Abstract 13611: Adherence to Non-VKA Oral Anticoagulant Medications Based on the Pharmacy Quality Alliance Measure
Bibliographic record
Abstract
Introduction: CMS Star Ratings help inform beneficiaries about the performance of health and drug plans. Medication adherence and other pharmacy measures are currently weighted at nearly half of a Part D plan’s Star Ratings. Two, 3, 4 and 5 Star Ratings are based on achievement of adherence threshold levels (e.g. 74%, 79%, 82%, and 85% for oral diabetes medications). Including the adherence to non-VKA oral anticoagulant (NOACs) as a measure in the Star Ratings program may increase a plan’s incentives to improve patient adherence. Objective: To assess the adherence to medication of patients who used the NOACs rivaroxaban, dabigatran, or apixaban in 2014 based on the Pharmacy Quality Alliance (PQA) adherence measure. Methods: Healthcare claims from the Humana database between 07/2013 and 12/2014 were analyzed. Adult patients with ≥2 dispensings of NOAC agents in 2014, at least 180 days apart between two NOAC dispensings in 2014 (a criterion to include chronic users), with >60 days of supply, and ≥180 days of continuous enrollment prior to the index NOAC were identified. PQA measure was calculated as the percentage of patients who had a proportion of days covered (PDC) ≥80%. PQA measure was compared between rivaroxaban and each of the other two groups using chi squared tests. Multivariate logistic regression analyses were also conducted adjusting for baseline confounders. Results: A total of 11,095 rivaroxaban, 6,548 dabigatran, and 3,532 apixaban users were identified. Based on the PQA adherence measure (PDC ≥0.8), a significantly higher proportion of rivaroxaban users (72.7%) was found to be adherent compared to dabigatran (67.2%: p<.001) and apixaban (69.5%: p<.001) users. Compared to apixaban users, the adjusted likelihood of being adherent was significantly higher for rivaroxaban users (unadjusted OR: 1.17, p-value<.001; adjusted OR: 1.20, p-value<.001), and significantly lower for dabigatran users (unadjusted OR: 0.90, p-value=0.019; adjusted OR: 0.85, p-value<.001). Conclusion: Using the PQA’s adherence measure, rivaroxaban users were found to have a significantly higher adherence compared to apixaban and dabigatran users. Healthcare providers should consider the impact of anticoagulation selection on their ability to achieve quality metrics.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.007 | 0.001 |
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".