Geographic Clustering of Emergency Department Presentations for Atrial Fibrillation and Flutter in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVES: Atrial fibrillation and flutter (AFF) are the most common arrhythmias seen in the outpatient setting, and they affect more than 300,000 adult Canadians. The aims of this study were to examine temporal and geographic trends in emergency department (ED) presentations made by adults (age ≥ 35 years) for AFF in Alberta, Canada, from 1999 to 2011. Statistical disease cluster detection techniques were used to identify geographic areas with higher numbers of individuals presenting with AFF and higher numbers of ED presentations for AFF than expected by chance alone. Geographic clusters of individuals with stroke or heart failure follow-up within 365 days of ED presentations for AFF were also identified. METHODS: All ED presentations for AFF made by individuals aged ≥35 years were extracted from Alberta's Ambulatory Care Classification System. The Alberta Health Care Insurance Plan provided population counts and demographics for the patients presenting (age, sex, year, geographic unit). The Physician Claims File provided non-ED physician claims data after a patient's ED presentation. Statistical analyses included numerical and graphical summaries, directly standardized rates, and statistical disease cluster detection tests. RESULTS: During 12 years, there were 63,395 ED presentations for AFF made by 32,101 individuals. Standardized rates remained relatively stable over time, at about two per 1,000 for individuals presenting to the ED for AFF and about three per 1,000 for ED presentations for AFF. The northern and southeastern parts of the province were identified as clusters of individuals presenting for AFF, and ED presentations for AFF, and several of the areas demonstrated clusters in multiple years. Further, several of the geographic clusters were also identified as potential clusters for stroke or heart failure within 365 days after the ED presentations for AFF. CONCLUSIONS: This population-based study spanned 12 fiscal years and showed variations in the number of people presenting to EDs for AFF and the number of ED presentations for AFF over geography. The potential clusters identified may represent geographic areas with higher disease severity or a lower availability of non-ED health services. The clusters are not all likely to have occurred by chance, and further investigation and intervention could occur to reduce ED presentations for AFF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".