COVID‐19 and Anticoagulation for Atrial Fibrillation: An Analysis of US Nationwide Pharmacy Claims Data
Bibliographic record
Abstract
Background Adherence to oral anticoagulation (OAC) is critical for stroke prevention in atrial fibrillation. However, the COVID‐19 pandemic may have disrupted access to such therapy. We hypothesized that our analysis of a US nationally representative pharmacy claims database would identify increased incidence of lapses in OAC refills during the COVID‐19 pandemic. Methods and Results We identified individuals with atrial fibrillation prescribed OAC in 2018. We used pharmacy dispensing records to determine the incidence of 7‐day OAC gaps and 15‐day excess supply for each 30‐day interval from January 1, 2019 to July 8, 2020. We constructed interrupted time series analyses to test changes in gaps and supply around the pandemic declaration by the World Health Organization (March 11, 2020), and whether such changes differed by medication (warfarin or direct OAC), prescription payment type, or prescriber specialty. We identified 1 301 074 individuals (47.5% women; 54% age ≥75 years). Immediately following the COVID‐19 pandemic declaration, we observed a 14% decrease in 7‐day OAC gaps and 56% increase in 15‐day excess supply (both P <0.001). The increase in 15‐day excess supply was more marked for direct OAC (69% increase) than warfarin users (35%; P <0.001); Medicare beneficiaries (62%) than those with commercial insurance (43%; P <0.001); and those prescribed OAC by a cardiologist (64%) rather than a primary care provider (48%; P <0.001). Conclusions Our analysis of nationwide claims data demonstrated increased OAC possession after the onset of the COVID‐19 pandemic. Our findings may have been driven by waivers of early refill limits and patients’ tendency to stockpile medications in the first weeks of the pandemic.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| 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".