## 3 Results
### Distribution and Sick Leave Days of Knee MSDs and Factors Associated with Knee MSDs  
Table 6 shows the distributions of total MSDs and knee MSDs and the mean and SD of sick leave days by workers' company size, occupation, age, MSD type, and work experience. According to occupations, there was a significant difference in frequency distributions between knee MSDs and the other MSDs (\(\chi^{2}\) = 8.497, \(p\) = 0.204). Fitters (24.1%) and electricians or plumber workers (24.1%) had prevalent knee MSDs. The ratio of knee MSDs among total MSDs was the highest in grinding workers (27.9%), followed by fitters (20.6%). However, the mean of sick leave days for knee MSDs was the highest in electricians or plumber workers (413.8 days).  
According to the workers' age, there was a significant difference in frequency distributions between knee MSDs and the other MSDs (\(\chi^{2}=4.690,p=0.196\)). The proportion of total knee MSDs and the ratio of knee MSDs among the total MSDs were higher in workers of 50-59 years. The mean of sick leave days for knee MSDs was also the highest in workers of 50-59 years (435.0 days).  
There was a significant difference in frequency distributions between knee MSDs and the other MSDs according to MSD type (\(\chi^{2}=169.541,p<0.001\)). In the case of knee MSDs, a rupture was the most prevalent type of injury (84.0%), followed by sprain (16.0%). The average number of sick leave days of sprain was 405.1 days.  
There was a significant difference in frequency distributions between knee MSDs and the other MSDs according to the worker's work experience (\(\chi^{2}=7.582,p=0.108\)). The proportion of total knee MSDs was higher in those with \(\geq\)20 years of work experience (41.4%), and the average number of sick leave days was also higher in those with \(\geq\)20 years of work experience (414.0 days).  
According to company size, there was a significant difference in frequency distributions between knee MSDs and the other MSDs (\(\chi^{2}=5.137,p=0.162\)). Companies with \(\geq\)1000 workers had the most frequent knee MSDs (59.9%), and the possibility of knee MSDs among total MSDs was also high in companies with 30-99 workers (20.6%) or companies  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Total**} & \multirow{2}{*}{**Knee MSDs**} & \multicolumn{2}{c}{**Sick Leave Days**} & \multicolumn{2}{c}{**Logistic Regression on Knees**} \\ \cline{2-9}  & \(N\) & \(n\) & \% & \(n/N\) & Mean & SD & OR & [95\% CI] & \(p\) \\ \hline \multirow{9}{*}{
\begin{tabular}{} \end{tabular} } & 985 & 162 & 100\% & 16.4\% & 332.6 & 327.4 & \multirow{9}{*}{} &  
with 100-999 workers (20.0%). The mean of sick leave days for knee MSDs was the highest at companies with \(\geq\)1000 workers (366.5 days).  
Table 6 also shows the result of the binary logistic regression analysis on knee MSDs. The factor most significantly associated with knee MSDs was the MSD type (classification table value = 83.6%). The logistic regression model was tested by the G value of the model coefficient (\(\chi^{2}\) = 221.724, \(p\) < 0.001). The explanatory power of the dependent variable was the Nagelkerke value (0.341), and the fitness test for the variable was the Hosmer and Lemeshow test (\(\chi^{2}\) = 0.522, \(p\) = 0.991 > 0.05). Therefore, it was found that the model and explanatory power were suitable.  
In terms of MSD type, the possibility that a rupture will be confirmed as a knee MSD is 3.699 times (95% CI = (2.327, 5.878)) higher than that of a sprain.