Regional disparities in the care and outcomes of atrial fibrillation patients in a universal health care system: a population-based cohort study
Bibliographic record
Abstract
Abstract Introduction While prior studies have shown regional disparities in patients with myocardial infarction and heart failure within a universal health care, there are very limited data on the association between different regions within a universal health care and access to care and outcomes after atrial fibrillation (AF) diagnosis. We aimed to assess variations in processes of care and outcomes among patients with new diagnosis of AF presenting to the emergency department (ED) in different regions within a universal health care system. Methods We conducted a population-based retrospective cohort study of all adult patients (≥18 years) with the first diagnosis of AF presenting to the ED between April 1, 2012, and March 31, 2022 in Ontario, Canada. We divided the analyses into 5 major health regions in Ontario (Ontario Health (OH) Regions: North (East and West), East, Central, Toronto and West). Medicare in Canada provides all funding for essential services and prohibits private health care. The primary outcome was the composite of all-cause mortality or admission. Secondary outcomes included all-cause mortality, all-cause admission and all-cause ED visit. Cox proportional hazards regression analysis was used to study the association of different regions and outcomes. Results Among 104,383 patients with new diagnosis of AF in the ED (mean age 69.4 years, 47.1% female), there were significant differences, within 1 year, between OH regions (North as a reference) in physician follow up (primary care or cardiologist), and procedures (cardioversion or ablation) (p<0.001). In comparison to the North, there was a significant difference in the primary outcome of all-cause mortality or admission [East HR 0.87 (0.83, 0.90) , Central HR 0.87 (0.83, 0.91), Toronto HR 0.87 (0.84, 0.92) and West HR 0.87 (0.84, 0.91)] (figure and map). Similar findings noted with higher all-cause admission and all-cause ED visit in the North but all-cause mortality did not differ between regions (figure). Conclusions Despite universal health care and prescription medication coverage, regional variations exist among AF patients, as those in Northern Ontario were less likely to visit a primary care physician or a cardiologist or receive rhythm control management and had worse outcomes driven by higher admission rates after AF diagnosis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".