Atrial fibrillation in the Indigenous populations of Australia, Canada, New Zealand, and the United States: a systematic scoping review
Bibliographic record
Abstract
BACKGROUND: The epidemiology of atrial fibrillation (AF) among Indigenous minorities in affluent countries is poorly delineated, despite the high cardiovascular disease burden in these populations. We undertook a systematic scoping review examining the epidemiology of AF in the Indigenous populations of Australia, Canada, New Zealand (NZ) and the United States (US). METHODS: PubMed, Scopus, EMBASE and CINAHL-Plus databases were systematically searched in May 2014. Supplementary full-text searches of Google Scholar and government website searches were also undertaken. RESULTS: Key findings from 27 publications with diverse aims and methods were included. Small studies from Canada and NZ suggest higher AF prevalence in Indigenous than other populations. However, this was not reflected in a large sample of US male military veterans. No data were identified on community-based incidence rates of AF in Indigenous populations. Australian and Canadian studies indicate higher first-ever and overall AF hospitalisation rates among Indigenous than other populations, at younger ages and with more comorbidity. Studies in stroke, heart failure and other clinical groups demonstrate AF as a common comorbidity, with AF possibly more prevalent at younger ages in Indigenous people. Indigenous patients have similar early post-hospitalisation adjusted mortality but higher 1-year risk-adjusted mortality than non-Indigenous patients. CONCLUSIONS: No clear epidemiological pattern of AF frequency across the considered Indigenous populations emerges from the limited available evidence. AF should be included in key conditions reported in national surveillance reports, although Indigenous identifiers are required in administrative data from Canada and the US. Sufficiently powered, community-based studies of AF epidemiology in diverse Indigenous populations are needed.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".