Health expenditure growth: reassessing the threat of ageing
Why this work is in the frame
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Bibliographic record
Abstract
In this paper we evaluate the respective effects of demographic change, changes in morbidity and changes in practices on growth in health care expenditures. We use microdata, i.e. representative samples of 3441 and 5003 French individuals observed in 1992 and 2000. Our data provide detailed information about morbidity and allow us to observe three components of expenditures: ambulatory care, pharmaceutical and hospital expenditures. We propose an original microsimulation method to identify the components of the drift observed between 1992 and 2000 in the health expenditure age profile. On the one hand, we find empirical evidence of health improvement at a given age: changes in morbidity induce a downward drift of the profile. On the other hand, the drift due to changes in practices is upward and sizeable. Detailed analysis attributes most of this drift to technological innovation. After applying our results at the macroeconomic level, we find that the rise in health care expenditures due to ageing is relatively small. The impact of changes in practices is 3.8 times larger. Furthermore, changes in morbidity induce savings which more than offset the increase in spending due to population ageing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it