Early Health Economic Modeling of Novel Therapeutics in Age-Related Hearing Loss
Bibliographic record
Abstract
Background: Health systems face challenges to accelerate access to innovations that add value and avoid those unlikely to do so. This is very timely to the field of age-related sensorineural hearing loss (ARHL), where a significant unmet market need has been identified and sizeable investments made to promote the development of novel hearing therapeutics (NT). This study aims to apply health economic modeling to inform the development of cost-effective NT. Methods: We developed a decision-analytic model to assess the potential costs and effects of using regenerative NT in patients ≥50 with ARHL. This was compared to the current standard of care including hearing aids and cochlear implants. Input data was collected from systematic literature searches and expert opinion. A UK NHS healthcare perspective was adopted. Three different but related analyses were performed using probabilistic modeling: (1) headroom analysis, (2) scenario analyses, and (3) threshold analyses. Results: The headroom analysis shows an incremental net monetary benefit (iNMB) of £20,017[£11,299-£28,737] compared to the standard of care due to quality-adjusted life-years (QALY) gains and cost savings. Higher therapeutic efficacy and access for patients with all degrees of hearing loss yields higher iNMBs. Threshold analyses shows that the ceiling price of the therapeutic increases with more severe degrees of hearing loss. Conclusion: NT for ARHL are potentially cost-effective under current willingness-to-pay (WTP) thresholds with considerable room for improvement in the current standard of care pathway. Our model can be used to help decision makers decide which therapeutics represent value for money and are worth commissioning, thereby paving the way for urgently needed NT.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".