Access criteria for anti-TNF agents in spondyloarthritis: influence on comparative 1-year cost-effectiveness estimates
Bibliographic record
Abstract
BACKGROUND: Anti-tumor necrosis factor (anti-TNF) agents are an effective, but costly, treatment for spondyloarthritis (SpA). Worldwide, multiple sets of access criteria aim to restrict anti-TNF therapy to patients with specific clinical characteristics, yet the influence of access criteria on anti-TNF cost-effectiveness is unknown. Our objective was to use data from the DESIR cohort, a prospective study of early SpA patients in France, to determine whether the French anti-TNF access criteria are the most cost-effective in that setting relative to other potential restrictions. METHODS: We used data from the DESIR cohort to create five study populations of patients meeting anti-TNF access criteria from Canada, France, Germany, United Kingdom, and Hong Kong, respectively. For each study population, we calculated the costs and quality-adjusted life years (QALYs) over 1 year of patients treated and not treated with anti-TNF therapy. To control for differences between anti-TNF users and non-users, we used linear regression models to derive adjusted mean costs and QALYs. We calculated incremental cost-effectiveness ratios (ICERs) representing the incremental cost per additional QALY gained by treating with an anti-TNF within each of the five study populations, using bootstrapping to explore the range of uncertainty in costs and QALYs. A series of sensitivity analyses was conducted, including one to simulate the effect of a 24-week stopping rule for anti-TNF non-responders. RESULTS: Anti-TNF access criteria from France were satisfied by the largest proportion of DESIR patients (27.8%), followed by Germany (25.1%), Canada (23.8%), the UK (12.1%) and Hong Kong (8.6%). Confidence intervals around incremental costs and QALYs in the basecase analysis were overlapping, indicating that anti-TNF cost-effectiveness estimates derived from each subset were similar. In the sensitivity analysis that examined the effect of excluding costs accumulated past 24 weeks by anti-TNF non-responders, the incremental cost per QALY was reduced by approximately 25% relative to the basecase analysis (France: €857,992 vs. €1,105,859; Canada: € 626,459 vs. €818,186; Germany: € 422,568 vs. €545,808); UK €578,899 vs. €766,217; Hong Kong €335,418 vs. €456,850). CONCLUSIONS: Anti-TNF cost-effectiveness is strongly affected by treatment continuation among non-responders. Access criteria could improve anti-TNF cost-effectiveness by defining patients likely to respond.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.267 | 0.522 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".