Healthcare systems around the world are facing incredible challenges due to the ageing population and the related disability, and the increasing use of technologies and citizen’s expectations. Improving health outcomes while containing costs acts as a stumbling block. In this context, Big Data can help healthcare providers meet these goals in unprecedented ways. The potential of Big Data in healthcare relies on the ability to detect patterns and to turn high volumes of data into actionable knowledge for precision medicine and decision makers. In several contexts, the use of Big Data in healthcare is already offering solutions for the improvement of patient care and the generation of value in healthcare organizations. This approach requires, however, that all the relevant stakeholders collaborate and adapt the design and performance of their systems. They must build the technological infrastructure to house and converge the massive volume of healthcare data, and to invest in the human capital to guide citizens into this new frontier of human health and well-being. The present work reports an overview of best practice initiatives in Europe related to Big Data analytics in public health and oncology sectors, aimed to generate new knowledge, improve clinical care and streamline public health surveillance.

To map the current status of head-to-head comparative randomized evidence and to assess whether funding may impact on trial design and results. From a 50% random sample of the randomized controlled trials (RCTs) published in journals indexed in PubMed during 2011, we selected the trials with ≥ 100 participants, evaluating the efficacy and safety of drugs, biologics, and medical devices through a head-to-head comparison. We analyzed 319 trials. Overall, 238,386 of the 289,718 randomized subjects (82.3%) were included in the 182 trials funded by companies. Of the 182 industry-sponsored trials, only 23 had two industry sponsors and only three involved truly antagonistic comparisons. Industry-sponsored trials were larger, more commonly registered, used more frequently noninferiority/equivalence designs, had higher citation impact, and were more likely to have "favorable" results (superiority or noninferiority/equivalence for the experimental treatment) than nonindustry-sponsored trials. Industry funding [odds ratio (OR) 2.8; 95% confidence interval (CI): 1.6, 4.7] and noninferiority/equivalence designs (OR 3.2; 95% CI: 1.5, 6.6), but not sample size, were strongly associated with "favorable" findings. Fifty-five of the 57 (96.5%) industry-funded noninferiority/equivalence trials got desirable "favorable" results. The literature of head-to-head RCTs is dominated by the industry. Industry-sponsored comparative assessments systematically yield favorable results for the sponsors, even more so when noninferiority designs are involved.

The expansion of primary care and community-based service delivery systems is intended to meet emerging needs, reduce the costs of hospital-based ambulatory care and prevent avoidable hospital use by the provision of more appropriate care. Great emphasis has been placed on the role of self-management in the complex process of care of patient with long-term conditions. Several studies have determined that nurses, among the health professionals, are more recommended to promote health and deliver preventive programs within the primary care context. The aim of this systematic review and meta-analysis is to assess the efficacy of the nurse-led self-management support versus usual care evaluating patient outcomes in chronic care community programs. Systematic review was carried out in MEDLINE, CINAHL, Scopus and Web of Science including RCTs of nurse-led self-management support interventions performed to improve observer reported outcomes (OROs) and patients reported outcomes (PROs), with any method of communication exchange or education in a community setting on patients >18 years of age with a diagnosis of chronic diseases or multi-morbidity. Of the 7,279 papers initially retrieved, 29 met the inclusion criteria. Meta-analyses on systolic (SBP) and diastolic (DBP) blood pressure reduction (10 studies—3,881 patients) and HbA1c reduction (7 studies—2,669 patients) were carried-out. The pooled MD were: SBP -3.04 (95% CI -5.01—-1.06), DBP -1.42 (95% CI -1.42—-0.49) and HbA1c -0.15 (95% CI -0.32–0.01) in favor of the experimental groups. Meta-analyses of subgroups showed, among others, a statistically significant effect if the interventions were delivered to patients with diabetes (SBP) or CVD (DBP), if the nurses were specifically trained, if the studies had a sample size higher than 200 patients and if the allocation concealment was not clearly defined. Effects on other OROs and PROs as well as quality of life remain inconclusive.

Background: During the intensive care units’ (ICUs) reorganization that was forced by the COVID-19 emergency, attention to traditional infection control measures may have been reduced. Nevertheless, evidence on the effect of the COVID-19 pandemic on healthcare-associated infections (HAIs) is still limited and mixed. In this study, we estimated the pandemic impact on HAI incidence and investigated the HAI type occurring in COVID-19 patients. Methods: Patients admitted to the main ICU of the Umberto I teaching hospital of Rome from March 1st and April 4th 2020 were compared with patients hospitalized in 2019. We assessed the association of risk factors and time-to-first event through multivariable Fine and Grey’s regression models, that consider the competitive risk of death on the development of HAI (Model 1) or device related-HAI (dr-HAI, Model 2) and provide estimates of the sub-distribution hazard ratio (SHR) and its associated confidence interval (CI). A subgroup analysis was performed on the 2020 cohort. Results: Data from 104 patients were retrieved. Overall, 59 HAIs were recorded, 32 of which occurred in the COVID-19 group. Patients admitted in 2020 were found to be positively associated with both HAI and dr-HAI onset (SHR: 2.66, 95% CI 1.31–5.38, and SHR: 10.0, 95% CI 1.84–54.41, respectively). Despite being not confirmed at the multivariable analysis, a greater proportion of dr-HAIs seemed to occur in COVID-19 patients, especially ventilator-associated pneumonia, and catheter-related urinary tract infections. Conclusions: We observed an increase in the incidence of patients with HAIs, especially dr-HAIs, mainly sustained by COVID-19 patients. A greater susceptibility of these patients to device-related infections was hypothesized, but further studies are needed.

The intestinal microbiota is a crucial regulator of human health and disease because of its interactions with the immune system. Tobacco smoke also influences the human ecosystem with implications for disease development. This systematic review aims to analyze the available evidence, until June 2021, on the relationship between traditional and/or electronic cigarette smoking and intestinal microbiota in healthy human adults. Of the 2645 articles published in PubMed, Scopus, and Web of Science, 13 were included in the review. Despite differences in design, quality, and participants’ characteristics, most of the studies reported a reduction in bacterial species diversity, and decreased variability indices in smokers’ fecal samples. At the phylum or genus level, the results are very mixed on bacterial abundance both in smokers and non-smokers with two exceptions. Prevotella spp. appears significantly increased in smokers and former smokers but not in electronic cigarette users, while Proteobacteria showed a progressive increase in Desulfovibrio with the number of pack-years of cigarette (p = 0.001) and an increase in Alphaproteobacteria (p = 0.04) in current versus never smokers. This attempt to systematically characterize the effects of tobacco smoking on the composition of gut microbiota gives new perspectives on future research in smoking cessation and on a new possible use of probiotics to contrast smoke-related dysbiosis.