Dissonance in the face of Alzheimer's disease breakthroughs: clinician and lay stakeholder acceptance, concerns and willingness to pay for emerging disease-modifying therapies
Bibliographic record
Abstract
BACKGROUND: Introducing new disease-modifying therapies (DMTs) for Alzheimer's disease demands a fundamental shift in diagnosis and care for most health systems around the world. Understanding the views of health professionals, potential patients, care partners and taxpayers is crucial for service planning and expectation management about these new therapies. AIMS: To investigate the public's and professionals' perspectives regarding (1) acceptability of new DMTs for Alzheimer's disease; (2) perceptions of risk/benefits; (3) the public's willingness to pay (WTP). METHOD: Informed by the 'theoretical framework of acceptability', we conducted two online surveys with 1000 members of the general public and 77 health professionals in Ireland. Descriptive and multivariate regression analyses examined factors associated with DMT acceptance and WTP. RESULTS: Healthcare professionals had a higher acceptance (65%) than the general public (48%). Professionals were more concerned about potential brain bleeds (70%) and efficacy (68%), while the public focused on accessibility and costs. Younger participants (18-24 years) displayed a higher WTP. Education and insurance affected WTP decisions. CONCLUSIONS: This study exposes complex attitudes toward emerging DMTs for Alzheimer's disease, challenging conventional wisdom in multiple dimensions. A surprising 25% of the public expressed aversion to these new treatments, despite society's deep-rooted fear of dementia in older age. Healthcare professionals displayed nuanced concerns, prioritising clinical effectiveness and potential brain complications. Intriguingly, younger, better-educated and privately insured individuals exhibited a greater WTP, foregrounding critical questions about healthcare equity. These multifaceted findings serve as a guidepost for healthcare strategists, policymakers and ethicists as we edge closer to integrating DMTs into Alzheimer's disease care.
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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 | 0.026 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".