Socioeconomic Status, Access to Health Care, and Outcomes After Acute Myocardial Infarction in Canada's Universal Health Care System
Bibliographic record
Abstract
BACKGROUND: There is a debate as to whether universal drug coverage confers similar access to care at all socioeconomic status (SES) levels. Experiences in Canada may bring light to questions raised regarding access. OBJECTIVE: To assess associations between SES and access to cardiac care and outcomes in Canada's universal health care system. DESIGN, SETTING, AND PATIENTS: All patients admitted to acute care hospitals in Quebec (QC), Ontario (ON), and British Columbia (BC), between 1996 and either 2000 (QC) or 2001 (ON, BC) with acute myocardial infarction, were identified using provincial government administrative databases (n = 145,882). MEASUREMENTS: Variables representing SES grouped at the census area level were examined in association with use of cardiac medications and procedures, survival, and readmission, while adjusting for individual-level variables. A Bayesian hierarchical logistic regression model was used to account for the nested structure of the data. RESULTS: Despite provincial variations in SES and drug reimbursement policies, there were generally no associations between the SES variables and access to cardiac medications or invasive cardiac procedures. The few exceptions were not consistent across SES indicators and/or provinces. Similarly, the only observed effect of SES on clinical outcomes was in BC, where there was increased 1-year mortality among patients living in less-affluent regions (adjusted odds ratios per standard deviation change in proportion of low-income households, 95% Bayesian credible intervals, QC: 1.09, 0.96-1.25; ON: 1.02, 0.95-1.08; and BC: 1.18, 1.09-1.28). CONCLUSIONS: These results suggest that intermediary factors other than SES, such as cardiovascular risk factors, likely account for observed "wealth-health" gradients in Canada. Implementation of a universal drug coverage policy could decrease socioeconomic disparities in access to health 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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".