Paradoxical impact of socioeconomic factors on outcome of atrial fibrillation in Europe: trends in incidence and mortality from atrial fibrillation
Bibliographic record
Abstract
AIMS: The aim of this study was to understand the changing trends in atrial fibrillation (AF) incidence and mortality across Europe from 1990 to 2017, and how socioeconomic factors and sex differences play a role. METHODS AND RESULTS: We performed a temporal analysis of data from the 2017 Global Burden of Disease Database for 20 countries across Europe using Joinpoint regression analysis. Age-adjusted incidence, mortality, and mortality-to-incidence ratios (MIRs) to approximate case fatality rate are presented. Incidence and mortality trends were heterogenous throughout Europe, with Austria, Denmark, and Sweden experiencing peaks in incidence in the middle of the study period. Mortality rates were higher in wealthier countries with the highest being Sweden for both men and women (8.83 and 8.88 per 100 000, respectively) in 2017. MIRs were higher in women in all countries studied, with the disparity increasing the most over time in Germany (43.6% higher in women vs. men in 1990 to 74.5% higher in women in 2017). CONCLUSION: AF incidence and mortality across Europe did not show a general trend, but unique patterns for some nations were observed. Higher mortality rates were observed in wealthier countries, potentially secondary to a survivor effect where patients survive long enough to suffer from AF and its complications. Outcomes for women with AF were worse than men, represented by higher MIRs. This suggests that there is widespread healthcare inequality between the sexes across Europe, or that there are biological differences between them in terms of their risk of adverse outcomes from AF.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".