Abstract
IMPORTANCE
Although PCSK9 inhibitors (PCSK9i) were approved in 2015, their high cost has led to strict prior authorization practices and high copays, and use of PSCK9i in clinical practice has been low.
OBJECTIVE
To evaluate patient access to PCSK9i among those prescribed therapy.
DESIGN, SETTING, AND PARTICIPANTS
Using pharmacy transaction data, we evaluated 45,029 patients newly prescribed PCSK9i in the United States between 08/01/2015 and 07/31/2016.
MAIN OUTCOMES AND MEASURES
Main outcomes were: the proportion of PCSK9i prescriptions approved and abandoned (approved but unfilled); multivariable analyses examined factors associated with approval/abandomnent including payor, prescriber specialty, pharmacy benefit manager (PBM), out-of-pocket cost (copay), clinical diagnoses, lipid lowering medication use, and low density lipoprotein cholesterol (LDL-C) levels.
RESULTS
Of patients given an incident PCSK9i prescription, 20.8% received approval on the first day, and 47.2% ever received approval. Of those approved, 65.3% filled the prescription, resulting in 30.9% of those prescribed PCSK9i ever receiving therapy. After adjustment, patients who were older, male and had ASCVD were more likely to be approved, but approval rates did not vary by patient low density lipoprotein (LDL-C) level nor statin use. Other factors associated with drug approval included: having government versus commercial insurance (odds ratio [OR] 3.3, 95% confidence interval [CI] 2.8–3.8), and those filled at a specialty versus retail pharmacy (OR 1.96, 95% CI 1.66–2.33). Approval rates varied nearly 3-fold among the top 10 largest PBMs. Prescription abandonment by patients was most associated with copay costs (c-statistic 0.86); with abandonment rates ranging from 7.5% for those with $0 copay, to more than 75% for copays greater than $350. Limitations include that the analysis was conducted in the first year of availability before outcomes trials results.
CONCLUSIONS AND RELEVANCE
In the first year of availability, only half of patients prescribed a PCSK9 received approval, and one third of approved prescriptions were never filled due to copay.
Keywords: PCSK9 inhibitors, patient access, pre-approval process, copays
Since 2015, two PCSK9is, alirocumab and evolocumab, have been approved for adults with persistently elevated low density lipoprotein cholesterol (LDL-C) levels despite maximally tolerated statin therapy and those with familial hypercholesterolemia.1,2 The retail cost for these PCSK9i can be as much as $14,000 per year, leading health insurers and pharmacy benefit managers (PBMs) to implement utilization management processes including prior authorization and patient therapy co-pays. To date, limited empirical information is available on how these pre-authorization processes and copays jointly affect access to PCSK9i in community practice.
In this analysis, we evaluated what proportion of patients prescribed PCSK9i ultimately received therapy, and factors associated with both approval and dispensing in the first year after PCSK9i were approved by the Food and Drug Administration (FDA). Specifically, we calculated the proportion of patients who received a rejection initially (within 24 hours), as well as the proportion who ultimately received approval. We also examined the proportion of approved prescriptions that were filled versus left at the pharmacy (abandoned), and determined the duration between initial prescription and medication dispense. Next, we examined what factors were associated with an increased odds of receiving therapy. Finally, we examined the association between patient copay and prescription abandonment.
Methods
Data Description
Using pharmacy claims transactional data from Symphony Health Solutions (SHS), we evaluated new PCSK9i prescriptions from August 1, 2015 through July 31, 2016. The SHS database captures full life cycle pharmacy claims data from initial submission of a prescription through its final disposition from more than 90% of retail, 60% of mail order, and 70% of specialty pharmacies in the United States. Pharmacy-level transmissions are date- and time-stamped, and include whether: 1) the claim was rejected or approved; 2) the patient filled the prescription (“dispensed”) or left it at the pharmacy after it was approved (“abandoned”); and 3) secondary insurance or a coupon program was used to defray the patient’s copay. Available patient characteristics include sex, age, payor(s), PBM(s), prescribing provider taxonomy code, and pharmacy type used. The payor associated with a patient was determined by evaluating all payors for which prescription claims were submitted for a given patient during the prescription episode. Payor types were split into commercial or government (including Veterans Affairs, Tricare, Medicare, Managed Medicaid, and Medicaid).
Pharmacy transaction data at the patient level were linked to electronic health record (EHR) and claims data for a subset of patients. Among patients with EHR and claims linkages, we identified adults with a diagnosis of prior atherosclerotic cardiovascular disease (ASCVD) on the day of or prior to the first prescription for a PCSK9i. ASCVD was defined as a diagnosis of coronary artery disease, cerebrovascular disease (prior stroke, transient ischemic attack, and carotid stenosis), peripheral arterial disease, or other atherosclerotic vascular disease on the day of or prior to the PCSK9i prescription. See appendix table 1 for ICD-9 and 10 codes used to classify patient diagnoses. Medication data were used to identify adults with an active prescription for a statin, a high-intensity statin (atorvastatin >= 40 mg or rosuvastatin >=20 mg), ezetimibe, or other lipid lowering therapy at the time of PCSK9i prescription. Follow-up was available from August 1, 2015 through August 31, 2016.
The unit of analysis was at the patient-level, since patients could be issued multiple or redundant prescriptions for the same medication, could be prescribed two different PCSK9i medications, and because individual prescriptions were often submitted and resubmitted multiple times in succession, either for administrative reasons (e.g. wrong date of birth) or clinical appeals. The first transaction for any PCSK9i prescription for a patient was considered the prescribing date, with the “first prescription episode” defined as the time between the prescribing date and first PCSK9i dispense for those who received medication, the time between the prescribing date and first approval for abandoned prescriptions, and the time between prescribing date and last rejection for those who were never approved.
Factors associated with approval
We evaluated ultimate approval and dispense rates, as well as approval rates in the first 24 hours after a prescription was submitted, for all prescriptions between August 1, 2015 and July 31, 2016. Approval rates in the first 24 hours were utilized to determine the proportion of prescriptions filled on initial attempt vs. those that are approved later with appeals or resubmissions of claims over time. Next, differences in approval rates were evaluated by pharmacy type (retail, mail order, institutional, or specialty), payor type (government, commercial, government and commercial, or other/none), and provider type (cardiology, endocrinology, general practice [including family medicine and internal medicine], and other), which were defined by provider taxonomy codes. Multivariable logistic regression modeling was used to evaluate factors associated with receiving approval for medication, and included age, sex, payor type, prescriber specialty, and pharmacy type. In addition, to evaluate differences in approval rates by PBM, the model included indicator variables for the top 10 PBMs volume of patients prescribed PSCK9i therapy in the sample.
In order to evaluate the impact of clinical factors and medication use on approvals, the multivariable model of approvals was re-run for the subsample of patients on whom clinical and medication data were available. A further subset of patients had linked laboratory data available with at least one LDL-C value in the prior year. Approval rates by most recent LDL-C level were evaluated for this subset of patients by evaluating the odds of approval at different LDL-C levels adjusting for patient demographics, clinical characteristics, and payor factors as in prior logistic regression models. In order to assess the degree to which clinical, medication, and laboratory data impact approvals, the c-statistics from this full model and a model without diagnoses, laboratory, and medication data were calculated.
Factors associated with dispensing after approval
To allow for comparison of out-of-pocket costs when variable medication quantities were issued to patients, “copay” was defined as a patient’s out-of-pocket cost for a 1-month supply of medication after any insurance claim, coupon, or patient assistance programs. The distributions of patient copays for dispensed and abandoned prescriptions were compared using the Wilcoxon rank-sum test, as were copays for prescriptions filled with and without the assistance of a coupon program. To evaluate the relationship between copay and abandonment (prescription approved but not picked up at the pharmacy), the proportion of prescriptions dispensed (vs. abandoned) by percentile of copay was evaluated for approved prescriptions. Next, a multivariable logistic regression model was created to evaluate the association between copay and prescription dispensing (vs. abandonment), first alone and then after adjusting for age, sex, payor type, prescriber specialty, pharmacy type, and time to first approval. Due to the nonlinear relationship between patient copay and prescription abandonment, the association between copay and prescription dispensing was modeled using restricted cubic splines with knots at the 40th, 60th, and 80th percentiles. Since copay is modeled with restricted cubic splines, odds ratios (ORs) for specific copays are displayed. The c-statistic for this full multivariable logistic regression model was calculated, in addition to the c-statistic for a model using only copay in order to determine the degree to which copay alone impacted prescription abandonment.
This study was approved by the Duke University Institutional Review Board (Pro00078196). All statistical analyses were performed using SAS version 9.4 (SAS Institute, Inc., Cary, NC) and STATA SE 14.2 (College Station, TX).
Results
Population Description and Rates of Rejection, Approvals, and Dispensing
We analyzed 45,029 patients initially prescribed a PCSK9i between August 1, 2015 and July 31, 2016 in the SHS data. In the first day following submission of the incident prescription, n=35,658 (79.2%) were initially rejected and 20.8% were approved. Of those rejected, 73.5% (n=26,193) were appealed or resubmitted, after which an additional 11,888 (45.4% of appeals, 26.4% of all patients) received approval, leading to an ultimate approval rate of 47.2% (Figure 1). Among 21,259 patients who received approval, 7,368 (34.7% of those who received approval) never picked up the medication and, therefore, were considered “abandoned”. Ultimately, 30.9% of patients initially prescribed a PCSK9i actually received therapy. (Figure 1). The median time between initial submission and approval was 3.9 days (IQR 0–20.0). However, approval times were more prolonged for other patients; 17.2% of patients waited 30 days or more, the top 10% of patients waited at least 52 days, and the top 5% waited 87 days or longer for approval (Supplement figure 1).
Figure 1. Rejections, Dispenses, and Abandonment in Patients Initially Prescribed PCSK9i.
This figure displays the rejections, dispenses, and abandonment in patients initially prescribed PCSK9i. Abbreviations: PCSK9i, proprotein convertase subtilisin/kexin type 9 inhibitors
Table 1 shows characteristics of patients prescribed PCSK9i by those receiving initial (first day) prescription approval and those who received ultimate approval. The majority (52.5%) of patients had government insurance, and of those with government insurance 89.8% had Medicare. Commercial insurers covered 40.0% of patients, and 4.5% had both commercial and government insurance. Retail pharmacies were most commonly used (78.2%), though nearly one-fifth of prescriptions were filled through a specialty pharmacy. A total of 18,398 unique providers wrote prescriptions for PSCK9i, with median of 1 [IQR 1–3] patients per provider. Nearly half of those patients were prescribed therapy by a cardiologist (48.3%), followed by general practitioners (36.8%).
Table 1.
Characteristics of Patients Prescribed PCSK9i, First Day and Ultimate Approval Rates
| Parameters | Number of Patients (% sample) | Patients Approved Day 1 N (% row) | Patients Ever Receiving Approval N (% row) | |
|---|---|---|---|---|
| Overall Sample | 45,029 (100%) | 9371 (20.8%) | 21259 (47.2%) | |
| Age | ||||
| <45 years | 1702 (3.8%) | 251 (14.7%) | 469 (27.6%) | |
| 45–54 years | 5239 (11.6%) | 814 (15.5%) | 1736 (33.1%) | |
| 55–64 years | 12,591 (28.0%) | 2084 (16.6%) | 4441 (35.3%) | |
| 65–74 years | 16,660 (37.0%) | 4041 (24.3%) | 9435 (56.6%) | |
| 75+ years | 8837 (19.6%) | 2181 (24.7%) | 5178 (58.6%) | |
| Sex | Women | 23,065 (51.2%) | 4788 (20.8%) | 10905 (47.3%) |
| Men | 21,964 (48.8%) | 4583 (20.9%) | 10354 (47.1%) | |
| Pharmacy | Retail | 35234 (78.2%) | 6173 (17.5%) | 14882 (42.2%) |
| Institutional | 239 (0.5%) | 37 (15.5%) | 102 (42.7%) | |
| Mail Order Pharmacy | 255 (0.6%) | 199 (78.0%) | 218 (85.5%) | |
| Specialty | 9300 (20.7%) | 2962 (31.8%) | 6057 (65.1%) | |
| Provider | ||||
| Cardiologist | 21,767 (48.3%) | 4844 (22.3%) | 11485 (52.8%) | |
| General Practitioner | 16,593 (36.8%) | 3112 (18.8%) | 6740 (40.6%) | |
| Endocrinologist | 2234 (5.0%) | 428 (19.2%) | 997 (44.6%) | |
| Other Provider | 4435 (9.8%) | 987 (22.3%) | 2037 (45.9%) | |
| PBM | ||||
| PBM 1 | 3303 (7.3%) | 1118 (33.8%) | 2211 (66.9%) | |
| PBM 2 | 500 (1.1%) | 98 (19.6%) | 195 (39.0%) | |
| PBM 3 | 1358 (3.0%) | 176 (13.0%) | 519 (38.2%) | |
| PBM 4 | 8196 (18.2%) | 1346 (16.4%) | 3233 (39.4%) | |
| PBM 5 | 8540 (19.0%) | 1298 (15.2%) | 3112 (36.4%) | |
| PBM 6 | 8054 (17.9%) | 1943 (24.1%) | 5383 (66.8%) | |
| PBM 7 | 1062 (2.4%) | 170 (16.0%) | 478 (45.0%) | |
| PBM 8 | 8163 (18.1%) | 1371 (16.8%) | 3632 (44.5%) | |
| PBM 9 | 2833 (6.3%) | 430 (15.2%) | 1087 (38.4%) | |
| PBM 10 | 617 (1.4%) | 18 (2.9%) | 156 (25.3%) | |
| Payor | Commercial | 17,999 (40.0%) | 2489 (13.8%) | 5111 (28.4%) |
| Government | 23,652 (52.5%) | 5767 (24.4%) | 14236 (60.2%) | |
| Government and | 332 (16.3%) | 1084 (53.1%) | ||
| Commercial | 2043 (4.5%) | |||
| Other | 1335 (3.0%) | 783 (58.7%) | 828 (62.0%) | |
| Clinical | Overall Clinical | |||
| Subsample | 17,851 | 3688 (20.7%) | 8738 (48.9%) | |
| Any ASCVD | 12,186 (68.3%) | 2657 (21.8%) | 6362 (52.2%) | |
| CAD | 10,869 (60.9%) | 2359 (21.7%) | 5708 (52.5%) | |
| PAD | 3262 (18.3%) | 753 (23.1%) | 1780 (54.6%) | |
| CVD | 4235 (23.7%) | 952 (22.5%) | 2296 (54.2%) | |
| Statin | High-Intensity | 2071 (11.6%) | 425 (20.5%) | 1012 (48.9%) |
| Low to Moderate | 393 (19.1%) | 1001 (48.8%) | ||
| Intensity | 2053 (11.5%) | |||
| No Statin | 13727 (76.9%) | 2870 (20.9%) | 6725 (49.0%) | |
| Ezetimibe | Yes | 1874 (10.5%) | 389 (20.8%) | 972 (51.9%) |
| No | 15,977 (89.5%) | 3299 (20.6%) | 7766 (48.6%) | |
| Other LLT | Yes | 2072 (11.6%) | 445 (21.5%) | 1054 (50.9%) |
| No | 15,779 (88.4%) | 3243 (20.6%) | 7684 (48.7%) | |
| Labs | Overall Lab Subsample | 5383 (100.0%) | 1089 (20.2%) | 2637 (49.0%) |
| <70 | 548 (10.2%) | 139 (25.4%) | 286 (52.2%) | |
| 70–99 | 726 (13.5%) | 160 (22.0%) | 368 (50.7%) | |
| 100–129 | 1031 (19.2%) | 219 (21.2%) | 528 (51.2%) | |
| 130–159 | 1246 (23.1%) | 228 (18.3%) | 621 (49.8%) | |
| 160–189 | 941 (17.5%) | 174 (18.5%) | 435 (46.2%) | |
| >=190 | 891 (16.6%) | 169 (19.0%) | 399 (44.8%) |
Among those with clinical data available (n=17,851, 39.6% of the sample), 68.3% had a diagnosis of atherosclerotic cardiovascular disease, the most common of which was coronary artery disease (60.9%, Table 1). Lipid lowering therapy use was infrequent at the time of PCSK9i prescription with only 23.1% on a statin (11.6% high intensity and 11.5% low-moderate intensity), 10.5% on ezetimibe, and 11.6% on another form of lipid lowering therapy. In the subsample of patients with lab data available and an LDL-C value from the prior year (n=5,383), 16.6% had an LDL-C over 190 mg/dL, 17.5% between 160–189 mg/dL, and 10.2% had an LDL-C below 70 mg/dL. Of note, patient characteristics as well as approval rates were generally similar among those who had clinical and/or laboratory data available as was seen in the overall patient sample (Supplement Table 2)
Factors Associated with Receiving Ultimate Prescription Approval
In multivariable modeling, several demographic, clinical and payor factors affected the likelihood that a PCSK9i prescription would be approved. Table 2 provides the odds ratios for ultimate approval among the subsample of patients with laboratory and clinical data available (n=5,383), however the magnitude and direction of these associations were similar in the overall patient sample and among those with clinical data only (supplemental table 3). Patients who were older and male were more likely to have their prescription approved than their counterparts. Adults with prior ASCVD were more likely to receive approval for therapy than adults without a diagnosis of ASCVD (OR 1.18, 95% CI 1.04–1.35) yet there was no difference in odds of approval seen in those on a high intensity statin (versus no statin, OR 1.0, 95% CI 0.9–1.2), a low/moderate intensity statin (vs no statin, OR 1.1, 95% CI 0.9–1.4), nor by the patient’s most recent LDL-C level to the time of PCSK9i prescription. Those on ezetimibe were more likely to receive approval (OR 1.3, 95% CI 1.1–1.6).
Table 2.
Predictors of Approval Among Patients with Laboratory, Clinical, and Medication Data
| N=5,383 Patients | Multivariable Results for Ever Receiving Approval OR (95% CI) |
|---|---|
| Age (Ref: <45) | |
| 45–54 | 1.31 (0.87 – 1.98) |
| 55–64 | 1.48 (1.00 – 2.19) |
| 65–74 | 1.90 (1.28 – 2.82) |
| 75+ | 1.79 (1.19 – 2.69) |
| Women vs. Men | 0.95 (0.84 – 1.08) |
| Pharmacy | |
| Specialty vs. Retail | 1.96 (1.66 – 2.33) |
| Mail Order | 6.43 (2.67 – 15.50) |
| Institutional | 4.98 (1.83 – 13.60) |
| Specialty (Ref: General) | |
| Cardiologist | 1.42 (1.24 – 1.63) |
| Endocrinologist | 1.03 (0.75 – 1.42) |
| Other | 1.37 (1.08 – 1.73) |
| Payor (Ref: Commercial) | |
| Government | 3.27 (2.79 – 3.83) |
| Gov + Commercial | 2.6 (1.95 – 3.46) |
| PBM (Ref: None of top 10) | |
| PBM 1 | 2.32 (1.73 – 3.13) |
| PBM 2 | 1.15 (0.67 – 1.98) |
| PBM 3 | 0.95 (0.66 – 1.37) |
| PBM 4 | 0.75 (0.6 – 0.93) |
| PBM 5 | 0.76 (0.61 – 0.94) |
| PBM 6 | 1.19 (0.94 – 1.5) |
| PBM 7 | 1.32 (0.92 – 1.92) |
| PBM 8 | 0.77 (0.62 – 0.95) |
| PBM 9 | 1.22 (0.92 – 1.62) |
| PBM 10 | 0.54 (0.32 – 0.9) |
| Indication | |
| Prior ASCVD v. Primary | 1.18 (1.04 – 1.35) |
| Medication | |
| High Intensity Statin v. No Statin | 1.03 (0.85 – 1.24) |
| Low/Moderate Statin v. No Statin | 1.14 (0.94 – 1.38) |
| Ezetimibe v. No | 1.29 (1.07 – 1.56) |
| Other LLT vs. No | 1.22 (1.01 – 1.48) |
| Last LDL-C (Ref: <70 mg/dL) | |
| 70–99 | 0.93 (0.73 – 1.19) |
| 100–129 | 0.94 (0.74 – 1.18) |
| 130–159 | 0.92 (0.73 – 1.15) |
| 160–189 | 0.89 (0.7 – 1.12) |
| 190+ | 0.9 (0.71 – 1.15) |
Patients with prescriptions written by general practitioners had the lowest approval rates, while those from cardiologists had the highest chance of success (OR 1.42, 95% CI 1.24–1.63 for cardiology vs. general practice). Patients who had prescriptions processed by specialty pharmacies were more likely to be approved than retail pharmacies (OR 1.96, 95% confidence interval [CI] 1.66–2.33). Patients with government insurance plans had a higher odds of approval than those with commercial insurance (OR 3.27, 95% CI 2.79 – 3.83).
The likelihood for prescription approval also varied significantly by which PBM used (Table 2). In fact, knowing patient age, sex, pharmacy type, prescriber type, payor, and PBM could correctly discriminate ultimate approval in 72% of cases (C-statistic 0.721). Including laboratory, medication, and diagnoses data only increased the model prediction to a C-statistic of 0.737.
Factors Associated with Abandonment of Approved Prescriptions
Among patients who received approval for therapy, 34.7% never filled the prescription. Patient co-pays ranged from $0 to $2,822 per month supply, with a median of $15, and interquartile range of $15 to 373). Figure 2 demonstrates the association between patient copays and the proportion of patients who had therapy dispensed. Overall, 23% of patients with approved prescriptions had a $0 copay; of these, 92.6% of patients picked up the medication from the pharmacy. The rate of dispensing dropped as copays increased until around $350, above which dispensing rates were flat between 20–25%. Once the prescription was approved, copay alone was highly predictive of whether or not a patient would fill their prescription; the c-statistic was 0.86 for the logistic regression model evaluating the association between copay and prescription abandonment with copay alone. Including payor, provider, time to approval, and pharmacy type only marginally increased model performance (c-statistic for multivariable model: 0.87).
Figure 2. Relationship between Copay and Prescription Abandonment for Patients Approved for PCSK9i Therapy.
The figure shows the proportion of patients who filled their prescription after approval by range of copay (black line with markers), and the number of patients whose out of pocket costs were in that range (grey bars). Copay reflects ultimate out of pocket costs after co-insurance, patient approval program use, and coupon use. Abbreviations: PCSK9i, proprotein convertase subtilisin/kexin type 9 inhibitors
After adjusting for patient demographics, pharmacy, prescribing provider, and payor, copay remained highly predictive of whether a patient would pick up the medication from the pharmacy after it was approved. Table 3 shows the proportion of prescriptions dispensed after approval by subgroup, and results from multivariable logistic regression of factors associated with prescription dispensing after approval. Compared with patients who had no out-of-pocket costs, those with a $10 copay had a 19% lower odds of filling their prescription (OR 0.81 (95% CI 0.81–0.82), and those with a $100 copay had a 84% lower odds (OR 0.16, 95% CI 0.15–0.17, Table 3). In addition to higher copay, women, those with government insurance, use of a specialty pharmacy, and increased times to approval were associated with lower rates of medication dispensing in multivariable modeling.
Table 3.
Percent of Approved Prescriptions Dispensed and Predictors of Dispensing after Approval Among Approved Prescriptions
| Number Approved | Dispensed given Approval N (%) | Multivariable Results for Dispensing after Approval ORa (95% CI) | |
|---|---|---|---|
| Overall | 21,259 | 13,891 (65.3%) | |
| Age | |||
| <45 | 469 | 367 (78.2%) | Ref |
| 45–54 | 1736 | 1422 (81.9%) | 1.06 (0.76 – 1.47) |
| 55–64 | 4441 | 3414 (76.9%) | 1.02 (0.75 – 1.39) |
| 65–74 | 9435 | 5687 (60.3%) | 1.11 (0.82 – 1.50) |
| >75 | 5178 | 3001 (58.0%) | 1.02 (0.75 – 1.39) |
| Sex | |||
| Men | 10905 | 6940 (67.0%) | Ref |
| Women | 10354 | 6951 (63.7%) | 0.83 (0.77 – 0.89) |
| Pharmacy | |||
| Retail | 14882 | 9982 (67.1%) | Ref |
| Institutional | 102 | 74 (72.6%) | 1.41 (0.81 – 2.47) |
| Mail order | 218 | 159 (72.9%) | 3.44 (1.82 – 6.49) |
| Specialty | 6057 | 3676 (60.7%) | 0.89 (0.82 – 0.97) |
| Provider type | |||
| General practice | 6740 | 4261 (63.2%) | Ref |
| Cardiology | 11485 | 7539 (65.6%) | 1.18 (1.08 – 1.29) |
| Endocrinology | 997 | 660 (66.2%) | 1.06 (0.87 – 1.28) |
| Other | 2037 | 1431 (70.2%) | 1.49 (1.29 – 1.72) |
| Payor type | |||
| Commercial only | 5111 | 4071 (79.7%) | Ref |
| Government only | 14,236 | 8418 (59.1%) | 0.57 (0.50 – 0.64) |
| Comm + government | 1084 | 762 (70.3%) | 0.68 (0.55 – 0.83) |
| Other | 828 | 640 (77.3%) | 1.17 (0.91 – 1.51) |
| Copay | * | * | Ref: $0 |
| $10 | 0.81 (0.81 – 0.82) | ||
| $20 | 0.66 (0.65 – 0.67) | ||
| $40 | 0.44 (0.43 – 0.45) | ||
| $100 | 0.16 (0.15 – 0.17) | ||
| $200 | 0.06 (0.05 – 0.06) | ||
| $300 | 0.04 (0.03 – 0.04) | ||
| $500 | 0.03 (0.03 – 0.03) | ||
| Time to Approval | |||
| 0–1 day | 9858 | 6620 (67.2%) | Ref |
| 2–7 days | 2829 | 1908 (67.4%) | 1.06 (0.94 – 1.19) |
| 8–14 days | 2263 | 1432 (63.3%) | 0.90 (0.79 – 1.03) |
| 15–31 days | 2647 | 1602 (60.5%) | 0.74 (0.65 – 0.83) |
| >1 mo | 3662 | 2329 (63.6%) | 0.78 (0.70 – 0.87) |
Multivariable model results from Copay reflects ultimate out of pocket costs after co-insurance, patient approval program use, and coupon use. Because copay was modelled using restricted cubic splines, representative odds ratios are presented at each copay level. See Figure 2 for absolute dispense rates by copay ranges.
Discussion
The high retail cost of PCSK9i therapy has led to significant debate about their cost effectiveness and optimal price points.3,4,5 In response to this costs, payors have implemented various utilization management strategies, including prior authorization requirements and patient cost-sharing (copays). While these strategies have contained costs,6 our analysis suggests that these practices may be a blunt instrument to identify those at highest risk or those who may benefit the most.
Our study was not designed to address the appropriateness of current drug prices; rather, our analyses address the degree to which utilization management and patient out-of-pocket costs affected the likelihood that a patient prescribed therapy by their physician would ultimately receive this therapy. In the first year of PSCK9i availability, fewer than 1 in 3 adults initially prescribed PCSK9i therapy actually received it – 52.8% never received approval, and 34.7% of those approved never filled the prescription. While those with ASCVD were slightly more likely to receive approval than those without, there was no difference in approval rates by statin use or LDL-C levels. In contrast, non-clinical factors such as the patient’s payor type, PBM, and type of pharmacy used all strongly affected the likelihood that a prescription will be approved. Once approved, out-of-pocket costs were the primary driver of prescription abandonment.
Provider and pharmacy type both affected the success of initial prescriptions. Cardiologists were more successful than other providers in obtaining approval for PCSK9i, which may indicate that subspecialists were better able to select patients who met these criteria. Alternately, the increased volume of prescriptions written by cardiologists may have allowed them to develop support systems to better navigate the complex application and appeal process for their patients. Specialty pharmacies also had higher approval rates, potentially due to systems to support providers in appealing payor rejections.
However, one of strongest factors affecting approval rates was which payor and PBM were involved. This is likely due to differences in both criteria required for approval and the process itself. While we were unable to determine the proportion of rejections that were clinically appropriate, concerns have been raised by patients and professional societies that the approval process may limiting therapy access to those who are clinically indicated.7,8 We did find that patients with established ASCVD and patients on ezetimibe had a slightly higher odds of receiving approval for PCKS9i. However, we found no difference in approval rates among patients who were on any statin or a high-intensity statin and those who were not, nor was there any difference in approval rates by LDL-C levels. Additionally, even after adjusting for these factors, nonclinical factors such as payor type, PBM, prescriber type, and pharmacy used remained highly predictive of approval success. One interesting finding was that 10.2% of patients had a most recent LDL-C value <70 mg/dL, with just over half of those patients receiving approval. Although the FOURIER trial shows that PCSK9i are effective in those with low starting LDL-C, most payor criteria at the time required LDL-C to be greater than 70 mg/dL.9 Whether this represents potential inappropriate prescribing vs. patients who later developed statin intolerance remains unclear and should be further studied.
After patients were approved for a medication, we found that up to one-third abandoned the prescription at the pharmacy. This high abandonment rate was almost fully accounted for by differences in patient out of pocket costs; fewer than 1 in 10 patients with a $0 copay failed to fill their medication, whereas 3 out of 4 patients with a copay exceeding $375 did not pick up their prescription. Furthermore, although the “sticker price” for therapy may be the same for all patients, how much patients actually paid out of pocket varied widely, from $0 for the lowest quartile of patients to over $300 per month for the highest quartile. Although coupon and patient assistance programs can help offset copay, and were used by 38% of patients in this sample, coupon programs are largely unavailable to those with government insurance. Additionally, even after adjusting for copay, patients with government insurance (which was mostly Medicare in this dataset) had higher abandonment rates than those with commercial insurance; whether this finding is due to socioeconomic factors or older age needs further exploration. Although we only assessed a patient’s first fill of PSCK9i, copay also has potential to affect medication persistence and refills, and should be studied. This may be particularly relevant to Medicare beneficiaries, who often have fluctuating out-of-pocket costs based on their phase of coverage (e.g. before, during, and after the “Donut hole”).
Our study also found that the approval process is often prolonged. In 17% of cases approval is only obtained after more than a month of appeals. This appeal and reapplication process adds significant time to providers work load. One study estimated that prior authorization requests and appeals in general consume up to 20 hours of staff and professional time per week, per medical practice.10 Additionally, we found that these delays could affect patients’ likelihood to fill their prescription even it ultimately approved. Specifically, those who waited longer for approval were less likely to pick up the prescription from the pharmacy, even adjusting for copay.
The biggest limitation of this study is that it is challenging to fully adjudicate the complex clinical factors used in various PBM approval processes. Thus, we could not fully determine whether or not specific rejections met these criteria or not. However, our results suggest that a person’s likelihood of receiving therapy is strongly influenced by payor and PBM dynamics, including differences in payor and PBM policies for approval, which we could not account for in this analysis. Our study had some additional limitations. First, SHS data does not cover 100% of all pharmacies; however, given the large proportion of United States pharmacies that are covered by SHS, the data we examined likely reflect the experience of the majority of patients prescribed PCSK9i. Second, we evaluated copay as the ultimate out-of-pocket expense. Given the use of coupon cards and patient assistance programs, our analysis reflects out-of-pocket costs rather than the cost-share that individual payors are expecting from patients. Finally, we only evaluated prescription patterns in the first year after FDA approval of PCSK9i. Since then, outcomes trial data from the FOURIER trial have demonstrated the effectiveness of evolocumab, potentially affecting both the rate of prescribing and payor approval criteria.12
Conclusions
The high cost of PCSK9 therapy has led payors to institute rigorous prior authorization practices and often leads to high patient co-pays. In the first year of PCSK9i availability, less than one-third of patients prescribed therapy actually received medication, due to a combination of high rejection rates and patient abandonment after approval. Even after navigating the approval process, nearly one-third of patients did not pick up their medication, which was mostly explained by patient out-of-pocket cost.
Supplementary Material
Acknowledgments
The authors thank Erin Campbell for editorial support.
Sources of Funding
This study was supported by Amgen Inc.
Footnotes
Author Contributions and Role of Funding Source
While Amgen, Inc. (B Taylor, K Monda, J Lopez, J Maya) and DCRI co-authors (AM Navar, E Peterson) collaborated on the data acquisition, data interpretation, and critical revision of the manuscript, the conception and design of the study and data analyses were conducted by the DCRI alone (AM Navar, E Peterson, H Mulder), with Symphony co-authors contributing to study design as it related to creating and utilizing the analytic dataset. All authors have been involved in the manuscript revision and have read and approved the final manuscript. Dr. Navar is the guarantor who accepts full responsibility for the work and the conduct of the study, had access to the data, and controlled the decision to publish.
AM Navar: Dr. Navar had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Dr. Navar contributed to the conception and design of the study, the data analysis and interpretation, the manuscript drafting, and the critical revision of the manuscript.
H Mulder: Ms. Mulder contributed to the design of the study, data analysis and interpretation, the manuscript drafting, and the critical revision of the manuscript.
B Taylor: Dr. Taylor contributed to the the data acquisition, the data interpretation, and the critical revision of the manuscript.
E Fievitz: Mr. Fievitz contributed to the data acquisition, the design of the study, the data interpretation, and the critical revision of the manuscript.
KL Monda: Dr. Monda contributed to the acquisition, the data interpretation, and the critical revision of the manuscript.
A Fievitz: Ms. Fievitz contributed to the data acquisition, the design of the study, the data interpretation, and the critical revision of the manuscript.
JF Maya: Dr. Maya contributed to the data acquisition, the data interpretation, and the critical revision of the manuscript.
JA López: Dr. Maya contributed to the data acquisition, the data interpretation, and the critical revision of the manuscript.
ED Peterson: Dr. Peterson contributed to the conception and design of the study, the supervision, data analysis and interpretation, the manuscript drafting, and the critical revision of the manuscript.
Author Disclosures
AM Navar: Dr. Navar is supported by a research grant from NHLBI (K01HL133416-01), and receives research funding from Amgen Inc., Sanofi Pharmaceuticals, and Regeneron Pharmaceuticals, as well as honoraria for research consulting for Sanofi and Amgen Inc.
H Mulder: Ms. Mulder reports no disclosures.
B Taylor: Dr. Taylor is an employee of Amgen, Inc.
E Fievitz: Mr. Fievitz is an employee of Symphony Health
KL Monda: Dr. Monda is an employee of Amgen, Inc.
A Fievitz: Ms. Fievitz is an employee of Symphony Health
JF Maya: Dr. Maya is an employee of Amgen, Inc.
JA López: Dr. López is an employee of Amgen, Inc.
ED Peterson: Dr. Peterson receives consultant/honoraria from AstraZeneca, Bayer, Janssen, Merck & Co., Sanofi; research grants from AstraZeneca, Bayer, Daiichi Sankyo, Genetech, Janssen, Regeneron, Sanofi, Merck & Co, and Amgen Inc.
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