Psychosocial Stressors at Work and Atrial Fibrillation Incidence: An 18‐Year Prospective Study
Bibliographic record
Abstract
BACKGROUND: Psychosocial stressors at work, defined by the job strain and effort-reward imbalance at work (ERI) models, were shown to increase coronary heart disease risk. No previous study has examined the adverse effect of psychosocial stressors at work from both models on atrial fibrillation (AF) incidence. The objective of this study was to examine the separate and combined effect of psychosocial stressors at work from the job strain and ERI models on AF incidence in a prospective cohort study. METHODS AND RESULTS: A total of 5926 white-collar workers (3021 women and 2905 men) free of cardiovascular disease at baseline were followed for an average of 18 years. Job strain (high psychological demands combined with low decision latitude) and ERI were assessed using validated instruments. AF events were identified in medical databases with universal coverage. Hazard ratios (HRs) with 95% CIs were estimated using Cox regression models, controlling for socioeconomic characteristics and lifestyle-related and clinical risk factors. A total of 186 AF incident events were identified over 18 years. Workers exposed to job strain (HR, 1.83 [95% CI, 1.14-2.92]) and ERI (HR, 1.44 [95% CI, 1.05-1.98]) had a higher risk of AF in fully adjusted models. Combined exposure to job strain and ERI was associated with a 2-fold AF risk increase (HR, 1.97 [95% CI, 1.26-3.07]). CONCLUSIONS: Psychosocial stressors at work from the job strain and ERI models are associated with an increased risk of AF, separately and in combination. Workplace prevention strategies targeting these psychosocial stressors at work may be effective to reduce the burden associated with 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".