Análise dos indicadores de Afastamentos por Distúrbios Musculoesqueléticos na Cadeia Produtiva de Proteína Animal: Diferenças Segundo Gênero e Ocupação

Resumo (EN)

This dissertation analyzes the occurrence of Work-Related Musculoskeletal Disorders (WRMSDs) in Brazil's animal protein production chain, focusing on sick leave records from 2006 to 2024. The study was motivated by the high prevalence of these diseases in the sector and the need to analyze the influence of demographic and occupational factors on illness patterns. The research adopts a quantitative, descriptive, and longitudinal approach. The analysis was based on secondary data from the Notifiable Diseases Information System (SINAN), covering the 18 years. Data were stratified by diagnosis (ICD-10), occupational role, gender, and age group. Findings confirm the high prevalence of WRMSDs, strongly associated with ergonomic factors (repetitive movements and cold environments). The M75 diagnosis (Shoulder Lesions) was concentrated in critical cutting and deboning functions, such as slaughterers, butchers, and meat cutters. A significant occupational risk segregation was observed, along with an inversion in epidemiological patterns starting in 2022, with male notifications surpassing those of females. This phenomenon is attributed to improvements in surveillance systems and increased recording of the Z57 code (Occupational exposure to risk factors), indicating a shift in notification culture and the visibility profile of occupational risks. The increase in the Z57 code (Occupational Exposure to Risk Factors) among men suggests greater recognition of environmental and organizational risks in the sector. The persistence of disorders and the inequality in exposure demand that workers' health policies be gender- and age-sensitive, overcoming the one-size-fits-all approach. Effective prevention relies on adopting differentiated strategies, psychosocial management, and investment in technology. Future research should quantify the economic impact of absences, calculate the Return on Investment (ROI) of mechanization, and incorporate intersectional analyses for fairer risk management.