Capturing the Impact of Constraints on the Cost-Effectiveness of Cell and Gene Therapies: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Decision-makers need to resolve constraints on delivering cell and gene therapies to patients as these treatments move into routine care. This study aimed to investigate if, and how, constraints that affect the expected cost and health consequences of cell and gene therapies have been included in published examples of cost-effectiveness analyses (CEAs). METHOD: A systematic review identified CEAs of cell and gene therapies. Studies were identified from previous systematic reviews and by searching Medline and Embase until 21 January 2022. Constraints described qualitatively were categorised by theme and summarised by a narrative synthesis. Constraints evaluated in quantitative scenario analyses were appraised by whether they changed the decision to recommend treatment. RESULTS: Thirty-two CEAs of cell (n = 20) and gene therapies (n = 12) were included. Twenty-one studies described constraints qualitatively (70% cell therapy CEAs; 58% gene therapy CEAs). Qualitative constraints were categorised by four themes: single payment models; long-term affordability; delivery by providers; manufacturing capability. Thirteen studies assessed constraints quantitatively (60% cell therapy CEAs; 8% gene therapy CEAs). Two types of constraint were assessed quantitatively across four jurisdictions (USA, Canada, Singapore, The Netherlands): alternatives to single payment models (n = 9 scenario analyses); improving manufacturing (n = 12 scenario analyses). The impact on decision-making was determined by whether the estimated incremental cost-effectiveness ratios crossed a relevant cost-effectiveness threshold for each jurisdiction (outcome-based payment models: n = 25 threshold comparisons made, 28% decisions changed; improving manufacturing: n = 24 threshold comparisons made, 4% decisions changed). CONCLUSION: The net health impact of constraints is vital evidence to help decision-makers scale up the delivery of cell and gene therapies as patient volume increases and more advanced therapy medicinal products are launched. CEAs will be essential to quantify how constraints affect the cost-effectiveness of care, prioritise constraints to be resolved, and establish the value of strategies to implement cell and gene therapies by accounting for their health opportunity cost.
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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 | 0.051 | 0.219 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".