Global, regional, and national prevalence, incidence, mortality, and risk factors for atrial fibrillation, 1990–2017: results from the Global Burden of Disease Study 2017
Bibliographic record
Abstract
AIMS: To estimate the prevalence, incidence, mortality, and risk factors for atrial fibrillation (AF) in 195 countries and territories from 1990 to 2017. METHODS AND RESULTS: Following the methodologies used in the Global Burden of Disease Study 2017, the prevalence, incidence, and mortality of AF were analysed by age, sex, year, socio-demographic index (SDI), and location. The percentage contributions of major risk factors to age-standardized AF deaths were measured by population attributable fractions. In 2017, there were 37.57 million [95% uncertainty interval (UI) 32.55-42.59] prevalent cases and 3.05 million (95% UI 2.61-3.51) incident cases of AF globally, contributing to 287 241 (95% UI 276 355-304 759) deaths. The age-standardized rates of prevalent cases, incident cases, and deaths of AF in 2017 and their temporal trends from 1990 to 2017 varied significantly by SDI quintile and location. High systolic blood pressure was the leading risk factor for AF age-standardized deaths [34.3% (95% UI 27.4-41.5)] in 2017, followed by high body mass index [20.7% (95% UI 11.5-32.2)] and alcohol use [9.4% (95% UI 7.0-12.2)]. CONCLUSION: Our study has systematically and globally assessed the temporal trends of AF, which remains a major public heath challenge. Although AF mainly occurred in developed countries, the unfavourable trend in countries with lower SDI also deserves particular attention. More effective prevention and treatment strategies aimed at counteracting the increase in AF burden should be established in some countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".