How are Volume–Outcome Associations Related to Models of Health Care Funding and Delivery? A Comparison of the United States and Canada
Bibliographic record
Abstract
How models of health care financing and delivery affect patterns of procedure volumes, outcomes, and volume-outcome associations is not known. We compared volume-outcome studies done in Canada, which provides residents with universal, single-payer health care, with those done in the United States, to determine whether there was a difference in the likelihood of finding statistically significant volume-outcome associations. We analyzed 142 articles, most (90.1%) of which were from the United States. The articles described a total of 291 separate analyses. After adjusting for the clustering of multiple analyses in the same study, the likelihood of finding a statistically significant volume-outcome association was substantially lower in Canadian studies as compared with those from the United States (odds ratio 0.24, 95% confidence interval 0.08 to 0.74, p = 0.01). This result persisted after adjustment for the procedure/condition studied, and the number of study subjects. Canadian volume-outcome analyses are less likely to identify statistically significant volume-outcome associations than US studies, possibly because of the smaller size of some Canadian studies. It is also possible that different models of health care financing and delivery affect patterns of procedure volumes and volume-outcome associations. By promoting competition between hospitals and providers, market-based models may exacerbate existing variations in the quality of hospital care.
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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 | 0.016 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".