Citation

Silverstein BA, Stetson DS, Keyserling WM, Fine LJ. Am. J. Ind. Med. 1997; 31(5): 600-608.

Affiliation

University of Michigan School of Public Health, Ann Arbor, USA.

Copyright

(Copyright © 1997, John Wiley and Sons)

DOI

unavailable

PMID

9099363

Abstract

Work-related upper extremity musculoskeletal disorders "associated with repeated trauma" account for more than 60% of all newly reported occupational illness, 332,000 in 1994 according to the U.S. Department of Labor. These numbers do not include, for example, those disorders categorized as "injuries due to overexertion in lifting," approximately 370,000. Early identification of potential disorders and associated risk factors is needed to reduce these disorders. There are a number of possible methods for conducting surveillance for work-related musculoskeletal disorders (WMDs) based on health outcome: workers' compensation, sickness and accident insurance, OSHA 200 logs, plant medical records, self-administered questionnaires, professional interviews, and physical examinations. In addition, hazard surveillance based on evaluation of job exposures to physical stressors by nonoccupational health personnel is possible. As part of a large labor-management-initiated intervention study to reduce the incidence of WMDs in four automotive plants, we were able to compare the strengths and limitations of each of these surveillance tools. University administered health interviews yielded the highest rate of symptoms; combined physical examinations plus interview (point prevalence) rates were similar to self-administered questionnaires (period prevalence) rates. Plant medical records yielded the lowest rate of WMDs. WMD status on self-administered questionnaire and on physical examination were associated with risk factor exposure scores. This study suggests that symptoms questionnaires and checklist-based hazard surveillance are feasible within the context of joint labor-management ergonomics programs and are more sensitive indicators of ergonomic problems than pre-existing data sources.

Language: en