Disability associated with work-related musculoskeletal disorders is an increasingly serious problem in society. Although the majority of workers with such disorders return to work quickly, a substantial number do not, and they account for the majority of associated costs. For example, a study of workers' compensation claims initiated in the United States in 1989 found that for low back pain claims, one quarter of the claimants accounted for 96% of the costs; similarly, for all claims, 25% accounted for 97% of total costs [1]. In a study of over 100,000 1992 low back claims, 10% of claimants were responsible for 86% of the total costs [2]. In 7% of the claims, disability was longer than one year, and these accounted for 75% of the costs and 84% of the total disability days. Similarly, about 7% of 1994 claims for work-related upper extremity musculoskeletal disorders had disability greater than one year, and these accounted for 60% of the costs and 75% of the total disability days [3].

The direct and indirect costs of chronic disability to the injured worker, his or her family, employers, and society are enormous. Data support the need to identify high-risk workers soon after an injury so that an effective intervention can be made within the first few months. For example, one study found that if a worker had not returned to work by 3 months, there was a 50% chance that he or she would not be working at 15 months [4]. With accurate predictive algorithms, limited resources can be targeted toward those most in need. Furthermore, the identification of factors that predict chronic disability may also shed light on why some workers develop chronic disability, and thus guide the development of intervention strategies that may prevent this process from occurring.

Secondary prevention programs are not necessary for the majority of injured workers, because most will recover quickly. Early accurate identification of injured workers at high risk for chronic disability and early intervention for these workers has the potential to have an enormous positive impact in terms of preventing the devastating financial and personal (e.g., quality of life) costs of disability. Currently, it is not possible to predict accurately which workers with recent injuries will go on to develop chronic disability.

We previously reviewed the literature on risk factors for chronic work disability [5]. The demographic factor most commonly found to be associated with chronic disability is older age [4,6-15]. With respect to biomedical and injury-related factors, more severe injuries [6,7,12,13,16] and greater pain and functional disability [15,17,18] have been demonstrated consistently to predict chronic disability. With respect to work-related factors, most studies have found occupation not to be associated significantly with chronic disability [6,8,9]. Two U.S. studies found that workers in small companies had poorer outcomes [7,8], but firm size was not predictive of cumulative compensated work absence in two Canadian studies [6]. Physically demanding work has been found to predict disability [19,20], and workplace offer of job accommodations/modifications has been found to be associated with shorter duration of disability [21]. With respect to psychosocial factors, worse outcomes have been found for workers who blamed work factors for their pain (as opposed to blaming themselves or other factors), rated their relations with coworkers as poor, or had low expectations of recovery [17,22,23].

Two recent studies that have examined predictors of disability in multivariate models warrant mention. In a study of 617 New Zealand workers in three occupational groups with claims for work-related back injuries, radiating leg pain, a high body mass index, moderate to high physical disability, psychological distress, and unavailability of light duty work predicted disability compensation status at three months [21]. In a study of Canadian workers with soft tissue injuries of the back, arms, or legs who were interviewed soon after injury and still receiving wage replacement benefits at four weeks, work disability duration was predicted by functional disability, change in pain from two to four weeks after injury, and workplace offers of job modifications [23]. For each factor, the relationship with disability duration was strongest over the period from the fourth to the twelfth week post-accident, and negligible for the remainder of the one-year follow-up period. Poor recovery expectations also predicted longer disability duration. The demographic and workplace factors examined did not predict disability duration. It is unknown whether similar results would be found in the U.S., in a setting with different health care insurance and workers' compensation systems.

The goal of this prospective, population-based study is to develop statistical models that optimally predict chronic work disability from data obtained from administrative databases and worker interviews soon after a work injury. Based on these models, we will develop a brief instrument that could be administered in medical or workers' compensation settings to screen injured workers for chronic disability risk. We are studying workers with back injuries and carpal tunnel syndrome (CTS) because these two musculoskeletal conditions are associated with higher rates of chronic disability as compared with other work injuries [7,9,13]. We will examine potential risk factors in five key risk domains: sociodemographic, biomedical/health care, work-related, administrative/legal, and psychosocial. We will develop a predictive model for our principal outcome measure (cumulative number of work disability days during the year after claim submission) and additional predictive models for the secondary outcomes (e.g., one-year work status, functional status), because the models may differ for different outcomes. We will determine whether worker self-report data add substantially to computerized administrative data in the predictive models, to assist in determining the potential cost-efficiency of collecting information from workers in a disability prediction and prevention program. Finally, we will develop a brief instrument that could be administered in medical or workers' compensation settings to screen injured workers for the key predictors of chronic disability.

We will examine variables in each of the five risk factor domains that have been found to predict chronic disability in prior research. Table 1 shows these variables, the predicted direction of their relationship to disability, and the source of information we will use (worker interview or workers' compensation administrative databases). Based on prior research, we expect that age, pain, and functional disability will be the strongest predictors of disability, but that other variables will add to the predictive model. Following is a list of specific factors in each risk domain that we hypothesize will be associated with chronic disability:

Sociodemographic: older age, lower socioeconomic status.

Biomedical/health care: greater severity of injury, greater number of comorbid medical conditions, higher body mass index, worse self-reported health prior to injury, radiating leg pain (back injury cases only), greater time from injury or appearance of symptoms to accessing medical care, greater baseline pain intensity and number of pain sites, worse baseline self-reported physical functioning, use of opiate or sedative/hypnotic medications for >4 weeks, greater number of sick leave days in the year prior to injury, and little treatment targeting reactivation and return to work.

Work-related: smaller firm size, loss of job within two months of claim allowance, no offer of a modified or light duty job, lack of communication between the attending doctor and the employer regarding return to work, greater physical demands of work, biomechanical factors of work, lower job satisfaction, and little confidence regarding return to work.

Administrative/legal: greater time from claim receipt to claim allowance by the workers' compensation system, employer protest in regard to claim validity, and worker retention of an attorney for the injury or claim.

Psychosocial: worse baseline mental health, alcohol abuse, greater fear-avoidance (perception that return to work might cause symptom worsening or re-injury), and greater pain-related catastrophizing (excessive focus on pain, magnification of the threat associated with pain, and feeling helpless to control pain).

The unique study environment in Washington State, in which two-thirds of workers are covered by a state workers' compensation fund, enables us to conduct this population-based study, to interview workers soon after musculoskeletal injury claim submission, and to link interview information with medical and claims information. These data will be used to identify the optimal combination of sociodemographic, biomedical, work-related, administrative/legal, and psychosocial risk factors for predicting chronic disability, and to develop a brief screening instrument that could be used early after injury to accurately identify workers at high risk for chronic disability.

None declared.

JAT, GF, DF-K, JDK, and TMW participated in the conceptualization and design of the study. JAT drafted the manuscript and JFL drafted the sample size and statistical analysis sections. All authors contributed to, and read and approved, the final manuscript.

The pre-publication history for this paper can be accessed here: