Integrating intangible costs into societal cost estimates of Alzheimer's disease
Bibliographic record
Abstract
BackgroundAlzheimer's disease (AD) is associated with considerable economic burden, the full extent of which can be challenging to quantify from a societal perspective.ObjectiveTo estimate the total societal cost of AD in the United States by integrating direct costs, out-of-pocket expenses, indirect costs to caregivers, costs to business, and intangible/emotional costs to patients/caregivers across the disease continuum from mild cognitive impairment (MCI) to severe AD.MethodsIntangible costs were derived from a patient-caregiver survey. Other indirect costs were from a Health and Retirement Study (HRS) analysis; direct costs were from the literature. We estimated integrated societal cost per patient per month (PPPM) for MCI and AD (mild/moderate/severe). Negative binomial regression of indirect costs examined associations with severity, adjusting for baseline characteristics.ResultsIntegrated societal costs PPPM were $4176 for MCI and $7873 for AD ($6,634, $7,291, and $9287 for mild, moderate, and severe, respectively); intangible costs represented 24-32% of societal costs. Indirect costs were higher with AD versus MCI (p < 0.001); married status and nursing home residence were associated with lower indirect costs in AD.ConclusionsIntangible costs are a major driver, besides direct costs, of the integrated societal cost of MCI/AD. Societal costs are higher with more severe AD.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".