P3‐407: Patients’ and caregivers’ willingness to pay for a new Alzheimer's disease medication
Bibliographic record
Abstract
Many Canadians pay ‘out-of-pocket’ for Alzheimer's disease (AD) medications. We investigated whether and to what extent Canadian patients with mild to moderate AD and their primary caregivers would be willing to pay out-of-pocket for a new AD medication. We recruited 96 patient-caregiver dyads from nine clinics across Canada and collected data via one-on-one personal interviews. Participants were presented with four scenarios describing a hypothetical, new AD medication. Two scenarios described the medication as treating the symptoms of cognitive decline; two other scenarios described the medication as halting disease progression. For each pair of ‘efficacy’ scenarios, the medication was assumed to have no adverse effects in one scenario and a 30% chance of primarily gastrointestinal adverse effects in the other scenario. In each scenario, interviewers asked participants whether they would pay out-of-pocket for the new medication. Participants who responded affirmatively provided the maximum Canadian dollar amount they would be willing to pay per month. We assessed willingness to pay using a combination of bid ranges and open-ended questions; we randomized participants to order of scenarios and bids. Median ages were 81 years (patients) and 70.5 years (caregivers); 43 patients and 69 caregivers were female. Median self-reported health state on the EQ-5D VAS (0=worst state; 100=best state) was 80 for patients and caregivers alike. Overall, 63-77% of patients and 66-88% of caregivers reported they would pay out-of-pocket for the AD medication, depending on scenario. Patient and caregiver support was highest for the symptom treatment and no adverse effects scenario. Average maximum willingness to pay per month ranged from $198-219 for patients and $289-331 for caregivers, depending on scenario. Both groups’ highest average maximum willingness to pay was for the disease modifying and no adverse effects scenario. Most participants supported paying out-of-pocket for the AD medication. For each scenario, greater proportions of caregivers supported out-of-pocket payment relative to patients. Also, regardless of scenario, caregivers’ average maximum willingness to pay exceeded patients’ average maximum willingness to pay.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".