Environmental influences on healthcare expenditures: an exploratory analysis from Ontario, Canada
Bibliographic record
Abstract
STUDY OBJECTIVE: This paper explores the relation between healthcare expenditures (HCEs) and environmental variables in Ontario, Canada. DESIGN: The authors used a sequential two stage regression model to control for variables that may influence HCEs and for the possibility of endogenous relations. The analysis relies on cross sectional ecological data from the 49 counties of Ontario. MAIN RESULTS: The results show that, after control for other variables that may influence health expenditures, both total toxic pollution output and per capita municipal environmental expenditures have significant associations with health expenditures. Counties with higher pollution output tend to have higher per capita HCEs, while those that spend more on defending environmental quality have lower expenditures on health care. CONCLUSIONS: The implications of our findings are twofold. Firstly, sound investments in public health and environmental protection have external benefits in the form of reduced HCEs. Combined with the other benefits such as recreational values, investments in environmental protection probably yield net social benefits. Secondly, health policy that excludes consideration of environmental quality may eventually result in increased expenditures. These results suggest a need to broaden the cost containment debate to ensure environmental determinants of health receive attention as potential complements to conventional cost control policies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".