Numerous epidemiological studies based on time-series analysis have shown associations between morbidity/mortality caused by respiratory and cardiovascular adverse events and chronic exposure to airborne particles (Bell et al. 2004), but a considerable uncertainty remains to be seen. This begs the question of whether these associations represent premature morbidity within only a few days among those people already near to acute health events. Statistical aspects of such a displacement effect (or harvesting) have been discussed by several authors (Dominici et al. 2003 and references therein); a reasonable underlying hypothesis is that mortality/morbidity displacement is associated with shorter timescales, while longer time scales are supposed to be resistant to displacement. If associations reflect only harvesting, the effect of air pollution on morbidity can be considered as having a limited impact from a public-health point of view. In this paper we discuss a new approach to assess the effect of short term changes in air pollution on acute health effects. Our method is based on a Singular Spectrum Analysis (SSA) decomposition of airborne particulate matter time series into a set of exposure variable, each one representing a different timescale. An advantage of our approach is that timescales need not to be set prior to their estimation.

This note reports an updated analysis of global climate change and its relationship with Carbon Dioxide (CO2) emissions: advanced methods rooted in econometrics are applied to bivariate climatic time series. We found a strong evidence for the absence of Granger causality from CO2 emissions to global surface temperature: we can conclude that our findings point out that the hypothesis of anthropogenically-induced climate change still need a conclusive confirmation using the most appropriate methods for data analysis.

Natural killer (NK) cells provide a major defence against human cytomegalovirus (HCMV) infection through the interaction of their surface receptors, including the activating and inhibitory killer immunoglobulin-like receptors (KIRs), and human leucocyte antigen (HLA) class I molecules. Also γ marker (GM) allotypes, able to influence the NK antibody-dependent cell-mediated cytotoxicity, appear to be involved in the immunological control of virus infections, including HCMV. In some cases, their contribution requires epistatic interaction with other genes of the immune system, such as HLA. In the present report, with the aim of gaining insight into the immune mechanisms controlling HCMV, we have studied the possible associations among humoral and NK responses, and HCMV infections. In a previous study we assessed whether the KIR and HLA repertoire might influence the risk of developing symptomatic (n = 60) or asymptomatic (n = 60) disease after primary HCMV infection in the immunocompetent host. In the present study, the immunocompetent patients with primary symptomatic HCMV infection were genotyped for GM3/17 and GM23 allotypes, along with the 60 participants with a previous asymptomatic infection as controls. Notwithstanding the presence of missing data record, advanced missing data recovery techniques were able to show that individuals carrying the GM23 allotypes, both homozygous and heterozygous, GM17/17, HLA-C2 and Bw4T KIR-ligand groups are associated with the risk of developing symptomatic infection. Our findings on the role of both cellular and humoral immunity in the control of HCMV infection should be of value in guiding efforts to reduce HCMV-associated health complications in the elderly, including immunosenescence, and in transplantation.

Among the many tools suited to detect local clusters in group-level data, Kulldorff–Nagarwalla’s spatial scan statistic gained wide popularity (Kulldorff and Nagarwalla in Stat Med 14(8):799–810, 1995). The underlying assumptions needed for making statistical inference feasible are quite strong, as counts in spatial units are assumed to be independent Poisson distributed random variables. Unfortunately, outcomes in spatial units are often not independent of each other, and risk estimates of areas that are close to each other will tend to be positively correlated as they share a number of spatially varying characteristics. We therefore introduce a Bayesian model-based algorithm for cluster detection in the presence of spatially autocorrelated relative risks. Our approach has been made possible by the recent development of new numerical methods based on integrated nested Laplace approximation, by which we can directly compute very accurate approximations of posterior marginals within short computational time (Rue et al. in JRSS B 71(2):319–392, 2009). Simulated data and a case study show that the performance of our method is at least comparable to that of Kulldorff–Nagarwalla’s statistic.

Achieving health equity has been identified as a major international challenge since the 1978 declaration of Alma Ata. Disease risk maps provide important clues concerning many aspects of health equity, such as etiology risk factors involved by occupational and environmental exposures, as well as gender-related and socioeconomic inequalities. This explains why epidemiological disease investigation should always include an assessment of the spatial variation of disease risk, with the objective of producing a representation of important spatial effects while removing any noise. Bearing in mind this goal, this review covers basic and more advanced aspects of Bayesian models for disease mapping, and methods to analyze whether the spatial distribution of the disease risk closely follows that of underlying population at risk, or there exist some nonrandom local patterns (disease clusters) which may suggest a further explanation for disease etiology. We provide a practical illustration by analyzing the spatial distribution of liver cancer mortality in Apulia, Italy, during the 2000–2005 quinquennial.

Killer immunoglobulin-like receptors (KIRs) regulate the activation of Natural Killer cells through their interaction with human leukocyte antigens (HLA). KIR and HLA loci are highly polymorphic and certain HLA-KIR combinations have been found to protect against viral infections. In this study we analyzed whether the KIR/HLA repertoire may influence the course of hepatitis B virus (HBV) infection. Fifty-seven subjects with chronic hepatitis B (CHB), 44 subjects with resolved HBV infection, and 60 healthy uninfected controls (HC) were genotyped for KIR and their HLA ligands. The frequency of the HLA-A-Bw4 ligand group was higher in CHB (58%) than subjects with resolved infection (23%) (crude OR, 4.67; P< 0.001), and HC (10%) (crude OR,12.38; P< 0.001). Similar results were obtained for the HLA-C2 ligand group, more frequent in CHB (84%), than subjects with resolved infection (70%) (crude OR, 2.24; P< 0.10), and HC (60%) (crude OR, 3.56; P< 0.01). Conversely, the frequency of KIR2DL3 was lower in CHB (81%) than in subjects with resolved infection (98%) (crude OR,0.10; P< 0.05). These results suggest a detrimental role of HLA-A-Bw4 and HLA-C2 groups, which are associated with the development of CHB, and a protective role of KIR2DL3. A stepwise variable selection procedure, based on multiple logistic regression analysis, identified these three predictive variables as the most relevant, featuring high specificity (90.9%), and positive predictive value (87.5%) for the development of CHB. Our results suggest that a combination of KIR/HLA gene/alleles is able to predict the outcome of HBV infection. This article is protected by copyright. All rights reserved.

L’indice di Hirsch (comunemente noto come indice h) è stato definito da Hirsch (2005) come quel numero numero h tale che, per un insieme di pubblicazioni di un dato autore, h di queste hanno ricevuto almeno h citazioni, mentre le rimanenti non ne hanno ricevute più di h. Tale metrica è ampiamente utilizzata per quantificare l’impatto della ricerca scientifica individuale (ed istituzionale), e diverse varianti ne sono state proposte tanto al fine di estendere la proposta originale quanto di superarne alcuni inconvenienti. In questo lavoro, discuteremo la crescente importanza di valutare l’impatto della ricerca in un contesto come quello italiano, al fine di poter finalmente garantire che le risorse pubbliche spese possano essere indirizzate verso settori di eccellenza della ricerca, con i conseguenti benefici che ne deriverebbero per il paese: a tal fine, l’indice h potrebbe avere un ruolo molto importante, trattandosi di un indicatore sintetico tanto della produttività scientifica quanto del relativo impatto, che permette quindi di semplificare la caratterizzazione della produzione scientifica dei ricercatori. Pertanto, presenteremo una rassegna completa su h e sugli indicatori ad esso collegati; verrà posta attenzione anche alla relativa teoria statistica, che costituisce un corpus di ricerche in rapida crescita, recentemente sviluppato per distribuzioni di probabilità continue, ed esteso successivamente al caso di distribuzioni aventi supporto sui numeri interi (che è il caso familiare per le applicazioni bibliometriche). Infine, presenteremo una applicazione relativa alla comunità accademica italiana dei docenti di Statistica (sett. scientifico disciplinare SECS-S/01) in servizio come professori di prima fascia.

The aim of this paper is to predict, on a purely algorithmic basis, students who are at risk of dropping out of university. Data used in this study originated from the University of Bari Aldo Moro, during 2013–16, and were provided by the Osservatorio Studenti-Didattica of Miur-Cineca. Data analysis is based solely on the information set available, for each student, inside the university information system. Predictions of individual dropouts have been carried out by means of suitable Machine Learning techniques, known as supervised classification algorithms.

The effects of environmental pollution on spontaneous abortion (SAB) are still unclear. Records of SAB were collected from five cities (514,996 residents) and correlated with PM10, NO2 and ozone levels. Median pollutant concentrations were below legal limits. Monthly SABs positively correlated with PM10 and ozone levels but not with NO2 levels. The mean monthly SAB rate increase was estimated equal to 19.7 and 33.6 % per 10 ?g/m3 increase in PM10 or ozone concentration, respectively. Higher values of PM10 and SABs were evident in cities with- than in those without pollutant industries, with a number of SABs twofolds higher in the former group. In conclusion, SAB occurrence is affected by PM10 (particularly if industrial areas are present) and ozone concentrations, also at levels below the legal limits. Thus, SAB might be considered, at least in part, a preventable condition.

Over the last few years there has been much debate about the hypothe- sis that anthropogenic emissions of CO2 and other greenhouse gases increase global temperature permanently. By using recent advances in time series econometrics, this paper tries to answer the question on how human activity affects Earth’s surface tem- peratures. Bearing in mind this goal, we estimated the long-run cointegration relations between global temperatures and changes in radiative forcings by a set of perturbing factors. We found that the temperature response to a doubling in radiative forcing of anthropogenic greenhouse gases is +2.94 °C [95% CI : +1.91, + 3.97], in perfect accordance with prior research), and that the orthogonalized cumulated effect over a 100 year time period, in response to a unit increase of size of one standard de- viation in greenhouse gas radiative forcing, is +3.86 °C [95% CI : +0.03, + 6.54]. Conversely, the amplitude of solar irradiance variability is hardly sufficient to explain observed variations in the Earth’s climate. Our results show that the combined effect of stochastic trends attributable to anthropogenic radiative forcing variations are driving the Earth’s climate system toward an ongoing phase of global warming, and that such long-run movement is unlikely to be transient.

Heart rate variability (HRV), the variation over time of the period between consecutive heartbeats (RR intervals), is pre-dominantly dependent on the autonomic regulation of the heart rate. By means of a suitable statistical analysis and using a battery of indicators calculated from the RR signal (summarizing its properties both in time and frequency domain, as well as its degree of structural complexity), we will show that it is possible to distinguish, with high sensitivity and specificity, healthy subjects from those which are at high risk of developing malignant cardiac arrhythmias.

This work aims to estimate the relationship between the expected return of a financial investment and its risk by means of a fuzzy version of the Capital Asset Pricing Model (CAPM). The expected return is usually computed as a function both of the rate of a risk-free security, that represents the time value of money, and of a premium that compensates investors for taking on an additional risk in the market. Actually we estimate the parameters of a simple regression model, where the dependent variable consists in the percentage change in prices of a surveyed (stable or volatile) stock and the independent variable consists in the percentage change in market indexes. As both changes in closure prices only partially represent the actual trend in returns, we use a range of observed values for each price; this allows us to estimate the sensitiveness of the stock to risk by means of the so called Fuzzy Least Square Regression. The corresponding estimates are compared with the ones obtained by means of the Ordinary Least Square Regression.