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Influenza is an acute viral respiratory infection that causes significant morbidity and mortality worldwide, with indirect costs considerably exceeding direct costs on patients and society. Flu vaccination is one of the major strategies for preventing it, especially among healthcare workers (HCWs), to avoid onward transmission to patients and colleagues. The aim of this research is to analyze the flu vaccination coverage rate among HCWs and to study which factors conditioned their adherence. The observational study was conducted in an Italian teaching hospital located in Rome (Italy) and data were collected from October to December 2020. A total of 6,568 HCWs was included considering physicians, nurses and other HCWs as job categories. Age class, gender and job category variables were analyzed. Statistically significant differences among groups were tested through 2 test. Univariate analyses and a multivariate logistic regression (p < 0.05) were performed to assess differences in flu vaccination adherence. The flu vaccination coverage rate was 60.94%. Considering the job categories, physicians reached the highest coverage (76.09%) followed by others HCWs (64.25%) and nurses (49.09%). According to the logistic regression, belonging to 20-40 age class was significantly associated with higher flu vaccination adherence, as well as being nurses (OR 0.26, 95% CI 0.22-0.31) or other HCWs (OR 0.48, 95% CI 0.40-0.58). Findings showed an encouraging vaccination coverage rate among HCWs. Deeper knowledge of the value of vaccination among younger HCWs could be probably a reason for higher adherence as well as the wrong perception of being at low risk of illness or transmitting infections may explains job categories differences. Especially when considering the disrupting impact of the SARS-CoV-2 pandemic, an improved attitude towards flu vaccination would reduce stress on hospitals and preserve scarce medical resources. Key messages: Reaching high coverage rates for flu vaccination could save healthcare resources. It is crucial to preserve this positive trend for the future years by spreading the message to all the involved stakeholders. [ABSTRACT FROM AUTHOR]

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.

In the article the authors illustrate: a) a community detection program designed to identify subjects with high blood pressure for treatment; b) the degree of participation; c) the cost of realization; d) the prevalence of arterial hypertension. A total of 4.149 subjects, or 65% of the examined population over age 8, were screened. The opportunity to have their blood pressure checked were offered with a letter of invitation to a centralized ambulatory. The cost of realization was L. 360 for every screened subject. The prevalence of hypertension was 20.4%.

398 women at various stages of pregnancy and living in two areas subject to different levels of atmospheric pollution were examined. Blood samples were taken from about 200 women and their funiculi at the moment of parturition. The results showed a difference in lead concentrations in the blood and in the ratio of mother and funicular lead concentrations according to the place of residence. Several hypothesis to explain these results are formulated.