Effect of the COVID-19 pandemic on adversity in individuals receiving anticoagulation for atrial fibrillation: A nationally representative administrative health claims analysis
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) is strongly associated with clinical adversity, including increased hospitalization and bleeding and stroke events. We examined the effect of the SARS-2 Coronavirus 2019 (COVID-19) pandemic on such events in individuals with AF receiving oral anticoagulation. METHODS: We employed medical and pharmacy claims spanning 2018-2020 from a nationally representative U.S. database (IQVIA Longitudinal Prescription, Medical Claims, and Institutional Claims). We selected individuals receiving oral anticoagulation in 2018 for AF and followed them from 1/1/2019-7/8/2020 for clinical events. We constructed interrupted time-series analyses across 30-day intervals with Poisson regression models to determine the effect of the COVID-19 pandemic on clinical events. RESULTS: The dataset included 1,439,145 individuals (half with age ≥75 years; 47.6% women) receiving oral anticoagulation. We determined a 19% decrease in emergency room visits following the pandemic declaration and 8% decrease in inpatient admissions. In contrast admissions for stroke and bleeding were not affected by the declaration of the pandemic. DISCUSSION: These results describe the temporal effect of the COVID-19 pandemic on clinical adversity - hospitalizations, strokes, and bleeding events - in individuals receiving oral anticoagulation for AF. Our analysis quantifies the decrease in clinical adversity accompanying COVID-19 in a large, highly representative U.S. health claims database.
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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 | 0.034 | 0.213 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".