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Updated: Jan 5, 2026

The bm12 Inducible Model of Systemic Lupus Erythematosus SLE in C57BL/6 Mice
Published on: November 1, 2015
Economic Evaluation of Damage Accrual in an International Systemic Lupus Erythematosus Inception Cohort Using a
Megan R W Barber1, John G Hanly2, Li Su3
1University of Calgary, Alberta, Canada.
Objective:
There is a paucity of data regarding health care costs associated with damage accrual in systemic lupus erythematosus. The present study was undertaken to describe costs associated with damage states across the disease course using multistate modeling.
Methods:
Patients from 33 centers in 11 countries were enrolled in the Systemic Lupus International Collaborating Clinics (SLICC) inception cohort within 15 months of diagnosis. Annual data on demographics, disease activity, damage (SLICC/American College of Rheumatology Damage Index [SDI]), hospitalizations, medications, dialysis, and selected procedures were collected. Ten-year cumulative costs (Canadian dollars) were estimated by multiplying annual costs associated with each SDI state by the expected state duration using a multistate model.
Results:
A total of 1,687 patients participated; 88.7% were female, 49.0% were white, mean ± SD age at diagnosis was 34.6 ± 13.3 years, and mean time to follow-up was 8.9 years (range 0.6-18.5 years). Mean annual costs were higher for those with higher SDI scores as follows: $22,006 (Canadian) (95% confidence interval [95% CI] $16,662, $27,350) for SDI scores ≥5 versus $1,833 (95% CI $1,134, $2,532) for SDI scores of 0. Similarly, 10-year cumulative costs were higher for those with higher SDI scores at the beginning of the 10-year interval as follows: $189,073 (Canadian) (95% CI $142,318, $235,827) for SDI scores ≥5 versus $21,713 (95% CI $13,639, $29,788) for SDI scores of 0.
Conclusion:
Patients with the highest SDI scores incur 10-year cumulative costs that are ~9-fold higher than those with the lowest SDI scores. By estimating the damage trajectory and incorporating annual costs, data on damage can be used to estimate future costs, which is critical knowledge for evaluating the cost-effectiveness of novel therapies.
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