Time-Varying Parameter Population Health Models and the Health Effects of Social Services vs. Health Care Spending: An Application to Canada
Bibliographic record
Abstract
A recent strand of the health literature claims that, for a healthier population, governments in high-income countries such as Canada should shift expenditure from health care to the provision of social services. Authors in this literature make this recommendation based on the finding that in standard static constant-parameter health models, the ratio of social services to health care spending is systematically associated with higher life expectancy and lower mortality across the OECD countries. We evaluate the robustness of this important claim to (i) likely time-variation in the model parameters (ii) delayed effects of health determinants and (iii) to disaggregation of health care spending into its major components. Methods: We conduct a longitudinal study of the comparative empirical performances of four time-varying parameter, dynamic and disaggregated health-indicator models relative to the benchmark models typically estimated in the literature, using a Canadian national dataset. Results: We find evidence that spending on social services may indeed increase life expectancy and lower mortality more than spending on health care; but this finding emerges only in dynamic models that allow for time variation in the coefficients. Disaggregation generally shows that hospital care lowers mortality by more than all other categories of spending, including social services.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".