Predictors of lost time from work among nursing personnel who sought treatment for back pain

Work. 2010;37(3):285-95. doi: 10.3233/WOR-2010-1080.

Abstract

Objective: To examine possible predictors of lost workdays among nurses and nurses' aides who sought treatment for work-related back pain.

Participants: Nursing staff employed at a tertiary care medical center over a 13-year time period (1994 through 2006).

Methods: We used existing data from clinic surveys administered to nursing personnel during their initial treatment visit to the hospital's occupational health clinic. Predictors of losing ≤ 7 and ≥ 8 workdays was examined.

Results: 589 of 708 (83%) nursing personnel with complaints of work-related back pain completed the survey, with 31% resulting in lost workdays. Experiencing sudden onset of pain (RR:1.9; 95% CI: 1.1, 3.1), a combination of severe pain with numbness and tingling in the back/legs (RR: 7.4; 95% CI: 2.9, 18.6), severe pain only (RR: 4.4; 95% CI: 1.8, 11.1), numbness and tingling in the back/legs only (RR: 3.5; 95% CI: 1.0, 12.2), and working < 5 years at the hospital (RR: 2.3; 95% CI: 1.2, 4.7) were predictive of losing ≥ 8 workdays. Job title, work demands, work conflicts, and most psychosocial factors were not predictive.

Conclusions: Severe pain, neurologic symptoms and sudden onset of pain were predictive of delayed return-to-work; however, these symptoms alone should not be considered indicators of poor outcomes given that most workers who reported these symptoms returned to work in less than 8~days. Among these health care workers, lost workdays appear to be related to more severe pathology rather than workplace characteristics.

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Back Pain / epidemiology*
  • Back Pain / prevention & control
  • Back Pain / rehabilitation
  • Back Pain / therapy
  • Humans
  • Musculoskeletal Diseases / epidemiology*
  • Nurses
  • Nursing Assistants
  • Occupational Diseases / epidemiology*
  • Occupational Health / statistics & numerical data*
  • Risk Factors
  • Sick Leave / statistics & numerical data*