Sex differences in the association of social determinants of health and adverse cardiovascular outcomes in patients with atrial fibrillation
Bibliographic record
Abstract
BACKGROUND: Despite anticoagulation, patients with atrial fibrillation (AF) experience persistent elevated cardiovascular risk, with conflicting evidence regarding sex-based outcome disparities. Social determinants of health (SDOH)-encompassing economic, psychosocial and environmental factors-demonstrate robust associations with cardiovascular outcomes and exhibit significant sex-specific patterns, yet remain understudied in AF populations. This study aimed to clarify sex differences in the association of SDOH and adverse cardiovascular outcomes in patients with AF. METHODS: Data came from the UK Biobank. Participants with AF enrolled between 2006 and 2010 were included. SDOH comprised economic, psychosocial and neighbourhood environmental factors. The primary outcome was a composite of major adverse cardiovascular events (ie, stroke/transient ischaemic attack, arterial thromboembolic events, myocardial infarction and cardiovascular mortality) and all-cause mortality. Sex-stratified, Cox proportional hazards models were used. RESULTS: Among 3842 participants (mean age 62.5±6.1 years; 35.1% female), males demonstrated higher adverse outcome event rates than females (29.1% vs 21.3%) over median 11.6-year follow-up. Multivariate analyses revealed independent SDOH associations with adverse outcomes, with distinct sex-specific patterns. In male participants, low income (HR 1.30, 95% CI 1.08 to 1.55), unemployment (HR 1.28, 95% CI 1.06 to 1.55), living alone (HR 1.29, 95% CI 1.07 to 1.55) and housing insecurity (HR 1.26, 95% CI 1.01 to 1.57) were associated with adverse outcomes, while emotional distress was the only predictor (HR 1.33, 95% CI 1.04 to 1.69) in females. CONCLUSIONS: SDOH demonstrate sex-specific associations with adverse cardiovascular outcomes in AF populations. Integration of SDOH into risk prediction algorithms may enhance cardiovascular risk stratification and inform targeted intervention strategies in AF management.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".