Distal Radius Fracture (DRF) is the most common injury-fractures. Based on U.
S. Department of Labor statistics, fracture is the fourth common injury type constituting the 5-13% of non-fatal occupational injuries and illnesses in the industry. Whether an accident that may result to this kind of fracture, befall on an individual at work or on personal time, this may result to a significant loss of time for work. The work-loss average was found to be 27 days while 46% cases incurred 31 days or more. Most DRF studies were centred on treatment approaches and evaluated outcomes based on radiographic results.
Although a greater number of epidemiological studies, independent of other health disorders, correlated DRF with long-term work disability, few studies investigated its impact on work. Purpose The study conducted by MacDermid, Roth, and McMurtry (2007) aimed to provide a deeper understanding on the work impact of DRF in terms of time lost from work, baseline patient characteristics, job demands, and injury characteristics. In addition, assessments of statistical models on self-reported pain or disability and measured wrists impairments were also explored.Methods Overview The study was an observational investigation approved by the Ethics Board of the University of Western Ontario. Employed patients of orthopaedic clinic were purposively chosen. Those who were able to speak in English and participate follow-up sessions were prioritized while patients with complete nerve lacerations were excluded.
Thus, only 222 out of 782 patients were selected from a specialized hand unit that manages orthopaedic care or surgery for local community that gives specialized services for South-western Ontario region.The patients were assessed at 2, 3, 6, and 12 moths after the injury. Potential Predictors Patients’ background information, injury, and work were deemed as potential predictors. Personal information includes patients’ age, sex, work injury compensation, smoking status, and education. In a 3-point scale, the work factor based on the demand for the use of the hands was categorized as minimal, moderate, or intensive while the energy of injury was described as fall from level ground, fall from height or high impact.
Fracture displacements, measured through radiographs, were classified based on Arbeitsgemeinschaft fur Osteosynthesefragen (AO) system as non-articular, partial articular, or fully articular. Only 101 radiographs were only successfully secured due to the lost of or failure of orthopaedic centres to issue radiographic films. Radiographic measurements of radial shortening, dorsal angulation and radial inclination, intra-articular step-off, and involvement of the distal radio-ulnar joint were made based on standardized method. Measures of self-reported pain, disability, and general healthMeasurement of self-reported pain or disability was done by using a joint-speci? c, regional, and generic health scales. The Patient-Rated Wrist Evaluation (PRWE), a 15-item scale constituting 5 items for pain, 6 items speci? c activities, and 4 items for usual activities, was used by the patients for their self-evaluation of their pain or disability with respect to their wrists. Meanwhile the validated Disabilities of the Arm, Shoulder, Hand (DASH), a 30-item scale, was used for patients to assess pain and disability of their upper extremity using likert-scaled questions.
Nevertheless, the SF-36, a 36-item general health questionnaire was used to estimate patients’ status on eight domains of overall health. Measures of physical capability of the wrist The determination of isolated, wrist-related, physical impairments was done through grip strength, ROM, and dexterity measurements. For measurement of strength and motion the NK Hand Assessment system was used while grip strength was assessed through the standardized positioning recommended by the American Society of Hand Therapists (ASHT) and the second handle position of the NK digit-grip device.Meanwhile, ROM which includes pronation, supination, ? exion, extension, radial and ulnar deviation, was measured using the NK Hand Assessment System electrogoniometer. Also, the Jebson’s Hand Function Test was used to test Dexterity.
Finally, the ROM, grip strength, and dexterity measures were combined to get wrist impairment score (WIS). Follow-up protocol After emergency treatment, patients were directed to orthopaedic clinic for definitive management within 0-3 days. Within the first post-fracture week, visitation has been undertaken to gather data for self-report forms, demographic data, and work nature information.The same things were done for the rest of follow-up appointments. In addition, patients were tested for wrist-related physical impairment for 2 and 3 months until the healing of the fracture.
All these evaluative measures were done through an independent evaluator adept in clinical assessments. Analysis Quality checks and descriptive statistics were utilized in the primary analysis of data. For quantitative data pairs, Pearson correlations were computed while Spearman rho for ordinal pairs. In addition, Chi-square was utilized to predict the relationship between specific categorical variables and time loss from work.Through stepwise multiple linear regression, subsequent work-loss models were developed. These models were analyzed based on their predictive value with respect to the loss of time from work in terms of physical impairments, self-reported disability, and radiographic fracture 3 months after the injury.
Also, self-report impact on work outcomes was determined by classifying work-loss into 4 clinical categories.Results Although most patients acquired low-energy injury from level ground fall, significantly high rate of intra-articular fractures was observed. It was found at 95% confidence level that 9. weeks was the average loss of time from work. Also, 21% of the patients lost >36 weeks. In addition, based on Chi-square results, patients with higher job demands, undergone surgery or physiotherapy, and with less educational attainment tend to incur significant work-loss.
Meanwhile, as revealed by bivariate correlations through Pearson and Spearman, poorer self-reported disability or general health, and greater physical impairment measures denoted longer work-off time. On the other hand, disability measures, except general health measures, have high correlations with return to work at 2 and 3 months after the injury.Nevertheless, grip strength and motion have high correlations with loss of time from work while low correlations were observed for both dexterity test and fracture displacement. The total WIS was highly correlated to return to work than its individual components showing that better physical performance results to a faster return to work. It was also observed that statistical models that include patients’ variables, injury variables, and self-reported disability scales accounted the 27% of return to work variation based on occupational demands and the baseline DASH score.DASH score, occupational demand, and energy of fracture at 3 moths, on the other hand, accounted the 40% return to work variation.
On the contrary, when the self-report measures were removed from the model, 29% of the return to work-times variation was explained by the wrist impairment score and occupational demands. Then, when both self-reported disability and physical impairment measures were excluded, a model with radiographic variables and job demands accounted the work-loss variation.Moreover, when all measures were included, the DASH score was the most predicative measure accounting for 29% loss of time variation. Patients with poorer return to work outcomes have higher self-report scores at each evaluation. Meanwhile, Post-hoc testing revealed that the no-work group, the prolonged-work loss group, and the intermediary work outcomes were different from each other on the ground of self-reported pain and disability. This trend was also faintly observed with physical impairment measures.
DiscussionThe study conducted by MacDermid, Roth, and McMurtry (2007) revealed high variation between return to work and DRF relationships. As a proof, 20% of their subjects reported no loss of time from work and minority did not return to work even after a year. Differences between their data and the 2005 report of U. S. Bureau of Labor statistics were also noted.
For instance, the labor statistics did not consider work-loss in their report while in their study aside from patients without work-loss, 20% of patients incurred minimal work-loss, either 1 or 2 days and patients with prolonged work disability were observed.Additionally, the median loss of time from work in their study was 8 weeks compared to 27 days or just over 5 weeks in the labor statistics. They attributed these differences in the monitoring of return to work and nature of disability agreement practices between Canada and U. S. work places.
MacDermid, Roth, and McMurtry (2007) found that only one-quarter of return to work variation is accounted by baseline clinical information. Even though, they failed to find post-reduction measures as significant predictor, this is not an indication that adequate reduction is insignificant in fracture management.Nevertheless, low correlations between radiographic features and work-loss suggest that in clinical decision making on fracture management, self-report score should be integrated with radiographic measures. The fact that initial fracture displacement was related to work-loss, the initial features then of the injury are important variables. MacDermid, Roth, and McMurtry (2007) were unable to describe these features and stated that quantitative measures for these are unavailable.
Thus, a better quantification of soft tissue injury may enhance future clinical studies.Even though self-reported disability was found to be a better predictor of work-off time, there was a component in the capability scores which indicated aspects of physical health that was not reflected by self-report measures. While poorer reported function and measured physical status were correlated with longer work-off time, a statistical model of several studies associated older age with shorter time-off work when other variables were controlled. Thus, this should be a subject for further studies to find out possible issues such as attitudes towards work or accommodations for older employees (MacDermid, Roth, and McMurtry, 2007).
In the mean time, both education and compensation were found to be related to time-off work through bivariate correlations but were excluded in multivariate models with occupational demand variable. This suggested that worker’s compensation and education, to a certain extent, were cofounders while occupational demand was the underlying predictor (MacDermid, Roth, and McMurtry, 2007). MacDermid, Roth, and McMurtry (200) also suggested for a more precise classification of upper extremity functions to investigate further the impact of job demands on work-loss.MacDermid, Roth, and McMurtry (2007) recognized the complexity of return to work statistical models and their multiple variable regressions employed failed to develop a comprehensive model.
Although the potential predictors they evaluated have efficacy on work-loss prediction, patients and their injury were only taken into consideration but failed to evaluate work organization and work place disability management. Also, environmental, cultural, psychosocial or family variables were not taken into account.
