Flu shot in the era of COVID-19 vaccination: findings from a research hospital of Rome : Domenico Pascucci

Influenza represents a major burden for public health. Healthcare workers (HCWs) are a priority target group for flu vaccination. During the COVID-19 pandemic, when SARS-CoV-2 vaccines were not yet available, susceptibility to influenza vaccination especially by HCWs increased. The aim of this study is to analyze the flu vaccination coverage among HCWs and to study which factors affected their adherence given the concomitant COVID-19 vaccination. The retrospective study was conducted in an Italian research hospital from October 2021 to January 2022. A total of 7,048 individuals was included. Age class, gender and job category variables were analyzed. Statistically significant differences among groups were tested through χ2 test. Univariate and multivariate analyses (p < 0,005) were performed to assess differences towards vaccination attitude. The flu vaccination coverage rate was 24.6%. Among the selected job categories, 29.8% of physicians, 19.9% of nurses and 19.7% of other HCWs were vaccinated with a statistically significant decrease (p < 0.001) across all categories respect with the last campaign. The findings of the logistic regression depicted that the 40-59 years old age class, compared with the youngest age class (OR 1.30, 95% CI 1.12-1.43) as well as being physician (OR 2.79, 95% CI 1.87-3.41) with the respect to being nurses, had a higher adherence to vaccination. Interestingly, being male, is associated with a statistically significant reduction (OR 0.71, 95% CI 0.59-0.87) in vaccination uptake. Study findings showed a several decline in the flu vaccination coverage comparing with previous campaigns, probably due to the concomitant administration of the booster dose against SARS-CoV-2. This alarm should not be underestimated and requires timely and innovative organizational approaches (i.e., combined vaccine). Further studies are needed to analyze the reasons for this poor adhesion and the strategies to be adopted to increase the awareness of the HCWs. Key messages • Reaching high coverage rates and restore a positive trend for the future campaign for flu vaccination it is essential strategy to protect HCWs themselves, their patients and the hospital community. • Decision-makers should implement consistent communication strategies to lessen vaccine hesitancy among HCWs.

Burden of COVID-19: Disability-Adjusted Life Years (DALYs) across 16 European countries.

OBJECTIVE: The aim of this study is to measure and compare the burden of disease of COVID-19 pandemic in 16 EU/EEA countries through the estimation of Disability-Adjusted Life Years (DALYs) over a long period of time. MATERIALS AND METHODS: The observational study was based on data from ECDC and WHO databases collected from 27 January 2020 to 15 November 2020. In addition to the absolute number of DALYs, a weekly trend of DALYs/100,000 inhabitants was computed for each country to assess the evolution of the pandemic burden over time. A cluster analysis and Kolmogorov-Smirnov (KS) test were performed to allow for a country-to-country comparison. RESULTS: The total DALYs amount to 4,354 per 100.000 inhabitants. YLLs were accountable for 98% of total DALYs.  Italy, Czechia and Sweden had the highest values of DALYs/100,000 while Finland, Estonia and Slovakia had the lowest. The latter three countries differed significantly from the others - in terms of DALYs trend over time - as shown by KS test. The cluster analysis allowed for the identification of three clusters of countries sharing similar trends of DALYs during the assessed period of time. These results show that notable differences were observed among different countries, with most of the disease burden attributable to YLLs. CONCLUSIONS: DALYs have proven to be an effective measure of the burden of disease. Public health and policy actions, as well as demographic, epidemiological and cultural features of each country, may be responsible for the wide variations in the health impact that were observed among the countries analyzed.

[Fragility in a Public Health perspective: principles and tools for a "life course" approach prevention- oriented]

The Covid-19 pandemic significantly increased the workload for the Italian Health Service. There is few information in the literature on the pediatric population and on the management of pediatric hospitals. The aim of this article is to describe the management of healthcare services during Covid-19 emergency in Regina Margherita Children's Hospital. The Regina Margherita Children's Hospital is specialized in the prevention, diagnosis and treatment of pediatric diseases. About 1000 health worker work in this Hospital and 278 hospitalization places are available.

Gemelli decision tree Algorithm to Predict the need for home monitoring or hospitalization of confirmed and unconfirmed COVID-19 patients (GAP-Covid19): preliminary results from a retrospective cohort study.

OBJECTIVE: To develop a deep learning-based decision tree for the primary care setting, to stratify adult patients with confirmed and unconfirmed coronavirus disease 2019 (COVID-19), and to predict the need for hospitalization or home monitoring. PATIENTS AND METHODS: We performed a retrospective cohort study on data from patients admitted to a COVID hospital in Rome, Italy, between 5 March 2020 and 5 June 2020. A confirmed case was defined as a patient with a positive nasopharyngeal RT-PCR test result, while an unconfirmed case had negative results on repeated swabs. Patients' medical history and clinical, laboratory and radiological findings were collected, and the dataset was used to train a predictive model for COVID-19 severity. RESULTS: Data of 198 patients were included in the study. Twenty-eight (14.14%) had mild disease, 62 (31.31%) had moderate disease, 64 (32.32%) had severe disease, and 44 (22.22%) had critical disease. The G2 value assessed the contribution of each collected value to decision tree building. On this basis, SpO2 (%) with a cut point at 92 was chosen for the optimal first split. Therefore, the decision tree was built using values maximizing G2 and LogWorth. After the tree was built, the correspondence between inputs and outcomes was validated. CONCLUSIONS: We developed a machine learning-based tool that is easy to understand and apply. It provides good discrimination in stratifying confirmed and unconfirmed COVID-19 patients with different prognoses in every context. Our tool might allow general practitioners visiting patients at home to decide whether the patient needs to be hospitalized.