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Impact of a Value-Based Formulary in Three Chronic Disease Cohorts
Kai Yeung, PharmD, PhD; Anirban Basu, PhD; Zachary A. Marcum, PharmD, PhD; John B. Watkins, PharmD, MPH, BCPS; and Sean D. Sullivan, PhD

Impact of a Value-Based Formulary in Three Chronic Disease Cohorts

Kai Yeung, PharmD, PhD; Anirban Basu, PhD; Zachary A. Marcum, PharmD, PhD; John B. Watkins, PharmD, MPH, BCPS; and Sean D. Sullivan, PhD
A value-based formulary was implemented that used cost-effectiveness analysis to inform medication co-payments. Diabetes cohort expenditures decreased by $9 per member per month.
ABSTRACT

OBJECTIVES: Value-based insurance design has been suggested as an effective approach to ensure access to highvalue medications in health insurance markets. Premera Blue Cross, a large regional health plan, implemented a value-based formulary (VBF) for pharmaceuticals in 2010 that explicitly used cost-effectiveness analysis to inform medication co-payments. This study assesses the impact of a VBF on adherence and patient and health plan expenditures on 3 chronic disease states: diabetes, hypertension, and hyperlipidemia.

STUDY DESIGN: Interrupted time series design of employer-sponsored plans from 2006 to 2013. Beneficiaries exposed to the VBF formed the intervention group, and beneficiaries in similar plans without any changes in pharmacy benefits formed the control group.

METHODS: We measured medication expenditures from member, health plan, and member-plus-health plan (overall) perspectives and medication adherence as proportion of days covered. We conducted an exploratory analysis of medication utilization classifying medications according to whether co-payments moved up or down in the year following VBF implementation.

RESULTS: For the diabetes cohort, there was a statistically significant reduction in member and overall expenditures of $5 per member per month (PMPM) and $9 PMPM, respectively. For the hypertension cohort, there was a statistically significant reduction in member expenditures of $4 PMPM and an increase in health plan expenditures of $3 PMPM. There were no statistically significant effects on hyperlipidemia cohort expenditures or on medication adherence in any of the 3 disease cohorts. Exploratory analyses suggest that patients in the diabetes and hyperlipidemia cohorts were switching to higher-value medications.

CONCLUSIONS: A VBF can ensure access to high-value medications while maintaining affordability.

Am J Manag Care. 2017;23(3 Suppl):S46-S53
Takeaway Points
A value-based formulary (VBF) estimates the value of individual medications and aligns medication co-payments with value. We evaluated the impact of a formulary on adherence and patient and health plan expenditures in 3 chronic disease states: diabetes, hypertension, and hyperlipidemia. 
  • For the diabetes cohort, member and overall expenditures decreased.
  • For the hypertension cohort, there was a cost shift from member to plan. 
  • There were no statistically significant effects on hyperlipidemia cohort expenditures or on medication adherence in any of the 3 disease cohorts. 
  • A VBF can ensure access to high-value medications while maintaining affordability.
Rising prescription drug expenditures in recent years have prompted healthcare payers to look for ways to achieve greater value in pharmaceutical care. Some proposed solutions have included the adoption of frameworks for assessing value,1 varying payments to manufacturers based on the effectiveness of a drug according to each clinical indication (indication-specific pricing),2 and aligning medication utilization with value through cost-sharing redesign.3,4 This last policy, known as value-based insurance design (VBID), advocates for the redesign of cost sharing such that patient out-of-pocket expense aligns with the value—both health benefit and cost—of an intervention rather than solely on the cost of the intervention.3

Recently, there has been federal interest in the exploration of the effects of VBID for patients with particular chronic conditions.5 Beginning in January 2017, the Center for Medicare & Medicaid Innovation (CMMI) began granting Medicare Advantage plans in 7 states the ability to pilot VBID for enrollees with at least 1 of 7 chronic conditions, including diabetes, hypertension, and certain cardiovascular diseases. One of the approaches permitted by CMMI for these pilot programs is the reduction of cost sharing for high-value services, including pharmaceuticals. In 2018, this pilot program will expand to 3 additional states and 2 additional chronic conditions5; there also is current Congressional discussion to expand the pilot to all states.6

According to CMMI, the goals of these pilot programs are to improve health outcomes and attain healthcare cost savings or cost neutrality. Specifically, applicants are required to actuarially demonstrate “net savings to Medicare and no net cost increases to enrollees over the course of the model test.”7 However, findings from previous trials of VBID implemented among patients with chronic diseases suggest that co-payment reductions alone may not necessarily reduce plan costs. The Post-Myocardial Infarction Free Rx Event and Economic Evaluation trial was a randomized trial to solely test the effect of targeted medication co-payment reduction on expenditures.8 This study randomized health plans to offer co-payment waivers for certain cardiovascular medications for patients who had been discharged from a hospital with a diagnosis of a myocardial infarction. The study found that although adherence improved moderately (4%-6%) and there were improvements in certain secondary cardiovascular health outcomes, plan medication expenditure increased significantly while plan nonmedication health expenditures were not significantly reduced. The results of other observational studies also show that co-payment reduction alone improves adherence (by 1.5%-9.4%) but increases health plan medication expenditures and has no significant effect on health plan nonmedication expenditures.8-12

A value-based formulary (VBF) is a fuller realization of VBID that incorporates both increases in co-payments for some drugs and decreases in co-payments for other drugs according to value estimates generated by cost-effectiveness analyses. Drugs that have higher estimated value are placed in lower co-payment tiers to incentivize use, and drugs that have lower estimated value are placed in higher co-payment tiers to disincentivize use. In 2010, Premera Blue Cross, the largest private health plan in Washington state, implemented a VBF for their employees and dependents. Details regarding the design and implementation of the VBF have been reported previously.13 A 3-year analysis of the impact of the Premera VBF has shown that the VBF is able to shift plan members toward higher-value drugs and decrease health plan and total (health plan plus member) medication expenditures while not having any measurable adverse effects on hospitalizations, emergency department visits, or nonmedication expenditures.14 Given the interest in value-based approaches among patients with chronic diseases, we evaluated the 3-year effects of the VBF on medication adherence and expenditures among individuals with diabetes, hypertension, or hyperlipidemia.

METHODS

Data Source

The study population comprised employees and dependents aged 0 to 64 years who were covered under employer-sponsored preferred provider organization plans administrated by Premera Blue Cross. The intervention group consisted of employees and dependents of Premera in a plan that implemented the VBF on July 1, 2010. The control group included employees and dependents of 5 employersponsored plans administered by Premera that did not have any changes in pharmacy benefits over the entire study period. These plans were chosen based on similarity to the intervention group prior to VBF implementation in industry classification, member geography of residence, and medication co-payment tiers.

Measures

The analysis was performed at the individual-member level. For each member in our sample, we obtained monthly measures on demographics (age, sex, zip code of residence, relationship to employee), prescriptions fills (National Drug Code, generic code number, number of days’ supply, date dispensed, place of purchase [retail or mail order pharmacy]), expenditures (amount paid by member, amount paid by health plan), and plan characteristics (benefit renewal month and medical benefit relativity value). The medical benefit relativity value is an index of medical benefit generosity commonly used in health insurance actuarial analyses that takes into account a large number of plan cost-sharing and utilization characteristics (eg, deductibles, co-payments, co-insurance, out-of-pocket maximums, prior authorization, quantity limits).15-17 The values range between 0 and 1; a value of 0.75 indicates that a health plan pays 75% of medical expenses and the member pays the remaining 25% for a typical market basket of healthcare interventions.

Patient Cohorts

Because the VBF benefit change was implemented across the entire population of members, we aimed to assess the effect of the VBF on this population by constructing prevalent disease cohorts. For these cohorts, individuals were not required to have been given a new diagnosis or to be newly initiating a drug of interest to be included. We identified disease cohorts using International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes (diabetes mellitus: 250.xx; hypertensive disease: 401. xx-404.xx; disorders of lipoid metabolism: 272.xx).

Patients were included in the analysis if they had a diagnosis of interest in the 4-year period prior to VBF implementation and if they had filled a retail or mail order drug of interest based on the American Hospital Formulary Service system of therapeutic classification—the most widely used drug classification system in the United States and Canada.18 The drugs of interest for the diabetes cohort were oral antidiabetic agents: alpha-glucosidase inhibitors (68:20.02), biguanides (68:20.04), dipeptidyl peptidase IV inhibitors (68:20.05), meglitinides (68:20.16), sodium-glucose cotransporter 2 inhibitors (68:20.18), sulfonylureas (68:20.20),thiazolidinediones (68:20.28). Nonoral antidiabetic agents, such as insulins, were not included in the analysis because there is no valid approach to measuring adherence of the nonoral agents using claims data. The drugs of interest for the hypertension cohort were oral antihypertensive agents: central alpha-agonists (24:08.16), beta-adrenergic blocking agents (24:24), calcium-channel blocking agents (24:28), renin-angiotensin-aldosterone system inhibitors (24:32), loop diuretics (40:28.08), potassium-sparing diuretics (40:28.16), thiazide diuretics (40:28.20), and thiazide-like diuretics (40:28.24). The drugs of interest for the hyperlipidemia cohort were antilipemic agents (24:06:xx). The first claim for a drug of interest was considered a patient’s index date, and both adherence and expenditure were measured beginning from this date. Patients were followed until they lost insurance eligibility or the study period ended.

Outcomes: Adherence and Expenditures

We measured medication adherence using the proportion of days covered (PDC) metric for each of the 3 disease cohorts.19 The PDC is defined as the proportion of days that a given cohort member in a given month had at least 1 disease cohort–specific drug of interest based on the initial prescription fill date(s) and the days’ supply.20 Specifically, the denominator was calculated as the sum of days from the index date or first day of the month (whichever occurs last) to the last day of the month or date of disenrollment from the plan (whichever occurs first). The PDC numerator was calculated as the sum of the days that a member had at least 1 disease cohort–specific drug of interest during the period defined in the denominator. This results in a continuous PDC measure between 0 and 1.

Consistent with standard PDC calculations, in the case of early refills, we assumed that the prior supply was taken fully before the new supply was initiated.21 Hence, the new supply is added to the prior supply in the calculation of the numerator, allowing for a “carryover” in the calculation. Drug supply extending beyond the end of 1 month was added to the drug supply for the following month. Drug supply extending beyond the end of the study period was truncated at the end of the study period such that the maximum PDC is 1. For expenditures, we measured member, health plan, and overall medication expenditures for the disease cohort–specific drugs of interest per member per month (PMPM).

Statistical Analysis

Because we aimed to estimate the average effect of the VBF among those subject to the VBF policy, we sought to compare the observed outcomes among VBF members with the expected outcomes for the same group of VBF members had the VBF not been implemented. We obtained the expected estimate by using the contemporaneous observed outcomes in the 5 control plans that were not exposed to the VBF, after adjusting for the covariate distributions in both the groups. We confirmed the similarity of the control group to the intervention group in pre-VBF outcomes trends by examining both the statistical significance and magnitude of the regression coefficients that represented the differential trends in the groups prior to VBF implementation.

We generated expected outcomes by using generalized linear models. To model PDC, we used a binomial distribution with logit link with iterated, reweighted least-squares optimization of the deviance. To model medication expenditures, we used 2-part models. We first used binomial distribution and logit link to model the probability of incurring expenditures and gamma distribution with log link to model expenditures among those who have incurred expenditures. We assessed overall model fit by using the following goodness-of-fit tests: Pearson’s correlation test, Pregibon link test, and a modified Hosmer-Lemeshow test.22,23 We modeled correlations between monthly observations within members using robust variance estimators.24 We generated standard errors and confidence intervals for all estimates using 1000 bootstrap replications.

 
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