Microsimulation model of the cost-effectiveness of anifrolumab compared to belimumab in the United Arab Emirates
Bibliographic record
Abstract
SLE imposes a significant morbidity and mortality as well as a substantial burden on the healthcare system. The model aimed to measure the cost-effectiveness of anifrolumab implementation against belimumab as an add-on-therapy to the standard of care (SoC) over a lifetime horizon for Emirati patients. A microsimulation model was used to assess the cost-effectiveness of anifrolumab against belimumab (IV/SC) as an add-on therapy to SoC in a hypothetical cohort of adult Emirati patients with systemic lupus erythematosus (SLE) over a lifetime horizon. The clinical data was captured from published clinical trials as; TULIP-1, TULIP-2, BLISS-52, BLISS-76 and BLISS‐SC. Health utility scores were constructed according to a linear regression model from the pooled data of the two TULIP Phase III trials of anifrolumab. Our model captures direct SLE-related medical costs from the Dubai Health Authority. Sensitivity analyses were conducted to assess model uncertainty. Using BICLA as a response criterion in the Johns Hopkins cohort, anifrolumab was found to be more effective than belimumab (IV/SC; the incremental discounted QALY of anifrolumab against belimumab was 0.42). The incremental cost-effectiveness ratio (ICER) of anifrolumab against belimumab IV and belimumab SC were AED 466,371 ($209,135) and AED 252,612 ($113,279), respectively, these ICERs are below the cost-effectiveness threshold in the United Arab Emirates (UAE) (three times gross domestic product capita; AED 592,278). In the Toronto lupus cohort, the ICER of anifrolumab against belimumab IV and belimumab SC were AED 491,403 ($220,360) and AED 276,642 ($124,055), respectively (anifrolumab was a cost-effective option vs. belimumab IV and belimumab SC). The addition of anifrolumab to SoC is a cost-effective option versus belimumab for the treatment of adult patients with active, autoantibody-positive SLE, despite being allocated to SoC. Cost-effectiveness was demonstrated by a reduction in complications and organ damage, which reflected costs and outcomes.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".