Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Semin Arthritis Rheum. Author manuscript; available in PMC 2012 August 1.
Published in final edited form as:
PMCID: PMC3153604

Determinants of Work Disability in Patients with Systemic Sclerosis: A Longitudinal Study of the GENISOS Cohort

Roozbeh Sharif, MD,1 Maureen D. Mayes, MD, MPH,1 Perry M. Nicassio, PhD,2 Emilio B. Gonzalez, MD,3 Hilda Draeger, MD,4 Terry A. McNearney, MD,3 Rosa M Estrada-Y-Martin, MD, MSc, FCCP,5 Deepthi K. Nair, MS,1 John D. Reveille, MD,1 Frank C. Arnett, MD,1 and Shervin Assassi, MD, MS1, for the GENISOS Study Group



To determine the prevalence, correlates, and predictors of work disability (WD) in the Genetics versus ENvironment In Scleroderma Outcome Study (GENISOS). We hypothesized that WD in systemic sclerosis (SSc) is a function of demographic, clinical, and psychosocial factors.


Patients enrolled in the GENISOS cohort were subdivided in 3 groups: work disabled, working, and retired or homemakers. The latter group (n=29) was excluded from further analysis. We used logistic regression analysis with a forward hierarchical variable selection strategy to investigate the independent correlates of WD at enrollment. Cox regression proportional Hazard’s model with a similar variable selection strategy was utilized to determine the predictors of WD in those working at enrollment.


Overall, 284 patients with mean age of 48.7 years and disease duration of 2.5 (±1.6) years were enrolled into the GENISOS cohort, consisting of 83.5% female, 46.8% Caucasian, 28.9% Hispanic, and 20.4% African American. Patients were longitudinally followed for 3.9 (±3.6) years in 1438 study visits. At enrollment, 124 patients (43.7%) were work disabled whereas 131 (46.1%) were working. Lower education (p<0.001), higher Medsger Lung Severity Index (p=0.012), higher Fatigue Severity Score (FSS) (p=0.008), and less social support (p<0.001) correlated independently with WD. Of those working at baseline, 35 (26.7%) eventually developed WD. Non-Caucasian ethnicity (p=0.038), lower DLCO %predicted value (p=0.038), and higher FSS (p=0.009) at enrollment independently predicted WD on follow-up visits.


Demographic, clinical, and psychosocial factors correlate with WD cross-sectionally and predict WD longitudinally in the patients with SSc.

Keywords: Work disability, Systemic Sclerosis, Medsger Lung Severity Index, ISEL, SF-36, Fatigue


Systemic sclerosis (SSc, scleroderma) is a chronic, multisystem, connective tissue disease of unknown etiology characterized by inflammation, vasculopathy, and widespread fibrosis of skin and internal organs (1). It is associated with substantial morbidity and mortality (2;3). Advances in diagnosis and treatment have improved the prognosis of SSc in recent years (4;5). Nevertheless, this disease continues to have a detrimental impact on patients’ personal and professional lives (612).

Previous studies by the National Arthritis Data Work Group and other investigators have shown a substantial increase in the economic impact of musculoskeletal and rheumatic conditions over the last decades in the United States. This has increased from $4 billion in 1963 (13) to $353 billion in 2005 (14), with a major proportion attributable to indirect costs of work disability and wage loss (13;14). Work disability (WD) continues to be a major burden on individuals affected from rheumatic conditions. This may become even more prominent in the future, as the working years and productivity lost due to WD will increase with the expected increase in the retirement age (15).

Work disability has been extensively studied in other rheumatic conditions such as systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and ankylosing spondylitis (AS) (1620). Although a few studies have addressed WD and its correlates in patients with SSc in other countries (711;21), there are no studies investigating this issue in the United States. Moreover, there are no published prospective studies reports investigating the long-term predictors of WD in SSc patients.

In the current study, we hypothesized that WD in SSc is a function of demographic, clinical, and psychosocial factors. We investigated the prevalence of work disability in a multiethnic cohort of early SSc patients and assessed the factors associated with WD, at early stages of disease. Then, we longitudinally examined the predictors of WD in the patients who were working at enrollment.


The Genetics versus ENvironment In Scleroderma Outcome Study (GENISOS) is a multicenter prospective study of early SSc patients. It is conducted at three sites: the University of Texas Health Science Center at Houston (UTHSC-H), the University of Texas Medical Branch at Galveston (UTMB), and the University of Texas Health Science Center at San Antonio (UTHSC-SA). Study recruitment started in January 1998 and is ongoing.

Study subjects

Details of patient selection and recruitment have been previously described (3;2225). Patients who fulfilled the following inclusion criteria were enrolled: 1) age ≥ 18 years; 2) diagnosis according to the American College of Rheumatology (formerly the American Rheumatism Association) criteria for SSc (26); 3) disease onset (defined as onset of the first non-Raynaud symptom) within five years of enrollment; and 4) defined ethnicity with all four grandparents from the same ethnic group. Patients who had SSc-like illnesses associated with environmental, ingested, or injected agents were excluded from the study. All the 284 patients enrolled at the time of analysis were included in this study. The institutional review boards of all participating sites approved the study and written informed consent was obtained from all subjects.

Data collection and questionnaires

As previously described (3;2225), the demographic, clinical, laboratory tests, chest radiographs, pulmonary function tests, autoantibody profile, patient-reported clinical outcomes as well as behavioral, psychosocial and functional data were obtained at the baseline visit and then on subsequent semi-annual visits.

Outcome variable

The primary outcome was the occupational status. We have annually gathered this information in a questionnaire designed according to the definitions in the Dictionary of Occupational Titles by the United States Department of Commerce (27;28). The questionnaire collected the employment status (working full-time or part-time, unemployed, retired, disabled, student, or homemaker), job description, and the reason of the current job status if anything other than working full-time. We categorized the patients into three groups (figure 1). Group A included patients working full-time, part-time for any reasons other than health problems, and full-time students. Full-time work was defined as working ≥ 40 hours per week. Group B were work-disabled individuals, including those who were early retired, unemployed, or part-time workers because of health problems. Furthermore, the third group of patients (Group C) consisted of homemakers, retired and unemployed individuals for reasons other than health problems. We excluded Group C from further analysis as we could not determine whether their occupational status was attributable to SSc.

Figure 1
The employment status of the patients at enrollment and follow-up visits

Independent variables

Independent variables from the following domains were investigated: demographic, clinical, patient-reported clinical and psychosocial variables.

Demographic information

Age at enrollment, gender, ethnicity, marital status, educational level, and smoking status were recorded. Marital status data were dichotomized as being married or in a marriage-like relationship versus being single, divorced, separated, or never married. We also dichotomized the educational level as holding an associate degree and above versus high school diploma and below.

Clinical manifestations

All patients were examined by the study investigators at the three study sites. Disease characteristics included disease type based on the extent of skin involvement (29), duration, and antibody profile. We calculated the disease duration with two different methods: from the first non-Raynaud’s phenomenon symptom attributable to SSc and from the first symptom attributable to SSc (Raynaud’s or non- Raynaud’s phenomenon symptoms). History and physical examination findings, Modified Rodnan Skin Score (MRSS) (30), Medsger Severity Index (SI) (31), and history of corticosteroid and cyclophosphamide use were recorded. Presence of digital ulcers was determined based on the investigators’ clinical assessment. SSc cardiac involvement was defined as having clinically significant arrhythmia or ejection fraction ≤ 40%.

As previously described (22), all pulmonary function tests were reviewed by a pulmonologist and studies that did not fulfill the American Thoracic Society/European Respiratory Society (ATS/ERS) were excluded (32;33). Myositis was diagnosed if the patient had proximal muscle weakness with at least one of the following: elevated levels of muscle enzymes, myopathic changes on electromyography, and/or a characteristic muscle biopsy. A decrease in range of motion > 25% in at least one joint axis was defined as joint contracture. Small joint contracture was determined based on involvement of metacarpophalangeal and/or proximal interphalangeal finger joints while large joint contracture was defined as involvement of elbow and/or knee joints. Arthritis was defined as presence of joint swelling and tenderness on physical examination not attributable to osteoarthritis, crystalline arthropathy, or trauma. Abnormal renal function was defined as serum creatinine ≥ 1.5 mg/dl. Furthermore, we calculated the number of co-morbid conditions in each patient based on the patients’ history of cardiovascular disease, hypertension, diabetes mellitus, stroke, lung disease, malignancy, kidney disease, SLE, RA, thyroid disease, osteoarthritis, fibromyalgia, peptic ulcer disease, obesity (body mass index ≥ 30), depression, and other neuropsychiatric disorders.

Patient-reported clinical outcomes

pain and shortness of breath (SOB) was captured on a visual analogue scale ranging from zero to ten. Higher numbers indicated more severe pain and dyspnea.

Psychosocial variables

Six psychosocial variables were measured: fatigue, social support, illness behavior, learned helplessness, self-perceived physical and mental health, as well as, functional status. Fatigue was assessed with Fatigue Severity Scale (FSS), a validated 29-item questionnaire (34). Answers to each question were rated from one to seven (no fatigue to very severe fatigue impaired function). Higher scores indicated more fatigue. Social support was evaluated by using the Interpersonal Support Evaluation List (ISEL) (35). ISEL is a 40-item validated instrument comprised of four scales ranging from zero to 3 on each question and a summary measure. Higher scores indicated better social support. Coping with illness was assessed with the Illness Behavior Questionnaire (IBQ) (36). IBQ is a 62-item instrument comprised of seven scales measuring generalized hypochondriasis, disease conviction, denial, affective disturbance, affective inhibition, psychosocial versus somatic, and irritability plus a summary measure with a range of 0–35. Higher IBQ scores indicate more inappropriate illness behaviors. Learned helplessness was ascertained with the Arthritis Helplessness Index (AHI), a 15-item standardized questionnaire used in other chronic illnesses (37). Higher AHI scores indicate more perceived helplessness concerning the ability of the patient to impact on their illness. We used the medical outcome Study Short Form-36 (SF-36) to assess self-perceived mental and physical functioning (3840). The SF-36 compromised of eight health related quality of life measures: physical functioning, role-physical, bodily pain, general health perceptions, vitality, social functioning, and role limitations due to emotional problems and mental health were individually scored. These scores were then summarized into the mental component (MCS) and physical component (PCS) summary scores, utilizing norm-based scoring based on a general population sample (mean of 50 and standard deviation of 10). All scores are rated so that higher values indicate higher levels of perceived functioning (range 0–100). The SF-36 has been previously validated in studies with SSc patients (39;40). In addition, the modified Health Assessment Questionnaire Disability Index (HAQ) was used for assessing functional status of the patients (41). Questions were graded from 0–3, based on self-perceived ability to “perform in a normal manner” to being “unable to perform”. The higher HAQ score indicated more physical disability.

Similar to our previous study (24), all psychosocial instruments demonstrated adequate internal consistency reliability. Social support measured by the ISEL questionnaire had the Cronbach’s Alphas of 0.87. IBQ and AHI showed the Cronbach’s Alphas of 0.85 and 0.70. SF-36 had the Cronbach’s Alphas of 0.91 and 0.88 for physical and mental components, respectively. The Cronbach’s Alphas of HAQ and fatigue scores were also adequate at 0.91 and 0.90, respectively.

Statistical analysis

Patients who were homemakers, retired, or unemployed for reasons other than health problems at baseline (Group C) were excluded from analysis (n=29). In the cross-sectional analysis, we examined the association of the independent variables with disability status at the baseline visit (Group A versus Group B). The univariate analysis was conducted with Student’s t tests for continuous and the χ2 test for categorical variables.

We next conducted a hierarchical modeling with successive conceptual blocks to evaluate our hypothesis that demographic, clinical and psychosocial variables independently contribute to WD in SSc. The independent variables with a p<0.1 in the univariate analysis were added into the analysis in the following successive conceptual blocks: demographic information, clinical manifestations, self-reported clinical outcomes (pain and dyspnea), and psychosocial variables. The model fit was assessed after addition of each block by using Bayesian Information Criterion (BIC). Lower BIC values indicate better model fit. BIC values are interpreted as a weak evidence for better fit with a difference of 0–2, a positive evidence with a difference of 2–6, a strong evidence with a difference of 6–10, and very strong evidence for better fit with a difference of >10. This approach tested the proposition that each conceptual block contributes independently to WD and is not merely a mediator of previous variables. HAQ is a direct measure of disability (41) and can mediate the effect of other factors on WD. In addition, SF-36 PCS as a measure of overall perceived physical health has overlap with clinical and patient-reported clinical outcomes and it might be partially an effect mediator of the clinical domain. Therefore, we exclude HAQ and SF-36 PCS in the blockwise hierarchical modeling and final multivariate models.

The final multivariate model at the cross-sectional level was computed utilizing a forward hierarchical variable selection strategy. This approach was chosen to decrease the effect of multi-colinearity in our analysis. We initially entered all variables that showed an association with p <0.1 in the univariate analysis. Subsequently, the number of independent variables was reduced to those variables that changed the R square of the entire model by 1%. We did not include income level and medical insurance as independent variables in our models because they were a consequence rather than a risk factor for WD.

We next included all patients who were working or full-time students at enrollment (Group A) in the longitudinal study. For this component, time to event (occurrence of disability) analysis was conducted utilizing a Cox proportional hazards regression model. The date of enrollment into the study was used as the starting point. An initial univariate analysis examining the relationship of independent variables with occurrence of disability after enrollment was conducted. The final multivariate model was computed using the hierarchical forward variable selection strategy as described above. All the statistical analyses were performed with NCSS 2007 (NCSS, Kaysville, UT) and STATA 11 (StataCorp, College Station, TX). The hypothesis testing was 2-sided with a p ≤ 0.05 significance level.


Population characteristics and prevalence of WD at baseline visit

All 284 patients in the GENISOS were included in the study. Patients were followed for a mean (±SD) time of 3.9 (3.6); up to 12 years, in 1438 visits. The mean age of patients at enrollment was 48.7 (±13.2) years and 237 (83.5%) of cohort were female. The patients were from the following ethnic groups: 133 (46.8%) Caucasian, 83 (28.9%) Hispanic, 58 (20.4%) African American, and 10 (3.5%) from other ethnic background. Diffuse skin involvement was reported in 162 (57.0%) of patients. Average disease duration at enrollment was 2.5 (±1.6) years (table 1).

Table 1
Socio-demographic, disease characteristics, and employment status of the patients enrolled in the GENISOS cohort

At enrollment, 131 patients (46.1%) were in Group A, including 111 (39.1%) full-time workers, 14 (4.9%) working part-time for reasons other than health problems, and 6 full-time students (figure 1). Group B (n=124; 43.7%) consisted of 83 (29.2%) patients self-reported as work disabled and 41 (14.3%) part-time workers, unemployed, or retired earlier due to health problems. The remainder of patients were in Group C (n=29, 10.2%).

We included the patients in Group A (working at baseline) for longitudinal study. Out of 131 patient, 96 (73.3%) were still working while 35 (26.7%) became disabled after, 4.4 (±3.8) years of follow up, in 740 visits (figure 1).

Univariate analysis of WD correlates at baseline visit

In the univariate analysis, the following demographic variables were associated with WD: older age, non-Caucasian ethnicity, being single, separated, or divorced, lower educational background (holding high school diploma or lower degrees), while there was no association with gender.

Among clinical manifestations; cardiac involvement, lung disease (FVC and DLCO % predicted values), small joint contracture, number of co-morbid conditions, and Medsger Gastrointestinal (GI) Tract and Lung Severity Indices were significant correlates of WD. However, disease duration, MRSS, digital ulcers, GI symptoms like dysphagia and diarrhea, body mass index, abnormal renal function, arthritis, large joint contracture, myositis, elevated creatinine kinase, and serologic findings were not associated with WD. Moreover, recent treatment with corticosteroid and history of ever cyclophosphamide therapy did not correlate with WD (table 2 and supplement 1). Patient-reported clinical outcomes, VAS pain and shortness of breath, were both significantly higher in those with WD.

Table 2
Univariate analysis of work disability correlates at enrollment

Patients with WD had more maladaptive illness behavior (higher IBQ score), more fatigue (higher score on Fatigue Severity Scale), higher learned helplessness (higher score on AHI) and HAQ scores. Furthermore, they reported less interpersonal support (lower ISEL score) and health related quality of life (lower SF-36- mental and physical components) (table 2 and supplement 1).

Independent correlates of WD at enrollment – Successive conceptual blocks

To determine the independent correlates of work disability in the patients with SSc; we successively added the following conceptual blocks into the multivariate multiple: 1) different demographic; 2) clinical manifestations; 3) patient-reported clinical outcome; and 4) psychosocial variables into conceptual blocks. The demographic characteristics (table 3, Model 1) provided the Bayesian information criterion (BIC) of 330 (p <0.001 for Model 1). In Model 2, we added the clinical manifestations to demographic variables resulting in an improved BIC of 303, which demonstrated a strong evidence for better model fit (delta=27, p<0.001 for model 2). In Model 3, the patients reported clinical outcomes (pain and dyspnea) were added to the previous two blocks. Model 3 had a BIC value of 295 which is consistent with positive evidence for better model fit to the observed data (delta=8, p<0.001 for Model 3).

Table 3
Block-wise modeling of demographic, clinical, patient-reported clinical outcomes, and psychosocial correlates of work[RS1]

In Model 4, the psychosocial factors were added to the demographic and clinical manifestation blocks. Model 4 had the lowest BIC (290) and showed a strong evidence for better model fit compared to Model 2 (delta= 13) and positive evidence for better model fit to Model 3 (delta=5). This blockwise hierarchical modeling strategy indicated that each successive block contributed independently to WD and was not merely a mediator of previous blocks while it cannot be ruled out that the successive models (e.g.: patient-reported clinical outcomes or psychosocial factors) are partially effect mediators of previous conceptual blocks.

Independent correlates of WD at enrollment – Final model

All relevant demographic, clinical, and psychosocial variables were included in the final model (table 4). The forward hierarchical model showed that educational level below associate’s degree (p<0.001), higher Medsger Lung Severity Index (p=0.004), higher Fatigue Severity Score (p<0.001), and less social support (p<0.001) were independently associated with WD, at baseline. For example, those patients who had an associate degree or above were 78% less likely to be WD at the baseline visit while with each unit decrease in the ISEL score (less social support), the patients were 2.1 times more likely to be WD.

Table 4
Multivariate analysis of demographic, clinical, patient-reported clinical outcomes, and psychosocial correlates of work disability at enrollment, based on forward hierarchical models

Univariate predictors of WD on follow up visits

We next examined predictors of developing WD in patients belonging to Group A (figure 1). Small joint contractures (p=0.001), lower DLCO % predicted value (p=0.017) and Medsger’s Lung Severity Index (p=0.049) at enrollment were the significant clinical predictors of long-term work disability. Furthermore, all six psychosocial measures were significant predictors of WD in the Cox regression univariate analysis. Higher IBQ, Learned helplessness, FSS, and HAQ scores (p=0.001, p=0.041, p= 0.006, p=0.002, respectively) and lower ISEL, SF-36 PCS and MCS scores (p=0.014, p=0.002, and p=0.041) were predictor of WD. However, serologic findings, MRSS, abnormal renal function, cardiac involvement, number of co-morbid conditions, and history of treatment with corticosteroid or cyclophosphamide did not influence on long-term the work disability (supplement 2).

Independent predictors of WD in the multivariate model

In the multivariate model following a forward hierarchical variable selection strategy, non-Caucasian ethnicity (p=0.038), lower DLCO (p=0.038), and higher score on Fatigue Severity Scale (p=0.009) at baseline were the only independent predictors of WD on follow up visits (table 5). Caucasians had 54 % less chance of becoming work disabled compared to non-Caucasians (p=0.038). Similarly, patients with higher DLco% predicted at the baseline visit were less likely to develop WD on follow-up visits. Moreover, for each point increase in FSS score at the baseline visit, the patient had 96% higher risk of developing WD (p=0.009). This final model supports our initial hypothesis that demographic, clinical and psychosocial factors contribute to WD in SSc.

Table 5
Demographic, clinical, and psychosocial predictors of work disability based on multivariate Cox regression modeling


Professional life plays a crucial role in patients’ overall quality of life. Herein, we demonstrate that demographic, clinical, and psychosocial factors contribute to SSc-WD. To our knowledge, this is the first report on impact of SSc on the employment status in the United States. Moreover, it is the first longitudinal study examining the predictors of WD in patients with early SSc.

In addition to comprehensive demographic and clinical data, we also investigated the cross sectional and longitudinal influence of social support (ISEL), learned helplessness (AHI), coping skills (IBQ), and other previously reported psychometric correlates of WD in SSc (fatigue, HAQ and SF-36) (711;21). We hypothesized that demographic, clinical, patient-reported outcomes, and psychosocial factors determine the occupational status in an individual with SSc.

Confirming previous studies (711), we showed a high prevalence of WD in early SSc. However, the observed prevalence was higher than reports from the well-characterized Canadian Scleroderma Registry Group (46% versus 21%) (7). This discrepancy could be explained by differences in the investigated study populations. Our patients had a higher frequency of diffuse cutaneous involvement (57% versus 48%). Furthermore, GENISOS was an incident cohort with mean disease duration of 2.5 years at enrollment, which is more likely to include patients with severe disease than a prevalent cohort. Moreover, the majority of the study population was Caucasian (90%) in the Canadian Scleroderma Registry whereas GENISOS included 53 % Non-Caucasian patients; Non-Caucasians had a higher mortality rate (3) and were more likely to be disabled (table 2). On the other hand, the prevalence of WD in GENISOS was lower than another study of 72 SSc patients in France (8). Same group later reported a WD prevalence of 41% among patients with digital ulcers (9). Differing health care policies of the investigated countries might also contribute to the observed variations in prevalence rate of WD (16). Nevertheless, the prevalence of work disability in SSc is substantially higher than other common rheumatic conditions (10;1620).

In agreement with previous studies, demographic variables, specifically lower educational level (7;11), lung involvement (9;11), fatigue severity (7;11), and social support were independent correlates of WD at the cross sectional level in our study. Patients with lower educational level are more likely to hold occupations that are physically demanding. Therefore, its correlation with WD is not surprising. Similar demographic variables were correlates of WD in patients with early RA (42) and SLE (43). Fatigue also correlated with WD in previous studies of patients with SSc (21).

Confirming a recent report by Sandqvist et al., social support was an independent correlate of WD in the GENISOS cohort (21). Previous studies have shown the impact of social support on disease activity, self-perceived quality of life, and occupational status in the patients with SLE (17;44;45). Furthermore, a national longitudinal study of the overall population in the United States demonstrated that social support is an independent predictor of overall functional health (46). These further emphasize on the pivotal role of high quality social relationships with family and friends on the patient’s work ability. Because of its modifiable nature, the independent correlation between social support and WD suggests the necessity of multifaceted psychosocial interventions to enhance the working abilities among the patients with SSc.

After follow up of 4.4 years, almost one in four patients working at enrollment (Group A) became work disabled. Using Cox proportional hazards regression model, non-Caucasian ethnicity, lung involvement (DLCO % predicted), and higher score on Fatigue Severity Scale at baseline were the independent predictors of WD in the longitudinal study.

We have previously shown that non-Caucasian patients have higher mortality in the GENISOS cohort (3), indicating an overall less favorable prognosis (3;47;48). The predictive significance of non-Caucasian ethnicity for development of WD might be secondary to unmeasured demographic, clinical, and psychosocial factors. Health care factors leading to disparities in access to care might also be important (49). Non-Caucasian ethnicity was also predictive of WD development in other rheumatic diseases like SLE (50).

Scleroderma pulmonary involvement, including both interstitial lung disease and pulmonary arterial hypertension, is the primary cause of SSc- related death (51). Confirming previous results, we have shown that pulmonary involvement is also a predictor of mortality in the GENISOS cohort (3). Therefore, it is not surprising that pulmonary involvement was associated with WD at baseline and longitudinally in the current study.

In the Scleroderma Lung Study, treatment with oral cyclophosphamide led to improvement in health-related quality of life measures along with better pulmonary function (52). In the current study, treatment with cyclophosphamide had no significant relationship to WD. However, differences in baseline characteristics between treated and untreated groups might have blunted the treatment effect. Randomized controlled studies with long-term follow up are more appropriate to determine the effect of various medications on WD.

Fatigue is one of the most common and overlooked complaints in patients with SSc (53). Our results confirm previous cross-sectional studies indicating that fatigue is an important contributor to WD (21;53;54). In our study, fatigue was not only correlate (cross-sectional) but also a predictor (longitudinal) of WD. Fatigue might be partially a mediator of the effects of demographic and clinical and factors on WD. However, our analysis with successive conceptual blocks demonstrates that psychosocial factors including fatigue contribute independently to WD and are not merely mediators of demographic or clinical factors. Our findings further underscore the need for future studies focusing on pathophysiology and treatment of fatigue in SSc.

In agreement with a previous study (21), small joint contracture was associated with WD at the univariate level. However, it was neither an independent correlate nor predictor of WD in the multivariate model. It is possible that a dichotomized variable as defined in our cohort does not capture the true severity of small joint contracture.

The GENISOS cohort includes a large number of early SSc patients from different ethnic backgrounds. The current study demonstrates the first assessment of prevalence, correlates, and predictors of WD in SSc that includes a sizable Hispanic and African American population. This enhances the generalizability of our findings. Furthermore, the comprehensive array of demographic, clinical, and psychosocial variables decreases the likelihood of missing important correlates or predictors.

However, the current study had some limitations. The majority of study subjects were recruited from tertiary medical centers, which can introduce referral bias (toward more severe cases). Detailed information on job descriptions and demands was not available in the GENISOS cohort. Therefore, we could not evaluate the impact of job demands on WD. Moreover, we could not examine change in the professional level i.e. downgrading to another job due to SSc in the current study. We did not use a validated psychometric instrument to assess depression in the GENISOS cohort. However, other psychometric instruments for assessment of mental health such as SF-36 MCS were utilized in the current study.

In conclusion, work disability is a prominent problem in patients with SSc. More than 40% of patients were already work disabled at early stages of the disease. After 4.4 years, more than one forth of those working at enrollment eventually became work disabled. Work disability is a function of demographic background, clinical and psychosocial factors, cross-sectionally and longitudinally. These findings underscore the need for a multi-disciplinary approach to treatment of SSc patients.

Supplementary Material


This study was funded by the National Institute of Health (NIH) Center of Research Translation P50AR054144 (Arnett and Mayes); NIH-KL2RR024149-04 (Assassi); NIH -5T32-AR052283-03(Reveille); University Clinic Research Center Grants: M01-RR00073 (UTMB) and M01-RR01346 (UT-HSC-SA); NIH Clinical and Translational Sciences Award UL1-RR024148 and TL1 RR024147 from the National Center for Research Resources. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Center for Research Resources or the National Institutes of Health. The authors thank Alison Z. Brown, Samuel Theodore and Barbara A. Boyle for their assistance in data collection.

Grant Support: This study was funded by the National Institute of Health (NIH) Center of Research Translation P50AR054144 (Arnett and Mayes); NIH-KL2RR024149-04 (Assassi); NIH -5T32-AR052283-03(Reveille); University Clinic Research Center Grants: M01-RR00073 (UTMB) and M01-RR01346 (UT-HSC-SA); NIH Clinical and Translational Sciences Award UL1-RR024148 and TL1 RR024147 from the National Center for Research Resources.


Departments/institution to which the work should be attributed: Division of Rheumatology and Immunogenetics, University of Texas Health Science Center at Houston, TX; Division of Rheumatology, University of Texas Medical Branch at Galveston, TX; Division of Rheumatology, University of Texas Health Science Center at San Antonio, TX.

Disclaimer: The content of this study is solely the responsibility of authors and does not represent the official views of the funding institutions. The authors do not have any financial interests that could create a potential conflict of interest and have not received any financial support or other benefits from commercial sources for the work reported in the manuscript.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Reference List

1. Steen VD. Clinical manifestations of systemic sclerosis. Semin Cutan Med Surg. 1998 Mar;17(1):48–54. [PubMed]
2. Ioannidis JP, Vlachoyiannopoulos PG, Haidich AB, Medsger TA, Jr, Lucas M, Michet CJ, et al. Mortality in systemic sclerosis: an international meta-analysis of individual patient data. Am J Med. 2005 Jan;118(1):2–10. [PubMed]
3. Assassi S, Del JD, Sutter K, McNearney TA, Reveille JD, Karnavas A, et al. Clinical and genetic factors predictive of mortality in early systemic sclerosis. Arthritis Rheum. 2009 Oct 15;61(10):1403–11. [PMC free article] [PubMed]
4. Ferri C, Valentini G, Cozzi F, Sebastiani M, Michelassi C, La MG, et al. Systemic sclerosis: demographic, clinical, and serologic features and survival in 1,012 Italian patients. Medicine (Baltimore) 2002 Mar;81(2):139–53. [PubMed]
5. Scussel-Lonzetti L, Joyal F, Raynauld JP, Roussin A, Rich E, Goulet JR, et al. Predicting mortality in systemic sclerosis: analysis of a cohort of 309 French Canadian patients with emphasis on features at diagnosis as predictive factors for survival. Medicine (Baltimore) 2002 Mar;81(2):154–67. [PubMed]
6. Khanna D, Ahmed M, Furst DE, Ginsburg SS, Park GS, Hornung R, et al. Health values of patients with systemic sclerosis. Arthritis Rheum. 2007 Feb 15;57(1):86–93. [PubMed]
7. Hudson M, Steele R, Lu Y, Thombs BD, Baron M. Work disability in systemic sclerosis. J Rheumatol. 2009 Nov;36(11):2481–6. [PubMed]
8. Nguyen C, Poiraudeau S, Mestre-Stanislas C, Rannou F, Berezne A, Papelard A, et al. Employment status and socio-economic burden in systemic sclerosis: a cross-sectional survey. Rheumatology (Oxford) 2010 May;49(5):982–9. [PubMed]
9. Berezne A, Seror R, Morell-Dubois S, De MM, Fois E, Dzeing-Ella A, et al. Impact of systemic sclerosis on occupational and professional activity with attention to patients with digital ulcers. Arthritis Care Res (Hoboken ) 2010 Sep 7; [PubMed]
10. Ouimet JM, Pope JE, Gutmanis I, Koval J. Work Disability in Scleroderma is Greater than in Rheumatoid Arthritis and is Predicted by High HAQ Scores. Open Rheumatol J. 2008;2:44–52. [PMC free article] [PubMed]
11. Sandqvist G, Scheja A, Eklund M. Working ability in relation to disease severity, everyday occupations and well-being in women with limited systemic sclerosis. Rheumatology (Oxford) 2008 Nov;47(11):1708–11. [PubMed]
12. Suarez-Almazor ME, Kallen MA, Roundtree AK, Mayes M. Disease and symptom burden in systemic sclerosis: a patient perspective. J Rheumatol. 2007 Aug;34(8):1718–26. [PubMed]
13. Yelin EH, Felts WR. A summary of the impact of musculoskeletal conditions in the United States. Arthritis Rheum. 1990 May;33(5):750–5. [PubMed]
14. Cisternas MG, Murphy LB, Yelin EH, Foreman AJ, Pasta DJ, Helmick CG. Trends in medical care expenditures of US adults with arthritis and other rheumatic conditions 1997 to 2005. J Rheumatol. 2009 Nov;36(11):2531–8. [PubMed]
15. Allaire SH. Update on work disability in rheumatic diseases. Curr Opin Rheumatol. 2001 Mar;13(2):93–8. [PubMed]
16. Sokka T, Kautiainen H, Pincus T, Verstappen SM, Aggarwal A, Alten R, et al. Work disability remains a major problem in rheumatoid arthritis in the 2000s: data from 32 countries in the QUEST-RA study. Arthritis Res Ther. 2010;12(2):R42. [PMC free article] [PubMed]
17. Bertoli AM, Fernandez M, Alarcon GS, Vila LM, Reveille JD. Systemic lupus erythematosus in a multiethnic US cohort LUMINA (XLI): factors predictive of self-reported work disability. Ann Rheum Dis. 2007 Jan;66(1):12–7. [PMC free article] [PubMed]
18. Yazdany J, Yelin E. Health-related quality of life and employment among persons with systemic lupus erythematosus. Rheum Dis Clin North Am. 2010 Feb;36(1):15–32. vii. [PMC free article] [PubMed]
19. Odegard S, Finset A, Kvien TK, Mowinckel P, Uhlig T. Work disability in rheumatoid arthritis is predicted by physical and psychological health status: a 7-year study from the Oslo RA register. Scand J Rheumatol. 2005 Nov;34(6):441–7. [PubMed]
20. Ward MM, Reveille JD, Learch TJ, Davis JC, Jr, Weisman MH. Impact of ankylosing spondylitis on work and family life: comparisons with the US population. Arthritis Rheum. 2008 Apr 15;59(4):497–503. [PubMed]
21. Sandqvist G, Scheja A, Hesselstrand R. Pain, fatigue and hand function closely correlated to work ability and employment status in systemic sclerosis. Rheumatology (Oxford) 2010 Sep;49(9):1739–46. [PubMed]
22. Assassi S, Sharif R, Lasky RE, McNearney TA, Estrada YMR, Draeger H, et al. Predictors of interstitial lung disease in early systemic sclerosis: a prospective longitudinal study of the GENISOS cohort. Arthritis Res Ther. 2010 Sep 2;12(5):R166. [PMC free article] [PubMed]
23. McNearney TA, Reveille JD, Fischbach M, Friedman AW, Lisse JR, Goel N, et al. Pulmonary involvement in systemic sclerosis: associations with genetic, serologic, sociodemographic, and behavioral factors. Arthritis Rheum. 2007 Mar 15;57(2):318–26. [PubMed]
24. McNearney TA, Hunnicutt SE, Fischbach M, Friedman AW, Aguilar M, Ahn CW, et al. Perceived functioning has ethnic-specific associations in systemic sclerosis: another dimension of personalized medicine. J Rheumatol. 2009 Dec;36(12):2724–32. [PMC free article] [PubMed]
25. Reveille JD, Fischbach M, McNearney T, Friedman AW, Aguilar MB, Lisse J, et al. Systemic sclerosis in 3 US ethnic groups: a comparison of clinical, sociodemographic, serologic, and immunogenetic determinants. Semin Arthritis Rheum. 2001 Apr;30(5):332–46. [PubMed]
26. Preliminary criteria for the classification of systemic sclerosis (scleroderma) Subcommittee for scleroderma criteria of the American Rheumatism Association Diagnostic and Therapeutic Criteria Committee. Arthritis Rheum. 1980 May;23(5):581–90. [PubMed]
27. United States Department of Commerece. Dictionary of Occupational Titles. 4. Vol. 1. 1991.
28. United States Department of Commerece. Dictionary of Occupational Titles. 4. Vol. 2. 1991.
29. Leroy EC, Black C, Fleischmajer R, Jablonska S, Krieg T, Medsger TA, Jr, et al. Scleroderma (systemic sclerosis): classification, subsets and pathogenesis. J Rheumatol. 1988 Feb;15(2):202–5. [PubMed]
30. Clements P, Lachenbruch P, Siebold J, White B, Weiner S, Martin R, et al. Inter and intraobserver variability of total skin thickness score (modified Rodnan TSS) in systemic sclerosis. J Rheumatol. 1995 Jul;22(7):1281–5. [PubMed]
31. Medsger TA, Jr, Silman AJ, Steen VD, Black CM, Akesson A, Bacon PA, et al. A disease severity scale for systemic sclerosis: development and testing. J Rheumatol. 1999 Oct;26(10):2159–67. [PubMed]
32. Macintyre N, Crapo RO, Viegi G, Johnson DC, van der Grinten CP, Brusasco V, et al. Standardisation of the single-breath determination of carbon monoxide uptake in the lung. Eur Respir J. 2005 Oct;26(4):720–35. [PubMed]
33. Wanger J, Clausen JL, Coates A, Pedersen OF, Brusasco V, Burgos F, et al. Standardisation of the measurement of lung volumes. Eur Respir J. 2005 Sep;26(3):511–22. [PubMed]
34. Schwartz JE, Jandorf L, Krupp LB. The measurement of fatigue: a new instrument. J Psychosom Res. 1993 Oct;37(7):753–62. [PubMed]
35. Cohen S, Mermelstein R, Kamarck T, Hoberman H. Measuring the Functional Components of Social Support. In: Sarason I, Sarason B, editors. Social support: theory, research, and applications. Boston: Martinus Nijhoff; 1985. pp. 73–94.
36. Pilowsky L, Spence N. Illnes behaviour questionnaire (IBQ) In: Corcoran K, Fischer J, editors. Measures for clinical practice. 3. NY: Free Press; 1996. p. 182.
37. Engle EW, Callahan LF, Pincus T, Hochberg MC. Learned helplessness in systemic lupus erythematosus: analysis using the Rheumatology Attitudes Index. Arthritis Rheum. 1990 Feb;33(2):281–6. [PubMed]
38. Ware JE, Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care. 1992 Jun;30(6):473–83. [PubMed]
39. Danieli E, Airo P, Bettoni L, Cinquini M, Antonioli CM, Cavazzana I, et al. Health-related quality of life measured by the Short Form 36 (SF-36) in systemic sclerosis: correlations with indexes of disease activity and severity, disability, and depressive symptoms. Clin Rheumatol. 2005 Feb;24(1):48–54. [PubMed]
40. Khanna D, Furst DE, Clements PJ, Park GS, Hays RD, Yoon J, et al. Responsiveness of the SF-36 and the Health Assessment Questionnaire Disability Index in a systemic sclerosis clinical trial. J Rheumatol. 2005 May;32(5):832–40. [PubMed]
41. Poole JL, Williams CA, Bloch DA, Hollak B, Spitz P. Concurrent validity of the Health Assessment Questionnaire Disability Index in Scleroderma. Arthritis Care Res. 1995 Sep;8(3):189–93. [PubMed]
42. Eberhardt K, Larsson BM, Nived K, Lindqvist E. Work disability in rheumatoid arthritis-- development over 15 years and evaluation of predictive factors over time. J Rheumatol. 2007 Mar;34(3):481–7. [PubMed]
43. Scofield L, Reinlib L, Alarcon GS, Cooper GS. Employment and disability issues in systemic lupus erythematosus: a review. Arthritis Rheum. 2008 Oct 15;59(10):1475–9. [PubMed]
44. Sanchez ML, McGwin G, Jr, Duran S, Fernandez M, Reveille JD, Vila LM, et al. Factors predictive of overall health over the course of the disease in patients with systemic lupus erythematosus from the LUMINA cohort (LXII): use of the SF-6D. Clin Exp Rheumatol. 2009 Jan;27(1):67–71. [PubMed]
45. Alarcon GS, Calvo-Alen J, McGwin G, Jr, Uribe AG, Toloza SM, Roseman JM, et al. Systemic lupus erythematosus in a multiethnic cohort: LUMINA XXXV. Predictive factors of high disease activity over time. Ann Rheum Dis. 2006 Sep;65(9):1168–74. [PMC free article] [PubMed]
46. Lachman ME, Agrigoroaei S. Promoting functional health in midlife and old age: long-term protective effects of control beliefs, social support, and physical exercise. PLoS One. 2010;5(10):e13297. [PMC free article] [PubMed]
47. Nietert PJ, Silver RM, Mitchell HC, Shaftman SR, Tilley BC. Demographic and clinical factors associated with in-hospital death among patients with systemic sclerosis. J Rheumatol. 2005 Oct;32(10):1888–92. [PubMed]
48. Krishnan E, Furst DE. Systemic sclerosis mortality in the United States: 1979–1998. Eur J Epidemiol. 2005;20(10):855–61. [PubMed]
49. Lurie N, Dubowitz T. Health disparities and access to health. JAMA. 2007 Mar 14;297(10):1118–21. [PubMed]
50. Baker K, Pope J. Employment and work disability in systemic lupus erythematosus: a systematic review. Rheumatology (Oxford) 2009 Mar;48(3):281–4. [PubMed]
51. Steen VD, Medsger TA. Changes in causes of death in systemic sclerosis, 1972–2002. Ann Rheum Dis. 2007 Jul;66(7):940–4. [PMC free article] [PubMed]
52. Tashkin DP, Elashoff R, Clements PJ, Goldin J, Roth MD, Furst DE, et al. Cyclophosphamide versus placebo in scleroderma lung disease. N Engl J Med. 2006 Jun 22;354(25):2655–66. [PubMed]
53. Sandusky SB, McGuire L, Smith MT, Wigley FM, Haythornthwaite JA. Fatigue: an overlooked determinant of physical function in scleroderma. Rheumatology (Oxford) 2009 Feb;48(2):165–9. [PMC free article] [PubMed]
54. Thombs BD, Hudson M, Bassel M, Taillefer SS, Baron M. Sociodemographic, disease, and symptom correlates of fatigue in systemic sclerosis: evidence from a sample of 659 Canadian Scleroderma Research Group Registry patients. Arthritis Rheum. 2009 Jul 15;61(7):966–73. [PubMed]