Revista Dor (Apr 2017)

Prevalence of musculoskeletal pain in leather products industry workers: cross-sectional study in a city of the state of Minas Gerais

  • Luiz Felipe Silva,
  • Sarah Lamas Teixeira

DOI
https://doi.org/10.5935/1806-0013.20170027
Journal volume & issue
Vol. 18, no. 2
pp. 135 – 140

Abstract

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ABSTRACT BACKGROUND AND OBJECTIVES: Musculoskeletal injuries induced by labor process and organization are relevant for health/labor relationship. This study aimed at investigating the prevalence of musculoskeletal complaints and associated factors among leather products manufacturers. METHODS: Cross-sectional study where data were obtained by means of self-applied questionnaires to 320 workers distributed among 13 plants of the city of Cristina, MG, between February and March 2011. Nordic questionnaire of musculoskeletal symptoms was applied to measure the prevalence of complaints in different body regions. A descriptive analysis was carried out on the socio-demographic profile of the studied population. Multivariate logistic regression was used to describe the association between dependent variable, musculoskeletal complaint and the set of explanatory variables, with adjusted odds ratio calculation. Logistic regression was used with adjusted odds ratio calculation. RESULTS: The study involved 138 workers. Better adjusted multivariate model after confusion variables control was for knee pain, with prevalence of 40.0% among males and 24.1% among females. Sewing and finishing sectors behaved as "protection", that is, less chance for pain as compared to the cutting sector. Age had negative association, that is, the higher the age the lower the chance of pain. In a different adjusted model for shoulder pain, workers and time working on the job showed higher chance of pain. CONCLUSION: The prevalence of complaints was higher than that found in the literature. Significant variables were identified which may subsidize the prevention of job distress, such as knee pain. Further studies are needed with the inclusion of other variables and other designs to minimize biases.

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