Identificação de fatores que impactam na saúde ocupacional de profissionais expostos ao agente físico ruído em empresas do ramo metalúrgico

Abstract

In the last decades, companies have invested in equipment, machinery and new technologies to improve their manufacturing processes to become more competitive, generating more profits and improving the work environment. However, these small, medium and large equipment and machinery generate noises of different levels. In the metallurgical sector, exposure to noise is a characteristic of the industrial environment and in companies that have transportation in their manufacturing area. Mainly during the shaping of metals, production and manufacturing processes such as cutting, bending, sanding, welding, etc. This can cause long term damage such as discomfort, hypertension, cardiovascular diseases, psychological symptoms, tends to harm the understanding of reading and memory. Thus, this work aimed to apply statistical tools to identify the factors that impact occupational health of professional exposed to noise in metalworking companies. To do so, it was necessary to carry out on-site visits in three companies, called A, B and C, to interview employees from various sectors. Documents on the types of leave and complaints of employees on occupational diseases were analyzed and noise measured in loco and verified how many employees are exposed to them. Finally, statistical techniques were applied on the data to facilitate the decision-making of the managers. After the interviews and application of Pareto analysis in the data, the results showed that the symptoms most cited by the employees were: tiredness, difficulty concentrating, stress, irritability, depression and tinnitus. Applying Pearson's correlation to noise exposure data above or below 80 dB can be seen that, for all companies, the administrative sector is exposed to noise levels below 80 dB (A) and, as it was if expected, the production and maintenance sectors are exposed to noise above 80 dB (A). And that the later sectors are also responsible for the removals of most employees for either 15 or more days. By applying Pearson's correlations, it can be verified that theories of relation noise exposure by other types of problems have a strong correlation, as the sector approaches production.