Economic Evaluation of Damage Accrual in an International Systemic Lupus Erythematosus Inception Cohort Using a Multistate Model Approach
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".