Non-chemical risk assessment for lifting and low back pain based on Bayesian threshold models

Details

• Personal Author:
  Lu M-L ; Pandalai, Sudah P. ; Wheeler, Matthew W.
• Description:
  Background: Self-report low back pain (LBP) has been evaluated in relation to material handling lifting tasks, but little research has focused on relating quantifiable stressors to LBP at the individual level. The National Institute for Occupational Safety and Health (NIOSH) Composite Lifting Index (CLI) has been used to quantify stressors for lifting tasks. A chemical exposure can be readily used as an exposure metric or stressor for chemical risk assessment (RA). Defining and quantifying lifting non-chemical stressors and related adverse responses is more difficult. Stressor-response models appropriate for CLI and LBP associations do not easily fit in common chemical RA modeling techniques (e.g. Benchmark Dose methods), so different approaches were tried. Methods: This work used prospective data from 138 manufacturing workers to consider the linkage of the occupational stressor of material lifting to LBP. The final model used a Bayesian random threshold approach to estimate the probability of an increase in LBP as a threshold step function. Results: Using maximal and mean CLI values, a significant increase in the probability of LBP for values above 1.5 was found. Conclusion: A risk of LBP associated with CLI values greater than 1.5 existed in this worker population. The relevance for other populations requires further study. [Description provided by NIOSH]
• Subjects:
  Manufacturing And Industrial Facilities Musculoskeletal Diseases Musculoskeletal System Occupational Health Risk Assessment Safety
• Keywords:
  Analytical Models Author Keywords: Bayes Theorem Lifting Low Back Disorders Low Back Pain Manual Lifting Manual Materials Handling Materials Handling Mathematical Models MSD Musculoskeletal Diseases Musculoskeletal Disorders Musculoskeletal System Disorders NIOSH Lifting Equation Occupational Exposure Physical Capacity Physical Stress Quantitative Analysis Quantitative Risk Assessment Risk Assessment Statistical Analysis Task Performance

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• ISSN:
  2093-7911
• Document Type:
  Text
• Genre:
  Journal Article
• Place as Subject:
  Ohio ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  EID - Education and Information Division ; DART - Division of Applied Research and Technology
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  206-211
• Volume:
  8
• Issue:
  2
• NIOSHTIC Number:
  nn:20048938
• Citation:
  Saf Health Work 2017 Jun; 8(2):206-211
• Contact Point Address:
  Sudha P. Pandalai, MD, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, Education and Information Division, MS C-15, 1090 Tusculum Avenue, Cincinnati, OH 45226
• Email:
  SPandalai@cdc.gov
• Federal Fiscal Year:
  2017
• Peer Reviewed:
  True
• Source Full Name:
  Safety and Health at Work
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:d85e18ee01b39b6a587e4fd9ed4477dd158eda67e8a2f2672642de0cd25a2330ad2328d0df3f20f402a2192a0105e7c1d67f1547c86511cc95dd4369a41ea51d
• Download URL:
  https://stacks.cdc.gov/view/cdc/203554/cdc_203554_DS1.pdf
• File Type:
  [PDF - 424.99 KB ]