Variations in the prevalence of atrial fibrillation, and in the strength of its association with ischemic stroke, in countries with different income levels: INTERSTROKE case–control study
Bibliographic record
Abstract
BACKGROUND: The contribution of atrial fibrillation (AF) to the etiology and burden of stroke may vary by country income level. AIMS: We examined differences in the prevalence of AF and described variations in the magnitude of the association between AF and ischemic stroke by country income level. METHODS: In the INTERSTROKE case-control study, participants with acute first ischemic stroke were recruited across 32 countries. We included 10,363 ischemic stroke cases and 10,333 community or hospital controls who were matched for age, sex, and center. Participants were grouped into high-income (HIC), upper-middle-income (subdivided into two groups-UMIC-1 and UMIC-2), and lower-middle-income (LMIC) countries, based on gross national income. We evaluated the risk factors for AF overall and by country income level, and evaluated the association of AF with ischemic stroke. RESULTS: AF was documented in 11.9% (n = 1235) of cases and 3.2% (n = 328) of controls. Compared to HIC, the prevalence of AF was significantly lower in UMIC-2 (aOR 0.35, 95% CI 0.29-0.41) and LMIC (aOR 0.50, 95% CI 0.41-0.60) on multivariable analysis. Hypertension, female sex, valvular heart disease, and alcohol intake were stronger risk factors for AF in lower-income countries, and obesity a stronger risk factor in higher-income countries. The magnitude of association between AF and ischemic stroke was significantly higher in lower-income countries compared to higher-income countries. The population attributable fraction for AF and stroke varied by region and was 15.7% (95% CI 13.7-17.8) in HIC, 14.6% (95% CI 12.3-17.1) in UMIC-1, 5.7% (95% CI 4.9-6.7) in UMIC-2, and 6.3% (95% CI 5.3-7.3) in LMIC. CONCLUSION: Risk factors for AF vary by country income level. AF contributes to stroke burden to a greater extent in higher-income countries than in lower-income countries, due to a higher prevalence and despite a lower magnitude of odds ratio.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".