On the quantitative relationships between individual/occupational risk factors and low back pain prevalence using nonparametric approaches

Joint Bone Spine. 2011 Dec;78(6):619-24. doi: 10.1016/j.jbspin.2011.01.014. Epub 2011 May 5.

Abstract

Objectives: To explore dual quantitative relationships between low back pain (LBP) prevalence and different individual and occupational risk factors, and detect the most important ones which can be used as weighted input data in LBP prediction diagnosis models, providing effective tools to help with the implementation of protection and prevention strategies among hospital staff.

Methods: Fourteen predictor individual risk factors (e.g., age, gender, body mass index BMI [kg/m(2)], domestic activity, etc.) and 17 occupational risk factors (e.g., job status, standing hours/day, sufficient break time, job dissatisfaction, etc.) were collected using self-reported questionnaire among the staff of Sacré-Coeur hospital - Lebanon (used as a case study), and correlated with LBP prevalence using Kendall's tau-b bivariate nonparametric approaches.

Results: This study indicates that among the investigated occupational risk factors, job status, working hours/day, and standing hours/day are the most influencing on LBP prevalence (highly correlated with other factors at 1 and 5% confidence levels). It also shows that strong positive (between 0.25 and 0.65)/negative (from -0.38 to -0.26) statistical correlations to LBP prevalence exist between these risk occupational factors and working days/week, sitting hours/day, job stress, job dissatisfaction, children care, and car driving. The weekly hours of domestic activity, the staff height, and gender type have proven also to be the strongest individual factors in aggravating LBP disease. These individual factors are highly correlated at 1% significance level (ranging between 0.28 and 0.49 for positive correlations, and from -0.49 to -0.25 for negative ones) to children care, weight, extra professional activity, and use of handling techniques.

Conclusions: These obtained bivariate correlations can be used successfully by expert physicians in their decision making for LBP diagnosis.

MeSH terms

  • Activities of Daily Living
  • Adult
  • Age Factors
  • Body Mass Index
  • Female
  • Health Surveys
  • Humans
  • Job Description
  • Job Satisfaction
  • Low Back Pain / diagnosis*
  • Low Back Pain / epidemiology*
  • Low Back Pain / etiology
  • Male
  • Middle Aged
  • Models, Statistical
  • Occupational Injuries / diagnosis*
  • Occupational Injuries / epidemiology*
  • Occupational Injuries / etiology
  • Personnel, Hospital*
  • Posture
  • Prevalence
  • Risk Factors
  • Sex Factors
  • Surveys and Questionnaires*