Purpose: This paper proposes an analysis of occupational accidents in Italy at the regional level. For this purpose, our panel is composed of 20 regions over the 2010–2019 time span.Design/methodology/approach: We apply different econometric estimation techniques (pooled OLS model, panel fixed and random effects models and semiparametric fixed model) using INAIL and ISTAT data. Our models investigate workplace accidents at the regional level by accounting for socioeconomic, labour market and productive system variables and controlling for possible underreporting bias.Findings: Overall results reveal the existence of a relevant under-notification phenomenon of accidents at work with respect to moderate accidents, that is higher especially for the southern regions of Italy. However, when considering as outcome variable an alternative set of more severe workplace accidents our model specification remains highly jointly statistically significant. Among our main findings, the analysis shows that worker skills (blue collar) strongly affect the regional pattern of workplace accidents, i.e. an increase of 1% of low paid employees generates about an increase of 1.8 severe workplace accidents per thousand workers. Moreover, we provide evidence that the size of the firm is inversely related to the occupational accident rates. Finally, our results highlight a nonlinear relationship between GDP and occupational accidents for the Italian regional context, confirmed by the high statistical significance of the quadratic term in all the estimated linear models and by the semi-parametric analysis.Originality/value: A first element of originality of our study consists of investigating the macro determinants of occupation accidents at a regional Italian level. Second, the empirical literature (Boone and Van Ours, 2006) highlights the possible bias of underreporting behaviours on nonfatal accidents in contrast to fatal accidents that are always reported. From this perspective, we have identified a few analyses (namely, Boone et al., 2011) considering different accident sets characterised by different severity degrees. Thus, this paper contributes to the literature considering five alternative subsets of accidents stratified by degree of severity (i.e. moderate, severe, moderate plus severe, severe plus fatal and total accident rates) to test for possible underreporting bias affecting our econometric model.

Brivaracetam (BRV) is a recent antiseizure medication (ASM) approved as an add-on therapy for people with focal epilepsy. BRV has a good efficacy and safety profile compared to other ASMs. However, its specific effects on resting-state EEG activity and connectivity are unknown. The aim of this study is to evaluate quantitative EEG changes induced by BRV therapy in a population of adult people with drug-resistant epilepsy (PwE) compared to healthy controls (HC). We performed a longitudinal, retrospective, pharmaco-EEG study on a population of 23 PwE and a group of 25 HC. Clinical outcome was dichotomized into drug-responders (i.e., >50% reduction in seizures' frequency; RES) and non-responders (N-RES) after two years of BRV. EEG parameters were compared between PwE and HC at baseline (pre-BRV) and after three months of BRV therapy (post-BRV). We investigated BRV-related variations in EEG connectivity using the phase locking value (PLV). BRV therapy did not induce modifications in power spectrum density across different frequency bands. PwE presented lower PLV connectivity values compared to HC in all frequency bands. RES exhibited lower theta PLV connectivity compared to HC before initiating BRV and experienced an increase after BRV, eliminating the significant difference from HC. This study shows that BRV does not alter the EEG power spectrum in PwE, supporting its favourable neuropsychiatric side-effect profile, and induces the disappearance of EEG connectivity differences between PwE and HC. The integration of EEG quantitative analysis in epilepsy can provide insights into the efficacy, mechanism of action, and side effects of ASMs.

Our goal was to evaluate the impact of gender on the 10-year outcome of patients after isolated coronary artery bypass grafting (CABG) included in the Italian nationwide PRedictIng long-term Outcomes afteR Isolated coronary arTery bypass surgery (PRIORITY) study. The PRIORITY project was designed to evaluate the long-term outcomes of patients who underwent CABG and were included in 2 prospective multicentre cohort studies. The primary end point of this analysis was major adverse cardiac and cerebrovascular events. Baseline differences between the study groups were balanced with propensity score matching and inverse probability of treatment. Time to events was analysed using Cox regression and competing risk analysis. The study population comprised 10 989 patients who underwent isolated CABG (women 19.6%). Propensity score matching produced 1898 well-balanced pairs. The hazard of major adverse cardiac and cerebrovascular event was higher in women compared to men [adjusted hazard ratio (HR) 1.13, 95% confidence interval (CI) 1.03–1.23; P = 0.009]. The incidence of major adverse cardiac and cerebrovascular event in women was significantly higher at 1 year (HR 1.31, 95% CI 1.11–1.55; P < 0.001) and after 1 year (HR 1.11, 95% CI 1.00–1.24; P = 0.05). Mortality at 10 years in the matched groups was comparable (HR 1.04, 95% CI 0.93–1.16; P = 0.531). Women have significantly a higher 10-year risk of myocardial infarction (adjusted HR 1.40, 95% CI 1.17–1.68; P = 0.002) and percutaneous coronary intervention (adjusted HR 1.32, 95% CI 1.10–1.59; P = 0.003). The present study documented an excess of non-fatal cardiac events after CABG among women despite comparable 10-year survival with men. These findings suggest that studies investigating measures of tertiary prevention are needed to decrease the risk of adverse cardiovascular events among women.

This paper presents an empirical investigation on the determinants of workplace accidents across Europe and focuses on the extent to which production-system characteristics (employment sectoral risk, size of firms, temporary contracts), business cycle and socio-economic factors (GDP, level of investments, unemployment, education) and other territorial controls (crime index) might account for cross-country heterogeneity. We use Eurostat data, and our panel is composed of 27 European countries over the period 2010–2018. Implementing different functional forms and estimation methodologies (pooled OLS, panel fixed and random effects models, system-GMM and semiparametric fixed effects model), we find robust evidence that productive-system structural characteristics, business cycle controls and the other territorial variables are effective in explaining European cross-country heterogeneity. Moreover, we find evidence of a nonlinear relationship between GDP and occupational accidents.

The Fuzzy K-Means algorithm extends the well-known classical K-Means algorithm by replacing the standard allocation matrix with the membership degree one. Consistently with the fuzzy approach to clustering, this allows for obtaining a soft assignment of the units to the clusters. The units are assigned to the clusters with membership degree taking values in the unit interval. In practice, what we get from Fuzzy K-Means is a fuzzy partition where even the units clearly belonging to only one cluster generally present non-zero membership degrees to all the clusters. In order to overcome this drawback, a generalization of Fuzzy K-Means is proposed where an $$\hbox {L}_0$$ L 0 regularization term for the membership degree matrix is introduced. This makes it possible to obtain a sparse membership degree matrix, where the units that clearly belong to one cluster have membership degrees strictly equal to one to the cluster involved and zero to the other clusters, without compromising the soft membership degrees of the units with unclear assignments. The adequacy of the proposal is evaluated by means of simulation and real-case studies.