Abstract
BACKGROUND:
Because of concerns of cost-effectiveness and low utilization, in 2018, manufacturers initiated a 60% price reduction for PCSK9 inhibitors, reducing the list price from more than $14,000 to $5,850. The goal of the reduction was to increase access and lower patient cost sharing for PCSK9 inhibitors.
OBJECTIVE:
To determine whether list price reductions resulted in a statistically significant decrease in patient cost sharing for PCSK9 inhibitors. The secondary objective is to quantify the change in monthly out-of-pocket (OOP) cost in the years following the price reduction policies.
METHODS:
This analysis uses a cross-sectional quasi-experimental design, with 2 time periods, to estimate the change in monthly OOP cost. A 2-stage cost model was used to quantify the difference in mean monthly OOP cost between the preprice and postprice reduction periods. This analysis was completed using IQVIA PharMetrics Plus for Academics health plan claims for PSCK9 inhibitors between January 2016 and December 2021 for commercially insured individuals in the United States. The primary exposure of interest is a manufacturer-initiated list price reduction in October 2018. The primary outcome of interest is the difference in the predicted monthly OOP cost between the prereduction and postreduction periods.
RESULTS:
There was a 50% decrease in the predicted monthly OOP cost, from $235.22 (SD = $241) in the prereduction period to $116.75 (SD = $152) in the postreduction period.
CONCLUSIONS:
This claims level analysis used robust statistical modeling techniques to quantify the effect of manufacturer-initiated price reductions on monthly OOP cost. This unique manufacturer decision resulted in a statistically significant decrease in the monthly OOP cost for beneficiaries using PCSK9 inhibitors. Manufacturer-initiated price reductions could be a strategy to reduce the cost for other therapies with access and cost concerns. Further research is needed on the downstream patient-level effects of cost reductions, particularly among individuals who experience multiple barriers to care.
Plain language summary
Manufacturer-initiated price cuts for PCSK9 inhibitors decreased the list price for these medications, with the intention of decreasing out-of-pocket (OOP) costs for patients and increasing utilization. We found a 50% reduction in the monthly OOP cost when comparing the period prior to and after the price cut announcement. Despite the decrease in price and OOP costs, medication use remains low, indicating obstacles to getting PCSK9 inhibitors may still remain. To ensure patients can access PCSK9 inhibitors, managed care organizations may need to address remaining barriers.
Implications for managed care pharmacy
This claims-based study found a 50% reduction in the monthly OOP cost when comparing the period prior to and following manufacturer-initiated price reductions. This suggests that such policies can be an effective approach to address concerns regarding the cost and utilization of high-cost therapies, such as PCSK9 inhibitors. Managed care organizations may consider advocating for or negotiating similar strategies with manufacturers for other medications to improve patient access and affordability while managing overall health care costs.
High out-of-pocket (OOP) costs for PCSK9 inhibitors are a significant barrier to access for patients.1 PSCK9 inhibitors were first approved in 2015 as a novel treatment to lower low-density lipoprotein cholesterol in high risk adults with familial hypercholesterolemia and clinical atherosclerotic cardiovascular disease.2 PCSK9 inhibitors are indicated for use in addition to diet and statin therapy.3 In clinical trials, compared with placebo, PCSK9 inhibitors, in conjunction with statins, demonstrated a 60% reduction in lower low-density lipoprotein cholesterol.4,5 In addition, in phase 3 clinical trials, PCSK9 inhibitors were shown to reduce cardiovascular events by 50%.3 Furthermore, the 2018 American Heart Association treatment guidelines added PCSK9 inhibitors as a recommended treatment, but they were not given a class 1 recommendation because of the high cost.6
Even with these benefits, early cost-effectiveness studies found that PCSK9 inhibitors were not cost-effective at a threshold of $100,000 per quality adjusted life year (QALY) willingness to pay threshold.7,8 A study by Arrieta et al modeling 1,000 hypothetical patients similar to those included in the FOURIER trial found that at an annual price of $14,300, the incremental cost-effectiveness ratio was $337,729 and the price would need to be lowered by 62% to reach a $100,000/QALY threshold. Also, in 2015, the Institute for Clinical and Economic Review (ICER), a private US-based health technology assessment organization, conducted a review of PCSK9 inhibitors, which at the time cost $14,350 per year compared with $2,828 for the comparator, ezetimibe. The ICER report found that compared with existing treatments, PCSK9 inhibitors provided substantial clinical benefit, but the high drug costs resulted in a cost-effectiveness ratio of $290,000/QALY, which is well above the standard willingness to pay threshold.9 This analysis was updated in 2018 to provide a value-based pricing benchmark for alirocumab (Praluent), an approved PCSK9 inhibitors. The ICER report estimates that an annual value-based price ranging from $2,300 to $8,000 would be suitable for this population.10
At launch, the annual list price of PCSK9 inhibitors exceeded $14,500,11 which resulted in high cost sharing for patients and restrictive prior authorization criteria.12 In the year immediately following approval, nearly 33% of patients abandoned their PCSK9 inhibitors prescription at the pharmacy, likely because of high cost sharing.12,13 Among Medicare Part D beneficiaries, cost sharing requirements for treatments were estimated to surpass $300/month or $5,000 annually.14 It is estimated that 90% of Medicare Part D plans cover PCSK9 inhibitors, although the high costs have resulted in stringent prior authorization practices for commercial insurance plans.14,15 A study of patients initiating PCSK9 inhibitors between August 1, 2015, and July 31, 2016, found that about 31% of patients who were prescribed a PSCK9 inhibitor never received the therapy and that prescription abandonment was highest in patients with monthly copays exceeding $350. This study suggests that the limited approval and prescription fills are related to high copays.15 High cost sharing is especially concerning for low-income families. A study of low-income families, with 1 or more member diagnosed with atherosclerotic cardiovascular disease, estimated that 1 in 4 families will experience high financial burden and 1 in 10 families will experience a catastrophic financial burden from health care expenses.15
To address concerns of the high cost and limited accessibility of treatments, manufacturers announced an unprecedented 60% wholesale acquisition cost or list price cut in October 2018.16,17 The wholesale acquisition cost is the manufacturers’ published catalog or list price for the sale of prescription drugs.18 The intention of these changes was to reduce patient cost sharing, increase insurance coverage, and increase utilization of therapies.16,17,19 Although, a net price reduction for a therapy does not mean that patients will experience lower copayments. Prior to this announcement, 1 manufacturer was offering significant rebates to payers in exchange for increased patient access.17 This initiative also aligns with the American Heart Association’s Value in Healthcare initiative, which is focused on eliminating barriers to care.20 Following the price reductions, the rate of new prescriptions increased from 0.5% to 3.3% and continuation rates increased from 18% to 60%.11 Yet, utilization among eligible patients remains low, with less than 1% of eligible patients receiving a prescription for a PCSK9 inhibitor.11 There are a number of factors that may also contribute to low utilization, such as provider preferences for treatment, perceived difficulty in the prior authorization process, limited insurance approvals, documentation issues, and price.11,21
What remains unknown is if the list price reduction resulted in a significant decrease in the OOP cost for this treatment. Therefore, the purpose of this analysis is to determine if there was a decrease in the monthly OOP cost for a PSCK9 inhibitor and to quantify the change in monthly OOP cost in the period following price reduction policies. The secondary objective is to characterize utilization for PSCK9 inhibitors in the period before and after the price reduction policies. This analysis will contribute to the limited evidence base regarding the impact of manufacturer price reductions on patient OOP spending.
Methods
DATA SOURCE
We conducted a claims level retrospective cross-sectional analysis using a 25% random sample of enrollees within the IQVIA PharMetrics Plus for Academics data from 2016 to 2021.22 IQVIA PharMetrics Plus for Academics is a comprehensive database composed of fully adjudicated health plan claims data and enrollment information for mostly commercial individuals. The information is primarily composed of commercial health plans and self-insured employer groups throughout the United States. The 25% random sample includes both medical and pharmacy claims for more than 27.5 million individuals. The database includes demographic data for beneficiaries including year of birth, sex, state and census region of residence, payer type, and plan type. Claims in the database include National Drug Codes, Healthcare Common Procedure Coding System codes, Current Procedural Terminology codes, International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis codes and procedure codes, date of pharmaceutical claims, allowed and paid amounts, as well as deductible, coinsurance, and copayment amounts. This study followed Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross-sectional studies.23
STUDY SAMPLE
The patient cohort used in this study was derived from a 25% random sample of enrollees within the PharMetrics Plus for Academics claims data from 2016 to 2021. The analysis employs a subset of the data that includes claims for PCSK9 inhibitors made between January 1, 2016, and December 31, 2021. Claims were identified using National Drug Codes. Claims were included in the analysis if there were no missingness for the key demographic variables (age, sex, payer type, and plan type), age (years) greater than or equal to 18, and OOP cost greater than or equal to $0.
TREATMENT ASSIGNMENT
Treatment assignment was based on a quasi-experimental design with groups determined based on the date of each PCSK9 inhibitors claim. Price reductions were first announced on October 25, 2018. The prereduction period (or control group) included claims with index dates between January 1, 2016, and October 25, 2018, whereas the postreduction (or treatment group) included claims with index dates between October 26, 2018, and December 31, 2021. Patients were able to contribute claims to 1 or both periods.
OUTCOME
Monthly OOP cost was calculated as the sum of copayments, coinsurance, and deductibles. To compare OOP costs across claims, each claim was standardized, using days supplied and prescription strength to represent a 30-day supply or monthly cost.
STATISTICAL ANALYSIS
Patient Characteristics. Baseline patient-level descriptive statistics were calculated at the time of a beneficiary’s first claim. Claims level descriptive statistics were also calculated. To characterize the included claims and patients, descriptive statistics, including frequency (%) and mean (SD), were calculated for included covariates (Table 1). Chi-square tests and t-tests were used to test the differences in baseline characteristics between the preprice and postprice reduction groups.
TABLE 1.
Descriptive Statistics of Included Claims and Patients Prescribed a PCSK9 Inhibitor Between January 2016 and December 2021
| Claim level | Total(N = 51,251) | Preprice reduction (n = 9,150) | Postprice reduction (n = 42,101) | P value |
|---|---|---|---|---|
| Type of plan, n (%) | <0.01 | |||
| HMO | 15,887 (31) | 2,407 (26) | 13,480 (32) | |
| PPO | 29,947 (58) | 5,987 (65) | 23,960 (57) | |
| Othera | 5,417 (11) | 756 (8) | 4,661 (11) | |
| Payer type, n (%) | <0.01 | |||
| Commercialb | 29,487 (58) | 6,266 (68) | 23,221 (55) | |
| Medicare Advantage / Cost and Managed Medicaidc | 21,764 (42) | 2,884 (32) | 18,880 (45) | |
| Patient level | Total (N = 4,566) | Preprice reduction (n = 1,013) | Postprice reduction (n = 3,553) | P value |
| Age, mean (SD) | 63 (11) | 62 (11) | 64 (11) | <0.01 |
| Age group, n (%) | <0.01 | |||
| 25-50 | 519 (11) | 125 (12) | 394 (11) | |
| 51-64 | 1,980 (43) | 484 (48) | 1,496 (42) | |
| 65+ | 2,067 (45) | 404 (40) | 1,663 (47) | |
| Sex | 0.02 | |||
| Female | 2,081 (46) | 429 (42) | 1,652 (47) | |
| Male | 2,485 (54) | 584 (58) | 1,901 (54) | |
| Region | <0.01 | |||
| Midwest | 1,361 (30) | 257 (25) | 1,104 (31) | |
| South | 1,374 (30) | 279 (28) | 1,095 (31) | |
| West | 1,162 (25) | 266 (26) | 896 (25) | |
| Northeast/unknown | 669 (15) | 211 (21) | 458 (13) |
Percent may not total to 100 because of rounding.
a Includes Consumer Directed Health Care and Point of Service.
b Includes Commercial Health Plans and self-insured.
c Includes Medicare Advantage, Medicare Supplement Plans, and Managed Medicaid.
HMO = health maintenance organization; PPO = preferred provider organization.
Cost Analysis. Because the cost data are heavily skewed to the right, with a large mass at $0, we used a 2-stage model to quantify the difference in mean monthly OOP cost between periods. The regression models used claim-level data.
The first model is a logistic regression model predicting the odds of having an OOP cost greater than $0. The second model is a generalized linear model with log link and γ distribution. The second generalized linear model predicted the mean monthly OOP cost, conditional on strictly positive costs. Estimates for unadjusted models are presented. We also estimated models adjusted for age, sex, census region (Midwest, West, South, and Northeast), payer type (Health Maintenance Organization, Preferred Provider Organization, and Other), and plan type (Commercial and Medicare Advantage/Cost or Managed Medicaid). To account for clustering at the patient level, a restricted maximum likelihood estimation method was used to estimate the variance of the sample. Based on Akaike Information Criterion, an exchangeable correlation structure was chosen. A modified Park test was used to assess the family distribution, and Ramsey’s Regression Equation Specification Error Test was used to test model specification.
Falsification Test. Lastly, because there is no formal control group, a falsification test was performed to evaluate internal validity. The 2-stage model was repeated using claims for individuals who received Janus kinase inhibitors (JAKi). This population was chosen because of a similar monthly OOP cost distribution, similar patient demographics, and the chronic nature of both conditions. Also, the utilization and cost of JAKi are not impacted or influenced by the utilization and cost of PCSK9 inhibitors. JAKi are used to treat chronic inflammatory disorders, such as rheumatoid and psoriatic arthritis, axial spondylarthritis, ulcerative colitis, atopic dermatitis, and alopecia areata.24 Like PCSK9 inhibitors, JAKi are high price and can cost between $26,000 and $60,000 annually.25 Patients using JAKi also can face difficulties in obtaining insurance coverage for treatments.25 All analyses were performed using SAS, version 9.4. For all analyses, a 95% confidence level was used.
Results
SAMPLE CHARACTERISTICS
Our dataset consisted of 51,251 claims representing 4,566 unique individuals. Across the 2 time periods, 9,150 (17.9%) claims corresponding to 1,013 (23.5%) patients were in the prereduction period and 42,101 (82.1%) claims corresponding to 3,553 (77.8%) patients were in the postreduction period (Figure 1). The average age for individuals with PCSK9 inhibitor claims in the preprice and postprice reduction periods was aged 62 (SD = 11) and 64 (SD = 11) years, respectively. This difference in age was found to be statistically significant (P < 0.01). Male patients accounted for about 54% of the sample. Of the included patients, 1,361 (30%) were from the Midwest, 1,374 (30%) were from the South, 1,162 (25%) were from the West, and 669 (15%) were either from the Northeast or missing. There were statistically significant differences in the sex and census regions. (Table 1).
FIGURE 1.
CONSORT Flow Diagram of Included Claims for a Prescribed PCSK9 Inhibitor Between January 2016 and December 2021 in a Nationally Represented Commercially Insured Dataset, IQVIA PharMetrics Plus
In both periods, more than 55% of the claims are covered under a Preferred Provider Organization and most claims, 68% in the prereduction and 55% in the postreduction periods, are covered under a commercial plan. There was a statistically significant difference in the number of claims covered by payer type (P < 0.01) and plan type (P < 0.01) (Table 1).
PATIENT OOP COST
The estimated mean monthly OOP cost for this sample was $135.00 (SD = $174). The mean monthly OOP cost was $235.22 (SD = $241) and $116.75 (SD = $152) in the prereduction and postreduction periods, respectively, representing a 50% decrease. Claims covered under a Medicare Advantage/Cost or Managed Medicare health plan had a 73% decrease and claims under a commercial health plan saw a 34% decrease in OOP cost between periods (Figure 2).
FIGURE 2.
Estimated Monthly OOP Cost for the Full Sample and by Payer Type
MODELING RESULTS
Model results for each model stage are presented in Table 2. Overall, when comparing the prereduction and postreduction periods, there was no difference in the odds of having a cost greater than $0 (odds ratio [OR] = 1.02; 95% CI = 0.94-1.11). These associations were not observed in the second-stage model, predicting mean monthly OOP costs.
TABLE 2.
Regression Results for Primary Analysis: Probability of Monthly Out-of-Pocket Costs Greater Than $0 and Monthly Out-of-Pocket Cost for PCSK9 Inhibitors
| Two-part model | ||
|---|---|---|
| Covariates | Odds of cost >$0 (First-stage model) | Cost ratios (Second-stage model) |
| OR (95% CI) | Cost estimate (95% CI) | |
| Intercept | 0.42 (0.38-0.45)a | $208.78 ($195.55-$222.94)a |
| Time period | ||
| Prereduction | Reference | |
| Postreduction | 1.02 (0.94-1.11) | 0.57 (0.53-0.61)a |
Model part 1 predicts the probability of monthly out-of-pocket costs > $0 and model part 2 predicts monthly out-of-pocket costs for PCSK9 inhibitors contingent on positive cost.
a Significant at 0.05.
OR = odds ratio.
Based on the second-stage model, there was a statistically significant change in the expected monthly OOP cost when comparing the prereduction and postreduction periods (cost ratio = 0.57; 95% CI = 0.53-0.61), indicating that the estimated OOP cost in the postreduction period is 43% lower than the expected OOP cost in the prereduction period. Although claims covered for both commercially and publicly insured individuals saw an approximately 50% decrease in the predicted monthly OOP cost between the 2 periods, the predicted monthly OOP cost for the commercially insured sample was greater than the predicted monthly OOP cost for the Medicare Advantage/Cost or Managed Medicare insured sample in both periods.
FALSIFICATION TESTING
Model results are presented in Table 3. Overall, when comparing the prereduction and postreduction periods for JAKi, there was a statistically significant difference in the odds of having a cost greater that $0 (odds ratio = 0.66; 95% CI = 0.64-0.70). The second-stage model showed a statistically significant change in the monthly OOP cost for JAKi. The cost ratio compared the prereduction and postreduction periods (cost ratio = 1.15; 95% CI = 1.12-1.20), indicating an increase in the monthly OOP cost between the prereduction and postreduction periods.
TABLE 3.
Regression Results for Falsification Test: Probability of Monthly Out-of-Pocket Costs Greater Than $0 and Monthly Out-of-Pocket Costs for Janus Kinase Inhibitors
| Two-part model | ||
|---|---|---|
| Covariates | Odds of cost >$0 (First-stage model) | Cost ratios (Second-stage model) |
| OR (95% CI) | Cost estimate (95% CI) | |
| Intercept | 1.05 (1.02-1.09)a | $402.54 (390.25-425.18)a |
| Time period | ||
| Prereduction | Reference | |
| Postreduction | 0.66 (0.64-0.70)a | 1.15 (1.12-1.20)a |
Model part one predicts the probability of monthly out-of-pocket costs >$0 and model part 2 predicts monthly out-of-pocket cost for PCSK9 inhibitors contingent on positive cost.
a Significant at 0.05.
Discussion
All in all, it was estimated that there was no change in the odds of having an OOP cost greater than $0 for PCSK9 inhibitors, but a statistically significant decrease in the monthly OOP cost was observed when comparing the preperiods and postperiods. Compared with individuals insured under Managed Medicaid, Medicare Advantage, or Medicare Cost plans, OOP costs for individuals insured under commercial insurance plans were higher in the postreduction period. This may be attributed to differences in rebates or reimbursements between plan types that have downstream effects on OOP costs. Although, managed care companies may also adjust OOP costs to offset premiums for beneficiaries.
This study employed rigorous modeling techniques and a quasi-experimental design to estimate the impact of manufacturer-initiated price reduction policies on patient OOP cost per claim. Moreover, the results were subjected to a falsification test, which did not find evidence of reduced OOP costs in a comparable population, indicating that the PCSK9 inhibitors price reduction strategies were more likely focused on the PCSK9 inhibitors users. It is also important to note that there are a variety of federal policies aimed at reducing drug prices, such as the 21st Century Cares act, the Trump Administration’s Blueprint to Lower Drug Prices, Putting Patients First, and President Biden’s Executive Order on Promoting Competition in the American Economy.26-28
Furthermore, the price reduction has not only impacted the OOP cost of PCSK9 inhibitors. Prior research has shown that it also positively impacted the rate of new prescriptions and resulted in a decrease in drug discontinuation,11 suggesting that the observed change in cost made it more sustainable for patients to access PCSK9 inhibitors. The reduction in cost has also been linked to improved adherence to PCSK9 inhibitors, primarily in patients with commercial insurance.11 Previous literature suggests that although there has been a reduction in cost, most patients who are eligible for PCSK9 inhibitors are still not prescribed one. Two studies using electronic medical records found that among patients eligible for PCSK9 inhibitors, less than 1% receive a prescription.29,30 Although the price reductions in October 2018 were a key step in lowering monthly OOP costs for and increasing access to PCSK9 inhibitors, there continues to be low utilization after the price reduction,11 implying that further additional efforts are needed to improve patient access to these therapies. It should be noted that a reduction in the list price for PCSK9 inhibitors may lower the OOP costs and increase access for patients electing to use the drugs but could result in additional downstream effects, such as increased premiums, which impact all beneficiaries. Lastly, OOP costs are influenced by factors beyond the list price of a medication, including copayments, coinsurance rates, and formulary placement. Recent analyses show that PCSK9 inhibitors have been moved from the specialty tier to a nonpreferred tier, likely increasing OOP costs for Medicare beneficiaries.31 Changes in insurance benefit plans resulting from the list price decrease may also be a downstream effect, which could be investigated in future research.
STRENGTHS
One strength of this study is the use of robust modeling techniques, including 2-stage models to address the skewed cost data and a falsification test to validate the findings. The quasi-experimental design helps isolate the effect of the price reduction on OOP costs from other confounding factors. These findings have implications for health care policy, showing how list price reductions can enhance patient access to therapies.
LIMITATIONS
Although robust modeling techniques have been used, there are limitations to the current analysis. First, the results of this analysis may not be generalizable to all populations because the data are primarily from commercial claims. Our results do not fully capture the Medicare and Medicaid populations or those that pay entirely in cash for treatment. This analysis is completed at the claims level; therefore, we are unable to extrapolate how the price reductions may have impacted OOP cost for individual patients over time. Next, currently, there is no way to assess the trend in OOP cost over time. There may have been anticipatory behavior or lag time, which is currently unobservable. Future research in this area could employ longitudinal analysis techniques to further explore the market behavior. Furthermore, data for race and ethnicity, residence in rural/urban setting, education level, or income status were not available. These are important covariates that may impact OOP cost and access to treatments and would be valuable for future analyses. Also, JAKi and PCSK9 inhibitors have different routes and frequency of administration. The route of administration for PSCK9 inhibitors may further limit utilization. Lastly, the data are limited to the 2016-2021 time period, so we are unable to capture OOP costs for claims in the first year following product launch, and we are unable to capture any effects associated with the launch of a third PCSK9 inhibitor in 2021.
Conclusions
This claims level analysis quantified the impact of manufacturer-initiated price reductions on monthly OOP costs. This unique manufacturer decision to align pricing decisions with cost-effectiveness estimates has resulted in reduced OOP costs for PCSK9 inhibitors and may be a strategy to reduce the cost for other therapies with access and cost concerns. Further research is needed on the downstream patient-level effects of cost reductions, particularly among individuals who experience multiple barriers to care.
Funding Statement
Dr Onukwugha reports funding, unrelated to this project, from Beigene Unlimited.
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