Work-related health problems among resident immigrant workers in Italy and Spain

Background: in both Spain and Italy the number of immigrants has strongly increased in the last 20 years, currently representing more than the 10% of workforce in each country. The segregation of immigrants into unskilled or risky jobs brings negative consequences for their health. The objective of this study is to compare prevalence of work-related health problems between immigrants and native workers in Italy and Spain. Methods: data come from the Italian Labour Force Survey (n=65 779) and Spanish Working Conditions Survey (n=11 019), both conducted in 2007. We analyzed merged datasets to evaluate whether interviewees, both natives and migrants, judge their health being affected by their work conditions and, if so, which specific diseases. For migrants, we considered those coming from countries with a value of the Human Development Index lower than 0.85. Logistic regression models were used, including gender, age, and education as adjusting factors. Results: migrants reported skin diseases (Mantel-Haenszel pooled OR=1.49; 95%CI: 0.59-3.74) and musculoskeletal problems among those employed in agricultural sector (Mantel-Haenszel pooled OR=1.16; 95%CI: 0.69-1.96) more frequently than natives; country-specific analysis showed higher risks of musculoskeletal problems among migrants compared to the non-migrant population in Italy (OR=1.17; 95% CI: 0.48-1.59) and of respiratory problems in Spain (OR=2.02; 95%CI: 1.02-4.0). In both countries the risk of psychological stress was predominant among national workers. Conclusions: this collaborative study allows to strength the evidence concerning the health of migrant workers in Southern European countries.

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.