An Exploratory Cost-Effectiveness Analysis of Immune Therapy in Delaying the Initiation of Automated Insulin Delivery Systems in Type 1 Diabetes
Bibliographic record
Abstract
Objective: Immune therapies such as teplizumab and antithymocyte globulin (ATG) offer promise in delaying type 1 diabetes (T1D). However, growing availability of automated insulin delivery (AID) systems for insulin management may alter the cost-effectiveness of these therapies. Immune therapies may become more cost-effective when paired with AID instead of conventional insulin management. Meanwhile, as immune therapies delay T1D for only a short period, effective AID may reduce the economic value of prevention. This study provides the first cost-effectiveness analysis of the interplay between immune therapies and AID systems. Methods: Using microsimulation modeling, we examined the cost-effectiveness of six alternative prevention-treatment strategies defined by a combination of three preventive immune therapies (teplizumab, ATG, or no therapy) and two insulin management strategies (AID or conventional insulin management). Effectiveness was measured by quality-adjusted life years (QALYs). Costs were estimated from a payer perspective. Results: Among the six strategies considered, preventive ATG therapy followed by AID was the most cost-effective. It entailed $394,250 in lifetime costs and yielded 19.13 QALYs. These costs were lower and QALY gains higher than those with strategies that did not involve immune therapy or AID. Preventive teplizumab therapy followed by AID generated 0.25 more QALYs than ATG therapy followed by AID, albeit at an additional cost of $153,670, resulting in an incremental cost-effectiveness ratio of $369,890/QALY. Conclusions: Preventive ATG therapy followed by AID after T1D onset can be a potentially cost-effective approach. In the absence of randomized clinical trials for ATG in the prevention space, findings in this study assume that ATG is at least half as efficacious as teplizumab. The optimal prevention-treatment strategy will ultimately depend on payers’ ability to negotiate prices for teplizumab and further evidence on efficacy of ATG in preventing T1D.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".