The purpose of this study was to identify patient characteristics that influence the time lost from work after worker injury. As hypothesized, our findings demonstrate that the worker’s claim type, injured body part, physical requirement, and educational group are independent predictors of days taken off from work following injury (Table 1). Although the number of subjects that were available for analysis precluded from recommending a formula for estimating the number of days lost given a specific injury and specific patient characteristics, the identified characteristics may be used by medical providers to make recommendations and set patients’ expectations for the anticipated days lost from work.
Patients in jobs with more significant physical requirement were more likely to require more days away from work following injury (Figure 5). This was expected, as employees can perform sedentary tasks sooner than physical ones when recovering from injury, and is consistent with findings in current literature, which have demonstrated that patients in jobs involving constant lifting are more likely to remain disabled at one year with musculoskeletal back pain . Gillen et al. also reported that patients with increased job strain relative to reward are less likely to return to work . In our study, both bivariate and multivariate analysis demonstrated correlation between the physicality of the job description and days lost from work. An explanation for these finding could be that more physical jobs, especially those requiring use of heavy machinery, may require more complete healing of the injury to allow adequate performance and therefore require longer healing time. Patients that have mixed physical/sedentary jobs are likely to take less time off work because they can return to their sedentary functions prior to the more physical ones.
Our findings demonstrate that employees in jobs requiring lower educational requirement are likely to return to work later than patients with undergraduate or graduate degrees (Figure 6). Similar results have been found in previous studies, which have demonstrated that educational level, white-collar occupation, job satisfaction, and expectation to return to work were significantly correlated to earlier return to work [5,7,8,9], while lower socioeconomic status is associated with less likelihood of returning to work . Occupations requiring advanced education have been shown to increase satisfaction and to be associated with higher compensation compared to lower academic categories, motivating employees to return to work earlier . Furthermore, patients in these occupations may also have stronger expectation to return to work due to greater job security and/or less severe injuries, while patients in lower academic categories have less job security and/or more severe injuries, making expectation to return less clear. Previous studies have also reported that jobs requiring less education are associated with increased work stress, which could reduce a patient’s interest in returning to work . Additionally, pressure to return to work may be greater in higher educational groups because these jobs tend to be more specialized and therefore less replaceable than those requiring less education. Lastly, occupations requiring more education are often less physical and therefore may require shorter healing time to return to basic job functions. However, the findings of our study suggest that education level and the physicality of the occupation are independent influencers of time lost from work.
Finger and rib/sternum injuries were independent predictors of fewer days lost from work on multivariable analysis (Table 1). This is consistent with injury severity scoring systems used by the United States Department of Labor, in which finger injuries are given the lowest possible scores [12,13]. It is possible that these injuries are associated with fewer days lost from work because they are less likely to require surgery or affect mobility, therefore allowing for shorter healing time and a higher level of functional ability despite the injury.
Older age was significantly associated with increased days lost from work on bivariate analysis and approached statistical significance in multi-variable analysis. This finding is supported by previous studies that showed that older patients do not heal as fast as younger ones [14,15,16]. Age was also shown to be an independent predictor of long-term disability leave in patients with musculoskeletal injury . The results of our multi-variable analysis suggest that in our population age was less of a predicting factor for days lost from work than other associated factors (Table 1).
Limitations to this study primarily relate to the limited nature of the dataset used. First, despite the large population used by this data set, the number of events resulting in significant amount of days lost before returning to work was relatively small and did not allow for meaningful comparisons between subgroups. Next, while job descriptions were available for most of the occupations listed, specific indicators of physicality (i.e. number of pounds lifted) were not. Knowing the specific physical requirements would have allowed for more exact stratification of physical groups and therefore more accurate determination of how physicality affects time lost from work. Furthermore, these data do not describe the success of return to work. While the dataset states that a patient returned to work, we were unable to determine the capacity at which they returned (half-duty, full-duty, etc.). The institutional dataset used in this study included a wide range of occupations with employees from many academic and socioeconomic backgrounds, increasing its generalizability. However, the specific institutional rules regarding medical leave may not reflect those of other employers and may have influence the return to work time. Lastly, this dataset does not address the socioeconomic and cultural influences on days lost from work. Patients in an unstable socioeconomic situation will likely return sooner if they are not receiving their full pay while on disability, and lack of job security may push an employee to return faster than those who are confident that their job will continue to be available to them. Marital status, number of dependents, cost of living, and social behaviors may also affect days lost from work in a similar fashion. Future studies are needed to further analyze the association of these factors with days lost from work.