Assessing Quality of Life, Economic Burden, and Independence Across the Alzheimer’s Disease Continuum Using Patient-Caregiver Dyad Surveys
Bibliographic record
Abstract
Background: Alzheimer's disease (AD) and mild cognitive impairment (MCI) have negative quality of life (QoL) and economic impacts on patients and their caregivers and may increase along the disease continuum from MCI to mild, moderate, and severe AD. Objective: To assess how patient and caregiver QoL, indirect and intangible costs are associated with MCI and AD severity. Methods: An on-line survey of physician-identified patient-caregiver dyads living in the United States was conducted from June-October 2022 and included questions to both patients and their caregivers. Dementia Quality of Life Proxy, the Care-related Quality of Life, Work Productivity and Activity Impairment, and Dependence scale were incorporated into the survey. Regression analyses investigated the association between disease severity and QoL and cost outcomes with adjustment for baseline characteristics. Results: One-hundred patient-caregiver dyads were assessed with the survey (MCI, n = 27; mild AD, n = 27; moderate AD, n = 25; severe AD, n = 21). Decreased QoL was found with worsening severity in patients (p < 0.01) and in unpaid (informal) caregivers (n = 79; p = 0.02). Dependence increased with disease severity (p < 0.01). Advanced disease severity was associated with higher costs to employers (p = 0.04), but not with indirect costs to caregivers. Patient and unpaid caregiver intangible costs increased with disease severity (p < 0.01). A significant trend of higher summed costs (indirect costs to caregivers, costs to employers, intangible costs to patients and caregivers) in more severe AD was observed (p < 0.01). Conclusions: Patient QoL and functional independence and unpaid caregiver QoL decrease as AD severity increases. Intangible costs to patients and summed costs increase with disease severity and are highest in 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".