Does age or life expectancy better predict health care expenditures?
Bibliographic record
Abstract
It is an unresolved issue whether age or (expected) remaining life years better predicts health care expenditures. We first estimate a set of hazard models to predict life expectancy based on individual demographic characteristics and health conditions, and then use regression analyses to compare the predictive power of age and life expectancy in explaining health care expenditures. This paper differs from previous studies in that it uses predicted life expectancy to address the censoring of death; as a result, this paper goes beyond the large health care expenditures at the end of life and the results apply to both deceased and survivors. We find that age has little additional predictive power on health care expenditures after controlling for life expectancy, but the predictive power of life expectancy itself diminishes as health status measures are introduced into the model. These results are not of esoteric interest only for their statistical properties; we show that using life expectancy rather than age results in lower projections of future health care expenditures. This result suggests that increases in longevity might be less costly than models based on the current age profile of spending would predict.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".