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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