Prior Authorization, Copayments, and Utilization of Sacubitril/Valsartan in Medicare and Commercial Plans in Patients With Heart Failure With Reduced Ejection Fraction
Bibliographic record
Abstract
Background: Slow uptake of sacubitril/valsartan in patients with heart failure with reduced ejection fraction has been reported, which may negatively impact clinical outcomes. We characterized prior authorization (PA) burden, prescription copayment, and utilization of sacubitril/valsartan by insurance plan type to identify potential barriers to its use. Methods: We conducted a national population-level, cross-sectional study using PA data from an insurance coverage website accessed in March 2019 and IQVIA National Prescription Audit data from August 2018 to July 2019. Primary outcomes were proportion of plans requiring PA, frequency of specific PA criteria, number of sacubitril/valsartan prescriptions, and copayments per insurance plan type. Results: Overall, 48.1% (1394/2896) of insurance plans required PA for sacubitril/valsartan. Fewer Medicare (27.7%) than commercial (57.2%) plans required PA ( P <0.001). For both plan types, the most frequently required PA criteria were ejection fraction (71.6%, 90.9%) and New York Heart Association class (60.4%, 90.8%) for Medicare and commercial plans, respectively. Copayment amounts varied by plan type, with more sacubitril/valsartan prescriptions for commercial plans not requiring a patient copayment (32.4%) compared with Medicare plans (19.3%; P <0.001). There were 814 437 sacubitril/valsartan prescriptions for Medicare and 822 292 for commercial plans dispensed from August 2018 to July 2019. Based on estimated heart failure with reduced ejection fraction populations for each plan type, 4-fold more sacubitril/valsartan prescriptions were dispensed in commercial than in Medicare plans (820 versus 215 prescriptions/1000 individuals in the heart failure with reduced ejection fraction population). The estimated proportion of heart failure with reduced ejection fraction patients prescribed sacubitril/valsartan was 3.6% (1.5%–6.8%) for Medicare and 13.7% (4.9%–31.8%) for commercial plan populations. Conclusions: Despite commercial plans having greater PA requirements than Medicare, population-adjusted use of sacubitril/valsartan was higher in commercial plans. Given that commercial plans had more prescriptions with low copayments than Medicare, copayment policies may be more influential on sacubitril/valsartan use than its PA policies. Low sacubitril/valsartan use in both plan types highlights the multifactorial nature of medication underutilization that includes factors beyond the drug policies that we evaluated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".