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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".