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      Work-Related Eye Injuries: A Relevant Health Problem. Main Epidemiological Data from a Highly-Industrialized Area of Northern Italy

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          The province of Modena is one of the most industrialized areas of Northern Italy. The medical records of the Ophthalmological Emergency Department (OED) of Modena University Hospital were studied: there were 13,470 OED accesses in 2014 and in 754 cases that an occupational eye injury occurred. The frequency of work-related eye injuries (3‰) was lower compared to other published studies, but the absolute number is still relevant, showing the need for more adequate prevention, especially in metal work, construction work, and agriculture, where the worst prognoses were observed. Intervention programs must be implemented as early as possible in the working life, considering that the frequency in younger workers is about double that of the oldest age class (3.5‰ vs. 1.8‰), and special attention should also be given to foreigners, who have a 50% higher injury risk. Furthermore, the planning of specific interventions for eye-injured workers may be useful, considering that a previous injury does not appear to encourage the adoption of preventive interventions, and a subgroup of eye-injured workers have a potential risk for new injuries. Finally, the data presented here indicates how OED records, integrated with specific occupational information, can be applied for studies on work-related eye injuries.

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              Cognitive and Motivational Biases in Decision and Risk Analysis.

              Behavioral decision research has demonstrated that judgments and decisions of ordinary people and experts are subject to numerous biases. Decision and risk analysis were designed to improve judgments and decisions and to overcome many of these biases. However, when eliciting model components and parameters from decisionmakers or experts, analysts often face the very biases they are trying to help overcome. When these inputs are biased they can seriously reduce the quality of the model and resulting analysis. Some of these biases are due to faulty cognitive processes; some are due to motivations for preferred analysis outcomes. This article identifies the cognitive and motivational biases that are relevant for decision and risk analysis because they can distort analysis inputs and are difficult to correct. We also review and provide guidance about the existing debiasing techniques to overcome these biases. In addition, we describe some biases that are less relevant because they can be corrected by using logic or decomposing the elicitation task. We conclude the article with an agenda for future research.

                Author and article information

                Role: Academic Editor
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                International Journal of Environmental Research and Public Health
                06 June 2017
                June 2017
                : 14
                : 6
                [1 ]Chair of Occupational Medicine, Department of Biomedical, Metabolic and Neural Sciences, University of Modena & Reggio Emilia, 41125 Modena, Italy; dallolio.e@ (E.D.O.); albertomodenese1@ (A.M.)
                [2 ]Institute of Ophthalmology, University of Modena & Reggio Emilia, 41124 Modena, Italy; michele.demaria86@ (M.D.M.); campi.luca@ (L.C.); gianmaria.cavallini@ (G.M.C.)
                Author notes
                [* ]Correspondence: f.gobba@ ; Tel.: +39-059-205-5463
                © 2017 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (



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