Cost-Effectiveness of Low-Dose Antithymocyte Globulin Versus Other Immunotherapies for Treatment of New-Onset Type 1 Diabetes
Bibliographic record
Abstract
Objective: Several immunotherapies have shown efficacy in slowing C-peptide decline in new-onset type 1 diabetes. Although most of these biologic drugs are expensive, they offer the opportunity to reduce downstream disease management costs and risk of complications. The objective of this study is to examine the cost-effectiveness of immunotherapies versus no treatment for patients with new-onset type 1 diabetes. Methods: Using Markov microsimulation modeling and efficacy data from immunotherapy trials, we examined the cost-effectiveness of six immunotherapies for new-onset type 1 diabetes, namely, low-dose (2.5 mg/kg) antithymocyte globulin (ATG), high-dose (6.5 mg/kg) ATG, abatacept, alefacept, rituximab, and teplizumab, versus no treatment. Effectiveness was measured by quality-adjusted life-years (QALYs). Costs were estimated from a health system perspective. Results: Low-dose ATG treatment saves US$10,270, on average, over a patient's lifetime and generates 0.09 additional QALYs compared with no treatment. These cost savings arise as low-dose ATG generates downstream savings in disease management costs that more than offset its cost. In contrast, treatment with other immunotherapies yields smaller QALY gains (0.02–0.05 additional QALYs) and increases lifetime costs by US$9500–US$168,380 relative to no treatment, with incremental cost-effectiveness ratios that exceed the willingness-to-pay threshold of US$100,000 per QALY. Conclusions: Low-dose ATG treatment is both less costly and more effective relative to other immunotherapies and no treatment for new-onset type 1 diabetes.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".