Session Information
Session Type: Abstract Submissions (ACR)
Background/Purpose: Different factors influence employment and work productivity in systemic lupus erythematosus (SLE) patients, including disease activity, cognitive impairment, comorbidities, education, and psychosocial factors. Previous studies have mostly been done in predominantly White SLE populations. This study assesses work loss and factors that impact change in work productivity over a 1-year period in patients with SLE who are primarily Black.
Methods: GOAL is a prospective cohort of SLE patients who provide patient-reported measures at least annually. It is derived primarily from the Georgia Lupus Registry, a population-based registry established in metropolitan Atlanta, Georgia. Data were collected in 2 Waves (2011-12 and 2012-13). We assessed demographic and disease factors associated with work loss over 1 year. Change in work productivity was measured in patients using the Work Productivity and Activity Impairment (WPAI) tool. Three measures were used to examine the change in WPAI: % of work time missed, % of overall work productivity impairment, and % of impairment to perform daily activities. Demographic and disease factors assessed at Wave 1 were evaluated as potential predictors of 1-year change in WPAI outcomes.
Results: Of 511 SLE patients employed at disease diagnosis, 252 aged 18-64 remained employed at the time of the Wave 1 survey and 225 completed the WPAI questionnaire. One-hundred-seventy-three patients who were employed at Wave 1 responded to the Wave 2 survey. Of them, 153 (88%) continued to be employed. Lower education attainment (p=0.01), worse physical health (p=0.002) and greater disease activity (p=0.04) were significantly associated with work loss over 1 year, while living with a partner tended to be a protective factor (p=0.09). One-hundred-thirty-six patients who were employed for 2 consecutive years responded to Waves 1 and 2 WPAI surveys. Their mean disease duration and education level were 13 years and 16 years, respectively; 95% were female and 65% were Black.
Conclusion: SLE has a profound impact on employment. In patients having endured more than one decade of disease, the majority become unemployed. Education, physical health and disease activity are associated with short-term work loss, while social support tends to be protective. In those who remain employed, lower education attainment is the only factor predictive of 1-year work productivity impairment. Although not statistically significant, there was a trend towards Black race being negatively associated with work productivity. Patients who live with a partner have better daily life activities performances than those who are single, divorced or widowed. Notably, disease factors do not impact 1-year changes in work productivity. Further research is needed to better understand the pathways through which education, social support and disease status impact short-term work loss and productivity in SLE.
|
Category |
N cases |
Work Time Missed* |
P value |
Overall Work Productivity Impairment* |
P value |
Daily Activities Impairment* |
P value |
|
Demographics |
|
|
|
|
|
|
|
|
Age |
|
|
|
|
|
|
|
|
18–34 (Ref) |
30 |
-0.1 ± 13.9 |
– |
0.2 ± 29.5 |
– |
-5.3 ± 30.0 |
– |
|
35–54 |
85 |
3.0 ± 25.0 |
0.30 |
2.6 ± 33.3 |
0.77 |
-1.5 ± 21.2 |
0.41 |
|
≥55 |
21 |
-0.8 ± 14.6 |
0.92 |
-5.2 ± 24.0 |
0.69 |
-1.9 ± 17.2 |
0.60 |
|
Race |
|
|
|
|
|
|
|
|
Black |
89 |
4.2 ± 25.6 |
0.088 |
5.0 ± 35.0 |
0.083 |
-2.8 ± 23.4 |
0.79 |
|
White (Ref) |
44 |
-2.7 ± 7.7 |
– |
-6.4 ± 19.7 |
– |
-2.0 ± 22.2 |
– |
|
Marital Status |
|
|
|
|
|
|
|
|
Married or cohabited |
71 |
1.7 ± 21.6 |
0.46 |
0.1 ± 31.0 |
0.58 |
-5.2 ± 19.5 |
0.020 |
|
All others (Ref) |
65 |
1.7 ± 21.8 |
– |
1.7 ± 31.4 |
– |
0.6 ± 25.7 |
– |
|
Education attainment |
|
|
|
|
|
|
|
|
≤High School |
22 |
6.3 ± 22.4 |
0.16 |
15.1 ± 25.8 |
0.002 |
1.4 ± 24.6 |
0.44 |
|
Some College |
32 |
-1.0 ± 18.5 |
0.91 |
1.0 ± 38.2 |
0.80 |
-5.6 ± 26.8 |
0.87 |
|
≥College (Ref) |
82 |
1.6 ± 22.5 |
– |
-3.0 ± 28.5 |
– |
-2.2 ± 20.7 |
– |
|
Disease Status |
|
|
|
|
|
|
|
|
Disease Activity |
|
|
|
|
|
|
|
|
Mild (0–15) (Ref) |
90 |
2.4 ± 22.9 |
– |
0.4 ± 31.0 |
– |
-1.7 ± 21.0 |
– |
|
Severe (≥16) |
46 |
0.5 ± 18.9 |
0.92 |
1.9 ± 31.8 |
0.87 |
-3.9 ± 26.1 |
0.49 |
|
Organ Damage |
|
|
|
|
|
|
|
|
No damage (Ref) |
59 |
1.1 ± 19.4 |
– |
-2.4 ± 31.4 |
– |
-1.9 ± 24.0 |
– |
|
Mild (1–2) |
55 |
4.7 ± 24.9 |
0.17 |
4.8 ± 34.1 |
0.62 |
-4.0 ± 23.4 |
0.42 |
|
Severe (>3) |
22 |
-3.8 ± 17.3 |
0.18 |
0.0 ± 21.1 |
0.94 |
0.0 ± 18.3 |
0.87 |
|
Overall Health |
|
|
|
|
|
|
|
|
Excellent or Very Good (Ref) |
40 |
3.2 ± 19.9 |
– |
3.5 ± 29.2 |
– |
1.8 ± 23.7 |
– |
|
Good |
65 |
0.4 ± 21.3 |
0.81 |
-4.8 ± 29.8 |
0.14 |
-3.1 ± 23.4 |
0.23 |
|
Fair or Poor |
31 |
2.6 ± 24.6 |
0.43 |
9.4 ± 34.7 |
0.44 |
-6.5 ± 19.9 |
0.10 |
|
Mental Health Score (SF-12) |
|
|
|
|
|
|
|
|
<50 |
67 |
0.4 ± 20.0 |
0.96 |
1.7 ± 34.4 |
0.69 |
-5.4 ± 26.9 |
0.13 |
|
>50 (Ref) |
69 |
3.1 ± 23.1 |
– |
0.1 ± 27.8 |
. |
0.4 ± 17.6 |
– |
|
Physical Health Score (SF-12) |
|
|
|
|
|
|
|
|
≤50 |
87 |
2.1 ± 26.4 |
0.31 |
-0.5 ± 34.5 |
0.28 |
-4.4 ± 20.2 |
0.35 |
|
>50 (Ref) |
49 |
1.0 ± 7.6 |
– |
3.3 ± 24.2 |
– |
1.0 ± 26.7 |
– |
|
* Mean of % change ±SD: negative values indicate improvement |
|||||||
Disclosure:
D. Sree,
None;
S. S. Lim,
GlaxoSmithKline,
2;
H. Kan,
GlaxoSmithKline,
3;
P. M. Jhingran,
GlaxoSmithKline,
3,
GlaxoSmithKline,
1;
C. T. Molta,
GlaxoSmithKline,
3,
GlaxoSmithKline,
1;
G. Bao,
None;
C. Drenkard,
GlaxoSmithKline,
2.
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