Area Median Income and Metropolitan Versus Nonmetropolitan Location of Care for Acute Coronary Syndromes: A Complex Interaction of Social Determinants
Bibliographic record
Abstract
BACKGROUND: Metropolitan versus nonmetropolitan status and area median income may independently affect care for and outcomes of acute coronary syndromes. We sought to determine whether location of care modifies the association among area income, receipt of cardiac catheterization, and mortality following an acute coronary syndrome in a universal health care system. METHODS AND RESULTS: We studied a cohort of 14 012 acute coronary syndrome patients admitted to cardiology services between April 18, 2004, and December 31, 2011, in southern Alberta, Canada. We used multivariable logistic regression to determine the odds of cardiac catheterization within 1 day and 7 days of admission and the odds of 30-day and 1-year mortality according to area median household income quintile for patients presenting at metropolitan and nonmetropolitan hospitals. In models adjusting for area income, patients who presented at nonmetropolitan facilities had lower adjusted odds of receiving cardiac catheterization within 1 day of admission (odds ratio 0.22, 95% CI 0.11-0.46, P<0.001). Among nonmetropolitan patients, when examined by socioeconomic status, each incremental decrease in income quintile was associated with 10% lower adjusted odds of receiving cardiac catheterization within 7 days (P<0.001) and 24% higher adjusted odds of 30-day mortality (P=0.008) but no significant difference for 1-year mortality (P=0.12). There were no differences in adjusted mortality among metropolitan patients. CONCLUSION: Within a universal health care system, the association among area income and receipt of cardiac catheterization and 30-day mortality differed depending on the location of initial medical care for acute coronary syndromes. Care protocols are required to improve access to care and outcomes in patients from low-income nonmetropolitan communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".