Projected Savings to Canadian Provincial Budgets from Reduced Long-Term Care Home Utilization Due to a Disease-Modifying Alzheimer's Treatment
Bibliographic record
Abstract
BACKGROUND: A disease-modifying Alzheimer's treatment could provide budgetary savings to Canadian provinces from a reduction in long-term care home use, yet we do not know the magnitude of those potential savings. OBJECTIVE: We project savings to each Canadian province's budget from 2023 to 2043. DESIGN: Annual savings are projected using a Markov model. We account for reduction in long-term care home use and in use of Alternative Level of Care (ALC) beds, which are hospital beds occupied by care home-eligible patients on the wait list for admission. RESULTS: A treatment that delays disease progression by 40% is projected to avoid 142,507 long-term care home and ALC years, resulting in $17.2 billion cumulative savings across all Canadian provinces, a 21% relative reduction among treatment eligible patients. Average per capita savings were $1,132, ranging from $734 (Alberta) to $2,895 (Prince Edward Island). Cumulative savings could increase to $22.7 billion with enhanced triage of patients in primary care stages and to $25.6 billion if all capacity constraints for diagnosis and treatment were removed. CONCLUSION: A disease-modifying treatment could create budgetary savings from lower long-term care home use, offsetting part of the treatment cost. With the increasing demand for long-term care home beds and the high rates of patients being held in hospitals while wait-listed, such a treatment could additionally provide relief to the overburdened long-term care system in Canada.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".