The Relation between Disease Severity and Cost of Caring for Patients with Alzheimer Disease in Canada
Bibliographic record
Abstract
OBJECTIVES: to characterize the cost of caring for an outpatient in Canada with Alzheimer disease (AD) based on disease severity, and to describe how costs change with increases in disease severity. METHOD: community-dwelling patients with mild-to-moderate AD were enrolled in a 3-year, naturalistic, observational study. Assessments included cognition (Mini Mental Status Examination), global ratings (Global Deterioration Scale [GDS]), and daily function (Functional Autonomy Measurement System) as part of the Canadian Outcomes Study in Dementia. Direct (medical and nonmedical) and indirect costs were collected using resource use questionnaires. Costs at baseline were compared with costs at follow-up and correlated with disease severity. RESULTS: total costs associated with treating AD were significantly higher with greater disease severity. The mean total cost to treat patients with very mild AD (GDS = 2) was $367 per month, compared with $4063 per month for patients with severe or very severe AD (GDS = 6). From baseline to follow-up, the greatest changes in cost were observed in the group of patients with the most severe AD as measured by all scales. The largest component of total cost was indirect costs at most severity levels, though medication costs contributed the most in patients with very mild AD. Significant independent contributors to cost were being female, having more impaired activities of daily living, and exhibiting more neuropsychiatric symptoms. CONCLUSIONS: costs for treating a patient with AD were strongly associated with disease severity, even though none of the patients were institutionalized. Delaying the progression of AD may reduce indirect costs and burden to caregivers.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".