Costs of early spondyloarthritis: estimates from the first 3 years of the DESIR cohort
Bibliographic record
Abstract
OBJECTIVES: To value health resource utilisation and productivity losses in DESIR, a longitudinal French cohort of 708 patients with early spondyloarthritis (SpA) enrolled between 2007 and 2010, and identify factors associated with costs in the first 3 years of follow-up. METHODS: Self-reported clinical data from DESIR and French public data were used to value health resource utilisation and productivity losses in 2013 Euros. Factors associated with costs, including and excluding biological drugs, were identified in generalised linear models using the generalised estimating equations algorithm to account for repeated observations over participants. RESULTS: The mean (±SD) annual cost per patient was €5004±6870 in year 1, decreasing to €4961±7457 in year 3. Patients who never received a biologic had mean 3-year total costs of €4789±6022 compared to €38 206±19 829 among those who received a biologic. Factors associated with increased total costs were peripheral arthritis (rate ratio (RR) 1.19; 95% CI 1.04 to 1.37; p<0.0001), time on biologics (RR 1.23 per month; 1.21, 1.24; p<0.0001), and average BASFI score (RR 1.18/10 point increase; 1.15, 1.25; p<0.0001). Factors associated with increased costs excluding biologics were baseline age (RR 1.10 per 5 year increase; 1.05, 1.16; p<0.0001), peripheral arthritis (RR 1.20; 1.02, 1.40; p<0.0133), time on biologics (RR 1.04 per month; 1.02, 1.05; p<0.0001), and average BASDAI score (RR 1.21 per 10 point increase; 1.16, 1.25; p<0.0001). CONCLUSIONS: In addition to biologics, factors like age, peripheral arthritis and disease activity independently increase SpA-related costs. This study may serve as a benchmark for cost of illness among patients with early SpA in the biologic era.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".