Pancreatic cancer (PC) represents the 6th cause of cancer death. Although the aetiology of PC is not completely understood, numerous risk factors have been identified in association with this cancer, among them diet. However, little is known about the association between the Mediterranean Diet (MedDiet) and the risk of PC. For this reason, we conducted a systematic review with meta-analysis according to the PRISMA guidelines, searching on three databases (PubMed/MEDLINE, Scopus, and EMBASE). The protocol was registered in PROSPERO. Both fixed and random effect models were performed. The Effect size was reported as a hazard ratio (HR) with a 95% Confidence Interval (CI). A total of eight articles were included. The methodological quality of the included meta-analyses was high. Our results show that a higher adherence to the MedDiet is associated with a lower risk of PC [HR:0.82 (0.76–0.88) p < 0.001, based on 1,301,320 subjects]. The results were also confirmed in sensitivity and subgroups analyses (avoidance of potential overlapping effects, type of tools used to assess dietary intake and the diagnosis of PC, prevalence and incidence of PC risk, country where the studies took place, sex, and cancer site). Promoting a higher adherence to the MedDiet could be an effective approach to reduce the risk of PC.

Background: During the COVID-19 pandemic, more than ever, optimal influenza vaccination coverage among healthcare workers (HCWs) is crucial to avoid absenteeism and disruption of health services, as well as in-hospital influenza outbreaks. The aim of this study is to analyze the 2020 influenza vaccination campaign, comparing it with the previous year’s in a research and teaching hospital in Northern Italy. Methods: adopting an approach based on combined strategies, three interventions were deployed: a promotional and educational campaign, vaccination delivery through both ad hoc and on-site ambulatories, and a gaming strategy. Personal data and professional categories were collected and analyzed using univariate logistic regression. Vaccinated HCWs were asked to fill in a questionnaire to describe their reasons for vaccination adherence. Results: the vaccination coverage rate (VCR) was 43.1%, compared to 21.5% in 2019. The highest increase was registered among administrative staff (308.3%), while physicians represent the most vaccinated category (n = 600). Moreover, residents (prevalence ratio (PR): 1.12; 95% CI 1.04–1.20), as well as intensive care (PR: 1.44; 95% CI: 1.24–1.69) and newborn workers (PR: 1.41; 95% CI: 1.20–1.65) were, respectively, the categories most frequently vaccinated for the first time. Conclusion: the significant increase in vaccination coverage rate confirms the suitability of the combined strategy of delivering the flu vaccination campaign and represents a first step towards reaching WHO recommended vaccination rates.

The first case of Coronavirus Disease 2019 in Italy was detected on February the 20^thin Lombardy region. Since that date, Lombardy has been the most affected Italian region by the epidemic, and its healthcare system underwent a severe overload during the outbreak. From a public health point of view, therefore, it is fundamental to provide healthcare services with tools that can reveal possible new health system stress periods with a certain time anticipation, which is the main aim of the present study. Moreover, the sequence of law decrees to face the epidemic and the large amount of news generated in the population feelings of anxiety and suspicion. Considering this whole complex context, it is easily understandable how people “overcrowded” social media with messages dealing with the pandemic, and emergency numbers were overwhelmed by the calls. Thus, in order to find potential predictors of possible new health system overloads, we analysed data both from Twitter and emergency services comparing them to the daily infected time series at a regional level. Particularly, we performed a wavelet analysis in the time-frequency plane, to finely discriminate over time the anticipation capability of the considered potential predictors. In addition, a cross-correlation analysis has been performed to find a synthetic indicator of the time delay between the predictor and the infected time series. Our results show that Twitter data are more related to social and political dynamics, while the emergency calls trends can be further evaluated as a powerful tool to potentially forecast new stress periods. Since we analysed aggregated regional data, and taking into account also the huge geographical heterogeneity of the epidemic spread, a future perspective would be to conduct the same analysis on a more local basis.

Background: Despite recommendations, the influenza vaccination coverage rate in healthcare workers (HCWs) in Italy is far from the recommended target. The aim of the study is to analyze the influenza vaccination campaign performed in 2019 in a research and teaching hospital in Milan. Methods: The vaccination strategy included an ad hoc ambulatory, as in the previous years, and an onsite ambulatory, introduced for the first time. Personal data and professional categories were collected and analyzed using univariate logistic regression. HCWs who refused the vaccination were asked to fill in a questionnaire to explain their reasons for dissent. Results: The achieved vaccination coverage rate (VCR) for HCWs was 21.5 %, compared to 17.1% in 2018. The lowest VCR was registered among nurses (11.9%), while physicians had the highest VCR (40.7%). Prevalence ratios show that some professional categories were more frequently vaccinated for the first time than attending physicians (reference category); those with statistically significant confidence intervals were nurses (PR: 2.42; 95% CI: 1.78–3.28), residents (PR: 1.85; 95% CI: 1.36–2.53), and auxiliary staff (PR: 2.33; 95% CI: 1.45–3.74). Conclusions: An onsite vaccination strategy failed in providing a remarkable increase in VCR in 2019, but it is important to point out that the campaign was influenced by several logistic problems.

On 18th February the first Italian case of Coronavirus Induced Disease 2019 (COVID19) due to secondary transmission outside China was identified in Codogno, Lombardia region. In the following days the number of cases started to rise not only in Lombardia but also in other Italian regions, although Lombardia remained and it is still the most affected region in Italy. At the moment, 234801 cases have been identified in Italy, out of which 90070 in Lombardia region. The (Severe Acute Respiratory Syndrome Coronavirus 2) SARS CoV 2 outbreak in Italy has been characterized by a massive spread of news coming from both official and unofficial sources leading what has been defined as infodemia, an over-abundance of information - some accurate and some not - that has made hard for people to find trustworthy sources and reliable guidance needed. Infodemia on SARS CoV 2 created the perfect field to build uncertainty in the population, which was scared and not prepared to face this outbreak. It is understandable how the rapid increase of the cases' number , the massive spread of news and the adoption of laws to face this outbreak led to a feeling of anxiety in the population whose everyday life changed very quickly. A way to assess the dynamic burden of social anxiety is a context analysis of major social networks activities over the Internet. To this aim Twitter represents a possible ideal tool since the focused role of the tweets according to the more urgent needs of information and communication rather than general aspects of social projection and debate as in the case of Facebook, which could provide slower responses for the fast individual and social context evolution dynamics. Aim of the paper is to analyse the most common reasons for calling and outcomes. Furthermore, the joint analysis with Twitter trends related to emergency services might be useful to understand possible correlations with epidemic trends and predict new outbreaks.

Background. Limited evidence exists on the balance between the benefits and harms of the COVID-19 vaccines. The aim of this study is to compare the benefits and safety of mRNA-based (Pfizer-BioNTech and Moderna) and adenovirus-vectored (Oxford-AstraZeneca) vaccines in subpopulations defined by age and sex. Methods. All citizens who are newly vaccinated from 27 December 2020 to 3 May 2021 are matched to unvaccinated controls according to age, sex, and vaccination date. Study outcomes include the events that are expected to be avoided by vaccination (i.e., hospitalization and death from COVID-19) and those that might be increased after vaccine inoculation (i.e., venous thromboembolism). The incidence rate ratios (IRR) of vaccinated and unvaccinated citizens are separately estimated within strata of sex, age category and vaccine type. When suitable, number needed to treat (NNT) and number needed to harm (NNH) are calculated to evaluate the balance between the benefits and harm of vaccines within each sex and age category. Results. In total, 2,351,883 citizens are included because they received at least one dose of vaccine (755,557 Oxford-AstraZeneca and 1,596,326 Pfizer/Moderna). A reduced incidence of COVID-19-related outcomes is observed with a lowered incidence rate ranging from 55% to 89% and NNT values ranging from 296 to 3977. Evidence of an augmented incidence of harm-related outcomes is observed only for women aged <50 years within 28 days after Oxford-AstraZeneca (being the corresponding adjusted IRR of 2.4, 95% CI 1.1–5.6, and NNH value of 23,207, 95% CI 10,274–89,707). Conclusions. A favourable balance between benefits and harms is observed in the current study, even among younger women who received Oxford-AstraZeneca.