Does Higher Spending Improve Survival Outcomes for Myocardial Infarction? Examining the Cost‐Outcomes Relationship Using Time‐Varying Covariates
Bibliographic record
Abstract
OBJECTIVES: Previous patient-level acute myocardial infarction (AMI) research has found higher hospital spending to be associated with improved survival; however, survivor-treatment selection bias traditionally has been overlooked. The purpose of this study was to examine the AMI cost-outcome relationship, taking into account this form of bias. DATA SOURCES: Hospital Discharge Abstract data tracked costs for AMI hospitalizations. Ontario Vital Statistics data tracked patient mortality. STUDY DESIGN: A standard Cox survival model was compared to an extended Cox model using hospital costs as a time-varying covariate to examine the impact of cost on 1-year survival in a cohort of 30,939 first-time AMI patients in Ontario, Canada, from 2007 to 2010. PRINCIPAL FINDINGS: Higher patient-level AMI spending decreased the hazard of dying (Standard Model: log-cost hazard ratio: 0.513, 95 percent CI: 0.479-0.549; Extended Model: log-cost hazard ratio: 0.700, 95 percent CI: 0.645-0.758); however, the protective effect was overestimated by 62 percent when survivor-treatment bias was overlooked. In the extended model, a 10 percent increase in spending was associated with a 3.6 percent decrease in hazard of death. CONCLUSION: The findings of this study suggest that if survivor-treatment bias is overlooked, future research may materially overstate the protective effect of patient-level spending on outcomes.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".