 excess deaths. This study extends the estimate of excess mortality in the country until June 2023, evaluating the persistence of excess mortality. METHODS: We used mortality and population data from 2011 to 2019 to establish a baseline for expected deaths during the pandemic. Over-dispersed Poisson regression models were employed, stratified by sex, to predict expected deaths. These models included calendar year, age group, and a smoothed function for the day of the year as predictors. Excess mortality was then calculated for all ages and working ages (25-64 years). RESULTS: From January to June 2023, we found a reduction in the number of deaths compared to the expected ones: 6,933 fewer deaths across all age groups and 1,768 fewer deaths in the working age category. This corresponds to a 2.1% and 5.2% decrease in mortality, respectively. CONCLUSIONS: The excess mortality observed in Italy from March to December 2022 was no longer observed in the first six months of 2023.

ChatGPT: unlocking the potential of Artifical Intelligence in COVID-19 monitoring and prediction.

BACKGROUND: The COVID-19 pandemic has had an unprecedent impact of everyday life with deleterious consequences on global health, economics, and society. Thus, accurate and timely information is critical for monitoring its spread and mitigating its impact. ChatGPT is a large language model chatbot with artificial intelligence, developed by OpenAI, that can provide both textual content and R code for predictive models. It may prove to be useful in analyzing and interpreting COVID-19-related data. METHODS: This paper explores the application of ChatGPT to the monitoring of the COVID-19 pandemic, presenting R code for predictive models and demonstrating the model's capabilities in sentiment analysis, information extraction, and predictive modelling. We used the prediction models suggested by ChatGPT to predict the daily number of COVID-19 deaths in Italy. The prediction accuracy of the models was compared using the following metrics: mean squared error (MSE), mean absolute deviation (MAD) and root mean squared error (RMSE). RESULTS: ChatGPT suggested three different predictive models, including ARIMA, Random Forest and Prophet. The ARIMA model outperformed the other two models in predicting the daily number of COVID-19 deaths in Italy, with lower MSE, MAD, and RMSE values as compared to the Random Forest and Prophet. CONCLUSIONS: This paper demonstrates the potential of ChatGPT as a valuable tool in the monitoring of the pandemic. By processing large amounts of data and providing relevant information, ChatGPT has the potential to provide accurate and timely insights, and support decision-making processes to mitigate the spread and impact of pandemics. The paper highlights the importance of exploring the capabilities of artificial intelligence in the management of public emergencies and provides a starting point for future research in this area.

Excess Total Mortality in Italy: An Update to February 2023 with Focus on Working Ages.

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.

Can artificial intelligence help for scientific writing?

This paper discusses the use of Artificial Intelligence Chatbot in scientific writing. ChatGPT is a type of chatbot, developed by OpenAI, that uses the Generative Pre-trained Transformer (GPT) language model to understand and respond to natural language inputs. AI chatbot and ChatGPT in particular appear to be useful tools in scientific writing, assisting researchers and scientists in organizing material, generating an initial draft and/or in proofreading. There is no publication in the field of critical care medicine prepared using this approach; however, this will be a possibility in the next future. ChatGPT work should not be used as a replacement for human judgment and the output should always be reviewed by experts before being used in any critical decision-making or application. Moreover, several ethical issues arise about using these tools, such as the risk of plagiarism and inaccuracies, as well as a potential imbalance in its accessibility between high- and low-income countries, if the software becomes paying. For this reason, a consensus on how to regulate the use of chatbots in scientific writing will soon be required.

Updated estimates of excess total mortality in Italy during the circulation of the BA.2 and BA.4-5 Omicron variants: April-July 2022.

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.

No excess mortality among working-age Italians during the Omicron wave of Covid-19.

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.

Excess total mortality during the Covid-19 pandemic in Italy: updated estimates indicate persistent excess in recent months.

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.

Excess mortality in Italy in 2020 by sex and age groups accounting for demographic changes and temporal trends in mortality.

BACKGROUND: Differences between total deaths registered during the COVID-19 pandemic and those registered in a previous reference period are the most frequently used measures of the pandemic effect. However, these measures do not consider demographic changes and temporal trends in mortality. In this study we estimated 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 overdispersed 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.

Excess total mortality in 2021 in Italy was about one third of that observed in 2020.

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.

Clinical and Molecular Diagnosis of Beckwith-Wiedemann Syndrome with Single- or Multi-Locus Imprinting Disturbance.

Beckwith-Wiedemann syndrome (BWS) is a clinically and genetically heterogeneous overgrowth disease. BWS is caused by (epi)genetic defects at the 11p15 chromosomal region, which harbors two clusters of imprinted genes, IGF2/H19 and CDKN1C/KCNQ1OT1, regulated by differential methylation of imprinting control regions, H19/IGF2:IG DMR and KCNQ1OT1:TSS DMR, respectively. A subset of BWS patients show multi-locus imprinting disturbances (MLID), with methylation defects extended to other imprinted genes in addition to the disease-specific locus. Specific (epi)genotype-phenotype correlations have been defined in order to help clinicians in the classification of patients and referring them to a timely diagnosis and a tailored follow-up. However, specific phenotypic correlations have not been identified among MLID patients, thus causing a debate on the usefulness of multi-locus testing in clinical diagnosis. Finally, the high incidence of BWS monozygotic twins with discordant phenotypes, the high frequency of BWS among babies conceived by assisted reproductive technologies, and the female prevalence among BWS-MLID cases provide new insights into the timing of imprint establishment during embryo development. In this review, we provide an overview on the clinical and molecular diagnosis of single- and multi-locus BWS in pre- and post-natal settings, and a comprehensive analysis of the literature in order to define possible (epi)genotype-phenotype correlations in MLID patients.

SARS-CoV-2 specific serological pattern in healthcare workers of an Italian COVID-19 forefront hospital.

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.