Impact of increased patient out-of-pocket costs on oral anticoagulant discontinuation among Medicare beneficiaries with atrial fibrillation treated with apixaban
Bibliographic record
Abstract
Objective To characterize the change in apixaban out-of-pocket (OOP) costs from 2016 to 2017 (after a formulary tier increase) and to assess the association between increased OOP costs and treatment discontinuation among Medicare beneficiaries with atrial fibrillation (AF).Methods Medicare fee-for-service claims data (2012-2019) were used to conduct a retrospective cohort study on adult patients with AF who experienced an increase in apixaban OOP costs from 2016 to 2017 due to formulary tier increase. Discontinuation was defined as apixaban treatment gap of >30 consecutive days without switching to another oral anticoagulant. Multivariable Cox proportional hazards models were used to identify factors associated with treatment discontinuation.Results Among 1,153 patients treated with apixaban who experienced increased OOP costs from 2016 to 2017, 321 (27.8%) discontinued treatment in 2017 (mean age 78.3 years, 58.9% male, 92.8% White, 38.6% from the South). From 2016 to 2017, the mean (standard deviation) OOP costs for apixaban increased from $76.61 ($40.75) to $162.41 ($60.16) per month (mean change: $85.80 [$57.12]), which was significantly higher among patients who discontinued treatment than those who did not ($93.42 [$61.40] vs $82.87 [$55.13], respectively; p < 0.05). After multivariable adjustment, each $50 increase in monthly OOP costs was associated with a 1.27 times higher likelihood of treatment discontinuation (p < 0.05).Conclusion Following the formulary tier increase of apixaban from 2016 to 2017, increases in OOP costs was associated with a significantly higher probability of treatment discontinuation among Medicare beneficiaries with AF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".