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0383 Use of an O*NET based job exposure matrix to predict prevalence of Carpal Tunnel Syndrome in a large pooled cohort
  1. Ann Marie Dale1,
  2. Angelique Zeringue1,
  3. Carisa Harris-Adamson2,
  4. Matt Thiese3,
  5. Stephen Bao4,
  6. Sue Burt5,
  7. Linda Merlino6,
  8. Arun Garg7,
  9. Ellen Eisen2,
  10. Fred Gerr6,
  11. Kurt Hegmann3,
  12. Jay Kapellusch7,
  13. David Rempel8,
  14. Barbara Silverstein4,
  15. Bradley Evanoff1
  1. 1Washington University School of Medicine, St. Louis, MO, USA
  2. 2University of California, Berkeley, Berkeley, CA, USA
  3. 3University of Utah, Salt Lake City, UT, USA
  4. 4Washington State Department of Labor and Industries, Olympia, WA, USA
  5. 5Eformerly National Institute for Occupational Safety and Health, Cincinnati, OH, USA
  6. 6F University of Iowa, Iowa City, IO, USA
  7. 7University of Wisconsin-Milwaukee, Milwaukee, WI, USA
  8. 8University of California, San Francisco, San Francisco, CA, USA


Objectives To determine if job title based physical exposure measures predicted prevalent carpal tunnel syndrome (CTS) in a large pooled cohort of workers.

Method We pooled baseline examination data from six prospective cohort studies, restricting analyses to those employed at least 1 year. CTS was defined as median neuropathy plus typical symptoms. Physical exposure estimates for static strength, dynamic strength, time spent making repetitive motions, and time handling objects were extracted from the Occupational Network (O*NET) database using Standard Occupational Classification codes based on reported job title. Three exposure categories of high force/ high repetition, low force/ low repetition, and mixed high and low exposures were entered into logistic regression models adjusting for age, gender, body mass index (BMI), diabetes, rheumatoid arthritis, employed time and study site.

Results Of 3562 in the pooled cohort, 7.6% met a prevalent CTS definition with mean employed time of 7.9 years (SD 8.2). Compared to subjects with low job requirements for dynamic strength and repetitive motion, those with mixed exposures or high exposures showed increased prevalence of CTS (OR 1.46; 95% CI: 1.01–2.11 and OR 2.32; 95% CI: 1.15–4.67, respectively). Similar dose dependent associations of combined exposures were shown for all exposure combinations tested, with high/high combinations having the largest effect sizes (OR range 2.32–3.17) relative to the low force/low repetition exposure combinations.

Conclusions Use of job-title based exposures was useful for demonstrating associations with prevalent CTS. Jobs with combined high exposures of force and repetition showed consistently greater risk of CTS compared to jobs with lower exposure levels.

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