Provincial health expenditure and cardiovascular disease mortality, a panel data study of Canadian provinces
Bibliographic record
Abstract
OBJECTIVE: Health expenditures on cardiovascular disease (CVD) account for a large proportion of health care expenditures of all the diseases in Canada, and hence there is a need to examine the responsiveness of CVD outcomes to health expenditures. The objective of this study was to examine the relationship between health care expenditures and CVD mortality, as a health care outcome at the provincial level in Canada. METHODS: A 10-year (2000-2009) panel dataset was constructed from multiple data sources for the purposes of this study. The dataset composed of age standardized CVD mortalities, health care expenditures, and covariates for the 10 Canadian provinces. We employed a fixed effects model based on the results of the Hausman test, with CVD mortalities as the dependent variable and health care expenditure and other covariates, as explanatory variables. RESULTS: Health care expenditures were significantly (0.05) and negatively associated with CVD mortality, with a 1% increase in health care expenditures associated with a decrease of 6.31 per 1 000 000 people in CVD mortality. CONCLUSION: In the Canadian context, increases in spending on health care were associated with improvements in CVD outcomes for the time period under investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".