Adherence to non-vitamin-K-antagonist oral anticoagulant medications based on the Pharmacy Quality Alliance measure
Bibliographic record
Abstract
BACKGROUND: CMS Star Ratings help inform beneficiaries about the performance of health and drug plans. Medication adherence is currently weighted at nearly half of a Part D plan's Star Ratings. Including the adherence to non-vitamin-K-antagonist oral anticoagulants (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 July 2013 and December 2014 were analyzed. Adult patients with ≥2 dispensings of NOAC agents in 2014, at least 180 days apart, with >60 days of supply, and ≥180 days of continuous enrollment prior to the index NOAC were identified. The PQA measure was calculated as the percentage of patients who had a proportion of days covered (PDC) ≥0.8. Multivariate logistic regression analyses were also conducted adjusting for baseline confounders. RESULTS: A total of 11,095 rivaroxaban, 6548 dabigatran, and 3532 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 < 0.001) and apixaban (69.5%: p < 0.001) users. Compared to apixaban users, the adjusted likelihood of being adherent was significantly higher for rivaroxaban users (unadjusted OR [95% CI]: 1.17 [1.08-1.27], p < 0.001; adjusted OR [95% CI]: 1.20 (1.10-1.31), p < 0.001) and significantly lower for dabigatran users (unadjusted OR [95% CI]: 0.90 [0.82-0.98], p = 0.019; adjusted OR [95% CI]: 0.85 [0.77-0.93], p < 0.001). LIMITATIONS: Limitations of the study are potential inaccuracies in claims data, possible change in patterns over time, and the impossibility of knowing whether all supplied tablets were taken. CONCLUSION: Using the PQA's adherence measure, rivaroxaban users were found to have significantly higher adherence compared to apixaban and dabigatran users.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".