Analysis of occupational accidents in the construction metal products manufacturing subsector in Spain: Trends and risk factors
[EN] Research on work accidents is important to determine the causes of occupational accidents to effectively prevent them in the future and improve workplace safety. This study aims to analyse the evolution of accidents in the metal products manufacturing subsector for construction (CNAE subsector...
| Autores: | , , , , , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2025 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositório: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglês |
| OAI Identifier: | oai:riunet.upv.es:10251/232251 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/232251 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Accidents rate Health & safety Spain Metal products Construction 08.- Fomentar el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo, y el trabajo decente para todos |
| Resumo: | [EN] Research on work accidents is important to determine the causes of occupational accidents to effectively prevent them in the future and improve workplace safety. This study aims to analyse the evolution of accidents in the metal products manufacturing subsector for construction (CNAE subsector 251) in Spain for the period 2009¿2022 to classify accidents into different operational profiles. This will facilitate the proposal of specific preventive measures based on the severity and characteristics of each accident. Data for this study are collected from occupational accident reports via the Delt@ (Electronic declaration of injured workers) IT system. The study variables were classified into five groups: temporal, personal, business, circumstances, and consequences. Accidents at work are more common in males and in middle-aged workers (30¿59 years). Companies with less than five workers, works outside the usual workplace and workers with less three months of length of the service in the company present high accident rate, both in light as fatal accidents. A semi-supervised model has been developed using the Fuzzy Cluster algorithm that can detect serious accidents with a recall rate of approximately 64% and group them into three distinct categories. This makes it possible to propose specific preventive measures for each category, of which there are 12 in total. |
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