American's overall and equity-based societal valuation of a disease-modifying Alzheimer's treatment: Results from a discrete choice experiment
Bibliographic record
Abstract
OBJECTIVES: To estimate Americans' willingness-to-pay (WTP) for universal access to a disease-modifying Alzheimer's disease (AD) treatment with a discrete choice experiment in a nationally representative sample. As part of this experiment, we examined whether providing information about the higher disease burden among minorities and persons of lower socioeconomic status (SES) changes WTP. METHODS: We conducted an information experiment using the nationally representative Understanding America Study (UAS) panel. Participants were provided with general information about AD and a hypothetical treatment that reduces disease progression by 30 %. Two-thirds of the sample were randomized to receive additional information about the higher prevalence of Alzheimer's among either lower SES groups or racial/ethnic minorities. We measured participants' WTP for making the treatment nationally available as a fixed annual fee and income-proportionate fee. Differences in WTP between those exposed to the additional information and those who were not provide the societal valuation of the equity-enhancing effects of the AD treatment. RESULTS: Average valuations were $252, $260 and $247 per year, and 0.59 %, 0.59 % and 0.61 % of earned income, for the control, race/ethnicity and SES frames, respectively-all statistically indistinguishable. These average results imply that Americans would be willing to pay $33.7 billion based on the fixed fee and $51.4 billion based on the income-related charge for universal access to an AD treatment annually, but their valuation does not further increase when informed about equity considerations. CONCLUSIONS: While Americans value universal access to an AD treatment highly, health equity considerations did not significantly alter respondents' WTP.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".