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 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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".