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Table 4 Multivariable analysis of the individual predictors of work-related musculoskeletal disorders

From: Prevalence and predictors of work-related musculoskeletal disorders among workers of a gold mine in south Kivu, Democratic Republic of Congo

Individual factors Self-reported WRMSDs in the past 12 months (%) Unadjusted Model Adjusted model
Yes (n = 49) No (n = 147) PR [95% CI] PR [95% CI]
Age in years
  < 30 9 (24.3) 28 (75.7) 1   1  
 30–34 12 (22.2) 42 (77. 8) 0.91 [0.43, 1.95] 0.95 [0.47, 1.91]
 35–39 8 (16.7) 40 (83.3) 0.69 [0.29, 1.61] 0.75 [0.33, 1.68]
  ≥ 40 20 (35.1) 37 (64.9) 1.44 [0.74, 2.82] 1.63 [0.86, 3.09]
Department
 Mining 13 (43.3) 17 (56.7) 1    
 Engineering 12 (37.5) 20 (62.5) 0.87 [0.47, 1.59]   
 Maintenance 3 (8.8) 31 (91.2) 0.20 [0.06, 0.65]**   
 Metallurgy 8 (26.7) 22 (73.3) 0.54 [0.26, 1.13]   
 Mineral Resources 12 (36.4) 21 (63.6) 0.84 [0.46, 1.55]   
 Others 1 (3.0) 32 (97.0) 0.70 [0.01, 0.51]**   
Level of education
 Primary 5 (29.4) 12 (70.6) 1    
 Secondary 18 (21.7) 65 (78.3) 0.74 [0.32, 1.72]   
 Tertiary 24 (25.5) 70 (74.5) 0.87 [0.38, 1.96]   
Sex
 Male 48 (25.7) 139 (74.3) 1    
 Female 1 (11.1) 8 (88. 9) 0.43 [0.07, 2.80]   
Experiencein mining
 1–5 years 29 (20.4) 113 (79.6) 1   1  
  > 5 years 20 (37.0) 34 (63.0) 1.81 [1.13, 2.92]* 1.39 [0.88, 2.18]
  1. ***p < 0.001, ** p < 0.01, * p < 0.05. significant association between explanatory variables and reporting WRMSDs
  2. CI confidence interval, PR prevalence ratios, WRMSD work related musculoskeletal disorders