Search

In observational studies evaluating the treatment effect on a given out- come, the treated and untreated subjects may be highly unbalanced in their observed covariates, and these differences can lead to biased estimates of treatment effects. Propensity score is popular tool to reduce this bias. In this work we propose to esti- mate the propensity score by using Bayesian Networks as alternative to conventional logistic regression. Based on it, we develop an inferential methodology to evaluate the treatment effect. In simulation study, our proposed approach resulted in the best performance.

This chapter examines mobility from the point of view of territories, and aims to classify university graduates according to their migration behaviour when entering higher education and after college completion. It provides descriptive evidence on the composition of these groups, in order to evaluate the human capital loss associated to student and graduate mobility investing the southern macro area. The structural determinants of internal student mobility in Italy have been addressed by a few studies examining aggregated migration flows of students across Italian provinces. Internal student mobility involves a transfer of economic resources from south to north. Italy has long been characterized by out-migration waves. The chapter investigates which individual characteristics play a role in shaping the size and direction of student and graduate mobility flows. Due to data availability, it focuses on young individuals who enrol in university within 3 years from the end of high school.

The current study, conducted on 9- and 10-year-old children, aimed at evaluating the effects of two trainings - one mentalistic and one non-mentalistic - on theory of mind, cooperation and prosociality. The two trainings were compared, randomizing the sample in two groups composed by 105 children each. Theory- of-mind abilities increased more in the group assigned to the theory-of-mind condition, levels of cooperation and prosocial behaviour increased in both groups and theory of mind emerged as a significant predictor of cooperation but not of prosocial behaviour.

Targeted gene editing in hematopoietic stem cells (HSCs) is a promising treatment for several diseases. However, the limited efficiency of homology-directed repair (HDR) in HSCs and the unknown impact of the procedure on clonal composition and dynamics of transplantation have hampered clinical translation. Here, we apply a barcoding strategy to clonal tracking of edited cells (BAR-Seq) and show that editing activates p53, which substantially shrinks the HSC clonal repertoire in hematochimeric mice, although engrafted edited clones preserve multilineage and self-renewing capacity. Transient p53 inhibition restored polyclonal graft composition. We increased HDR efficiency by forcing cell-cycle progression and upregulating components of the HDR machinery through transient expression of the adenovirus 5 E4orf6/7 protein, which recruits the cell-cycle controller E2F on its target genes. Combined E4orf6/7 expression and p53 inhibition resulted in HDR editing efficiencies of up to 50% in the long-term human graft, without perturbing repopulation and self-renewal of edited HSCs. This enhanced protocol should broaden applicability of HSC gene editing and pave its way to clinical translation. Transient p53 inhibition and induced cell-cycle progression increase clonal engraftment and homology-directed repair in hematopoietic stem cells.

The “Healthcare workers’ wellbeing [Benessere Operatori]” project is an exploratory longitudinal study assessing healthcare workers’ mental health at three different time points over a 14-month period during the COVID-19 pandemic. We collected socio-demographic and work-related information and assessed the perceived social support, coping strategies, and levels of depression, anxiety, insomnia, anger, burnout, and PTSD symptoms. In total, 325 Italian healthcare workers (i.e. physicians, nurses, other healthcare workers, and clerks) participated in the first initial survey and either the second or third subsequent survey. Participants reported subclinical levels of psychiatric symptoms that remained mostly unchanged across time, except for an increase in stress, depression, state anger, and emotional exhaustion symptoms. Despite subclinical levels, healthcare workers’ distress can adversely affect the quality of care, patient satisfaction, and medical error rates. Therefore, implementing interventions to improve healthcare workers’ wellbeing is required.

This contribution explores in a new statistical perspective the antibody responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 141 coronavirus disease 2019 (COVID-19) patients exhibiting a broad range of clinical manifestations. This cohort accurately reflects the characteristics of the first wave of the SARS-CoV-2 pandemic in Italy. We determined the IgM, IgA, and IgG levels towards SARS-CoV-2 S1, S2, and NP antigens, evaluating their neutralizing activity and relationship with clinical signatures. Moreover, we longitudinally followed 72 patients up to 9 months postsymptoms onset to study the persistence of the levels of antibodies. Our results showed that the majority of COVID-19 patients developed an early virus-specific antibody response. The magnitude and the neutralizing properties of the response were heterogeneous regardless of the severity of the disease. Antibody levels dropped over time, even though spike reactive IgG and IgA were still detectable up to 9 months. Early baseline antibody levels were key drivers of the subsequent antibody production and the long-lasting protection against SARS-CoV-2. Importantly, we identified anti-S1 IgA as a good surrogate marker to predict the clinical course of COVID-19. Characterizing the antibody response after SARS-CoV-2 infection is relevant for the early clinical management of patients as soon as they are diagnosed and for implementing the current vaccination strategies.

The possibility to rely on rapid and accurate diagnostic techniques has proved itself crucial during the last year to contain the spreading of SARS-CoV-2 infection [1]. Even if quantitative RT-PCR (RT-qPCR) on NasoPharyngeal Swabs (NPS) is still considered the standard for COVID19 diagnosis, saliva has been evaluated in several studies as a possible alternative to NPS and is currently extensively utilised in South Korea, Germany, and Japan [2, 3]. Nonetheless, the use of saliva is still debated, and a rigorous standardisation of the analysis protocol is greatly needed [4–6]. The application of point of care (POC) technologies on saliva, able to rapidly perform highly specific and sensitive molecular testing, could prove invaluable to allow the diagnosis also in challenging and remote settings by simplifying and speeding up the diagnostic process [1]. Acknowledgements:We thank DiaSorin Italia and Cepheid Italia for providing the reagents to perform the study free of charge.

Significance The global outbreak of COVID-19 infections generated an unprecedented need to develop novel therapeutic strategies. The SARS-CoV-2 virus enters host cells after binding to the angiotensin-converting enzyme 2 (ACE2), but whether renin−angiotensin−aldosterone system inhibitors (RAASi) are beneficial remains controversial. Standard statistical approaches may fail in assessing medications effects, due to multiple sources of bias in COVID-19 case series collected on an emergency basis. We present a data-driven approach to tackle these challenges. Multilayer risk stratifications were derived for assessing drugs effect, while Bayesian networks were estimated, to analyze dependencies among risk factors’ and treatments’ impact on survival. We provide strong evidence for protectivity of RAASi on hospitalized patients that call for randomized controlled trials of RAASi as COVID-19 treatment option., As the COVID-19 pandemic is spreading around the world, increasing evidence highlights the role of cardiometabolic risk factors in determining the susceptibility to the disease. The fragmented data collected during the initial emergency limited the possibility of investigating the effect of highly correlated covariates and of modeling the interplay between risk factors and medication. The present study is based on comprehensive monitoring of 576 COVID-19 patients. Different statistical approaches were applied to gain a comprehensive insight in terms of both the identification of risk factors and the analysis of dependency structure among clinical and demographic characteristics. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus enters host cells by binding to the angiotensin-converting enzyme 2 (ACE2), but whether or not renin−angiotensin−aldosterone system inhibitors (RAASi) would be beneficial to COVID-19 cases remains controversial. The survival tree approach was applied to define a multilayer risk stratification and better profile patient survival with respect to drug regimens, showing a significant protective effect of RAASi with a reduced risk of in-hospital death. Bayesian networks were estimated, to uncover complex interrelationships and confounding effects. The results confirmed the role of RAASi in reducing the risk of death in COVID-19 patients. De novo treatment with RAASi in patients hospitalized with COVID-19 should be prospectively investigated in a randomized controlled trial to ascertain the extent of risk reduction for in-hospital death in COVID-19.

In this paper, we focus on a Bayesian network s approach to combine traditional survey and social network data and official statistics to evaluate well-being. Bayesian networks permit the use of data with different geographical levels (provincial and regional) and time frequencies (daily, quarterly, and annual). The aim of this study was twofold: to describe the relationship between survey and social network data and to investigate the link between social network data and official statistics. Particularly, we focused on whether the big data anticipate the information provided by the official statistics. The applications, referring to Italy from 2012 to 2017, were performed using ISTAT’s survey data, some variables related to the considered time period or geographical levels, a composite index of well-being obtained by Twitter data, and official statistics that summarize the labor market.

Background: COVID-19 forced healthcare workers to work in unprecedented and critical circumstances, exacerbating already-problematic and stressful working conditions. The “Healthcare workers’ wellbeing (Benessere Operatori)” project aimed at identifying psychological and personal factors, influencing individuals’ responses to the COVID-19 pandemic. Methods: 291 healthcare workers took part in the project by answering an online questionnaire twice (after the first wave of COVID-19 and during the second wave) and completing questions on socio-demographic and work-related information, the Depression Anxiety Stress Scale-21, the Insomnia Severity Index, the Impact of Event Scale-Revised, the State-Trait Anger Expression Inventory-2, the Maslach Burnout Inventory, the Multidimensional Scale of Perceived Social Support, and the Brief Cope. Results: Higher levels of worry, worse working conditions, a previous history of psychiatric illness, being a nurse, older age, and avoidant and emotion-focused coping strategies seem to be risk factors for healthcare workers’ mental health. High levels of perceived social support, the attendance of emergency training, and problem-focused coping strategies play a protective role. Conclusions: An innovative, and more flexible, data mining statistical approach (i.e., a regression trees approach for repeated measures data) allowed us to identify risk factors and derive classification rules that could be helpful to implement targeted interventions for healthcare workers.

Aim To radiographically evaluate the effect of immediate implant placement plus alveolar ridge preservation (ARP) with a deproteneized bovine bone mineral and a collagen matrix (IMPL/DBBM/CM) as compared to ARP (DBBM/CM) or spontaneous healing (SH) on vertical and horizontal bone dimensional changes after 4 months of healing. Materials and methods Thirty patients requiring extraction of one single-rooted tooth or premolar were randomly assigned to IMPL/DBBM/CM, ARP DBBM/CM or SH. Cone-beam computed tomography (CBCT) scans, performed before tooth extraction and after 4 months, were superimposed in order to assess changes in ridge height at the buccal and lingual aspect and in ridge width at 1 mm, 3 mm and 5 mm apical to the bone crest. Kruskal-Wallis test was applied for comparison of differences between groups. Results No statistically significant differences between the groups were observed for the vertical bone resorption of the buccal and the lingual side, while significant differences were found between SH group (-3.37 +/- 1.55 mm; -43.2 +/- 25.1%) and both DBBM/CM (-1.56 +/- 0.76 mm; -19.2 +/- 9.1%) and IMPL/DBBM/CM (-1.29 +/- 0.38 mm; -14.9 +/- 4.9%) groups in the horizontal dimension at the most coronal aspect. Conclusion Ridge preservation techniques using DBBM and CM reduce the horizontal bone morphological changes that occur, mostly in the coronal portion of the buccal bone plate following tooth extraction, when compared to spontaneous healing. This is true regardless of whether immediate implant placement is performed or not.

Correctly recognizing emotions is an essential skill to manage interpersonal relationships in everyday life. Facial expression represents the most powerful mean to convey important information on emotional and cognitive states during interactions with others. In this paper, we analyze physiological responses triggered by an emotion recognition test, which requires the processing of facial cues. In particular, we evaluate the modulation of several Heart Rate Variability indices, collected during the Reading the Mind in the Eyes Test, accounting for test difficulty (derived from a Rasch analysis), test performances, demographic and psychological characteristics of the participants. The main idea is that emotion recognition is associated with the Autonomic Nervous System and, as a consequence, with the Heart Rate Variability. The principal goal of our study was to explore the complexity of the collected measures and their possible interactions by applying a class of flexible models, i.e., the latent class mixed models. Actually, this modelling strategy allows for the identification of clusters of subjects characterized by similar longitudinal trajectories. Both univariate and multivariate latent class mixed models were used. In fact, while the interpretation of the Heart Rate Variability indices is very difficult when considered individually, a joint evaluation provides a better description of the Autonomic Nervous System state.

A broad literature focused on the effectiveness of tertiary education. In classical models, a performance indicator is regressed on a set of characteristics of the individuals and fixed effects at the institution level. The FE coefficients are interpreted as the pure value added of the universities. The innovative contribution of the present paper resides in the use of Bayesian network (BN) analysis to assess the effectiveness of tertiary education. The results of an empirical study focused on Italian universities are discussed, to present the use of BN as a decision support tool for policy-making purposes.

To test the factorial structure of the Italian version of the Insomnia Severity Index (ISI) using a confirmatory approach and to assess its psychometric properties. ISI questionnaire was completed by 272 patients (average age 41.28, range 18-73) with insomnia diagnosis performed by a sleep medicine physician and retrospectively enrolled in the study. All patients underwent Cognitive Behavioral Treatment for Insomnia (CBT-I) and completed sleep diaries before starting the treatment. Data from sleep diaries were analyzed for assessing concurrent validity of the ISI. Confirmatory factor analysis (CFA) for ordinal Likert-type items was applied to compare four competing models proposed in the literature. 244 patients, out of the 272, completed the ISI at the end of CBT-I. A comparison of ISI score before and after treatment was performed. The CFA analysis confirmed the presence of three main factors conceptualized as severity and impact of the disease along with sleep satisfaction. Significant correlations of the first three items of the questionnaire, investigating three different subtypes of insomnia, and the subjective measures from the sleep diaries were found, thus supporting the concurrent validity of the test. Sleep efficiency (SE) had a significant inverse correlation with the severity and satisfaction factors and with ISI's total score. After CBT-I treatment, a significant reduction of ISI's scores was observed, thus confirming the effectiveness of the CBT-I treatment. The internal reliability coefficient was 0.75. The ISI questionnaire maintains good psychometric properties in the Italian version, thus confirming that this instrument is reliable for detecting insomnia severity and identifying patients' symptoms.

Pulmonary infections caused by Mycobacterium abscessus (MA) have increased over recent decades affecting individuals with underlying pathologies such as chronic obstructive pulmonary disease, bronchiectasis and, especially, cystic fibrosis. The lack of a representative and standardized model of chronic infection in mice has limited steps forward in the field of MA pulmonary infection. To overcome this challenge we refined the method of agar beads to establish MA chronic infection in immunocompetent mice. We evaluated bacterial count, lung pathology and markers of inflammation and we performed longitudinal studies with magnetic resonance imaging (MRI) up to three months after MA infection. In this model, MA was able to establish a persistent lung infection up to two months and with minimal systemic spread. Lung histopathological analysis revealed granulomatous inflammation around bronchi characterized by the presence of neutrophils, lymphocytes and aggregates of vacuolated foamy cells, mimicking the damage observed in humans. Furthermore, MA lung lesions were successfully monitored for the first time by MRI. The availability of this murine model and the introduction of the successfully longitudinal monitoring of the murine lung lesions with MRI pave the way for further investigations on the impact of MA pathogenesis and the efficacy of novel treatments.

SETTING: Screening for latent tuberculous infection (LTBI) of groups at high risk of active tuberculosis (TB) is a key component of the End TB Strategy.OBJECTIVE: To conduct a retrospective descriptive analysis of LTBI rates among foreign-born individuals applying to shelters in the metropolitan area of Milan, Italy.DESIGN: All foreign-born individuals registering for accommodation centres in the city of Milan from November 2009 to April 2017 were screened for active TB and LTBI. Individuals aged 10 mm were offered confirmatory testing with QuantiFERON (R)-TB Gold In-Tube (QFT-GIT).RESULTS: Of the 2666 TST-positive migrants aged 250/100 000 person-years.CONCLUSION: Our data showed a high proportion of LTBI among individuals coming from intermediate TB burden countries.

In this paper, we provide a comprehensive picture of social selectivity in higher education in Italy by focusing on enrolment and two alternative indicators of success: retention and timely completion. The existing literature has shown that young individuals of disadvantaged backgrounds have lower enrolment probabilities and higher chances of withdrawal. This paper analyses the size and features of this cumulative disadvantage, shaping the chances of high-school leavers of eventually attaining the university degree. We analyse whether social background inequalities are stronger at enrolment or at later outcomes, and how these inequalities vary with previous schooling experience. We confirm previous evidence that disadvantaged groups with respect to enrolment are also disadvantaged on persistence, add new findings on timely completion and show that cumulative inequalities are very strong. Inequalities are strongest among students holding technical and vocational high-school qualifications and weakest among well-performing students from lyceums. In addition, we examine the role of labour market conditions and whether they explain geographical differences. Enrolment, retention and timely completion probabilities appear negatively related to local youth unemployment rates, suggesting that with poor labour market prospects, individuals lose motivation and reduce their engagement in education.

Background: Breast Cancer susceptibility genes 1 and 2 are implicated in hereditary breast and ovarian cancer and women can test for the presence of these genes prior to developing cancer. The goal of this study is to examine psychological distress, quality of life, and active coping mechanisms in a sample of women during the pre-test stage of the genetic counseling process, considering that pre-test distress can be an indicator of post-test distress. We also wanted to identify if subgroups of women, defined based on their health status, were more vulnerable to developing distress during the genetic counseling process. Methods: This study included 181 female participants who accessed a Cancer Genetic Counseling Clinic. The participants were subdivided into three groups on the basis of the presence of a cancer diagnosis: Affected patients, Ex-patients, and Unaffected participants. Following a self-report questionnaire, a battery of tests was administered to examine psychological symptomatology, quality of life, and coping mechanisms. Results: The results confirm that the genetic counseling procedure is not a source of psychological distress. Certain participants were identified as being more vulnerable than others; in the pre-test phase, they reported on average higher levels of distress and lower quality of life. These participants were predominantly Ex-patients and Affected patients, who may be at risk of distress during the counseling process. Conclusions: These findings highlight that individuals who take part in the genetic counseling process are not all the same regarding pre-test psychological distress. Attention should be paid particularly to Ex-patients and Affected patients by the multidisciplinary treating team.

In this article, we compare alternative missing imputation methods in the presence of ordinal data, in the framework of CUB (Combination of Uniform and (shifted) Binomial random variable) models. Various imputation methods are considered, as are univariate and multivariate approaches. The first step consists of running a simulation study designed by varying the parameters of the CUB model, to consider and compare CUB models as well as other methods of missing imputation. We use real datasets on which to base the comparison between our approach and some general methods of missing imputation for various missing data mechanisms.

Modelling relationships between variables has been a major challenge for statisticians in a wide range of application areas. In conducting customer satisfaction surveys, one main objective, is to identify the drivers to overall satisfaction (or dissatisfaction) in order to initiate proactive actions for containing problems and/or improving customer satisfaction. Bayesian Networks (BN) combine graphical analysis with Bayesian analysis to represent relations linking measured and target variables. Such graphical maps are used for diagnostic and predictive analytics. This paper is about the use of BN in the analysis of customer survey data. We propose an approach to sensitivity analysis for identifying the drivers of overall satisfaction. We also address the problem of selection of robust networks. Moreover, we show how such an analysis generates high information quality (InfoQ) and can be effectively combined with an integrated analysis considering various models. (C) 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license

This chapter is about features and capabilities of the R software that support information quality (InfoQ). The examples provided, with their R code, are designed to show how InfoQ can be achieved by treating with R InfoQ components and dimensions.

The importance that users or customers attach to various services and products is an essential part of customer satisfaction surveys. Some proposals for linking satisfaction and importance can be found in available literature. The objective is to identify and understand the dimensions with high importance but low perceived quality. These dimensions are primary candidates for focused improvement initiatives. In this study, we propose to apply a class of statistical models, denoted as CUB models, generally used to estimate the feeling and the uncertainty, to measure the importance of items on observed overall satisfaction. A questionnaire with explicit variables of importance for each dimension is considered to compare the obtained ranks with the observed ones. Then the estimated importance and the perceived quality, both obtained with the CUB models, will be jointly analyzed in different datasets coming from various fields. This approach will be compared with some others reported in the literature.

Bayesian networks (BN) implement a graphical model structure known as a directed acyclic graph (DAG) that is popular in statistics, machine learning, and artificial intelligence. They enable an effective representation and computation of a joint probability distribution (JPD) over a set of random variables. The paper focuses on the selection of a robust network structure according to different learning algorithms and the measure of arc strength using resampling techniques. Moreover, it shows how 'what-if' sensitivity scenarios are generated with BN using hard and soft evidence in the framework of predictive inference. Establishing a robust network structure and using it for decision support are two essential enablers for efficient and effective applications of BN to improvements of products and processes. A customer-satisfaction survey example is presented and R scripts are provided.