On the Importance of Taking End-of-Life Expenditures into Account when Projecting Health-Care Spending
Bibliographic record
Abstract
This paper examines health-care spending projections when the interaction between end-of-life care expenditures and declining mortality is taken explicitly into account. Based on Quebec's historical public health-care spending data and mortality rates for 20 age groups over the period 1998 to 2009, an econometric model is developed with the aim of differentiating “ordinary” health-care spending from end-of-life care expenditures. Numerical simulations reveal that the average annual growth rate of future health-care spending projected over the period 2009–2056 diminishes by about 0.19 to 0.23 percentage points. This implies a cumulative health-care savings of about 8.4 to 10.3 percent in 2056, independent of other health-related factors.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".