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The American Journal of Managed Care May 2012
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Medication Adherence Changes Following Value-Based Insurance Design
Joel F. Farley, PhD; Daryl Wansink, PhD; Jennifer H. Lindquist, MStat; John C. Parker, PhD; and Matthew L. Maciejewski, PhD
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Medication Adherence Changes Following Value-Based Insurance Design

Joel F. Farley, PhD; Daryl Wansink, PhD; Jennifer H. Lindquist, MStat; John C. Parker, PhD; and Matthew L. Maciejewski, PhD
Value-based insurance design copayment reductions sustained medication adherence 2 years into policy implementation and were most effective in patients with poor adherence before policy implementation.
Objectives: To determine whether participation in a value-based insurance design (VBID) program was associated with improved medication adherence in 8 drug classes 2 years after implementation and to examine whether adherence changes varied by baseline adherence.

Study Design: We used a pre-post quasi-experimental study design with a retrospective cohort of 74,748 enrollees using 8 different therapeutic classes of medications to treat diabetes, hypertension, hyperlipidemia, or congestive heart failure.

Methods: Brand-name medication copayments were lowered (from tier 3 to tier 2) for all enrollees, while generic copayments were waived only for employers who opted into the VBID program. Medication adherence of VBID program participants and nonparticipants 12 months before and 12 and 24 months after program implementation were estimated on 8 propensity-matched cohorts using generalized estimating equations, as well as on subgroups stratified by baseline adherence. Adherence was measured using the medication possession ratio (MPR) from medication refill records.

Results: VBID was associated with improved medication adherence ranging from 1.4% to 3.2% at 1 year, which increased to 2.1% to 5.2% 2 years following VBID adoption. Adherence changes were most notable among patients who were nonadherent (MPR <.50) before VBID implementation.

Conclusions: Population-based implementation of VBID can improve adherence to medications to treat cardiometabolic conditions, particularly for previously nonadherent patients. VBID guidelines being developed in response to healthcare reform should account for the heterogeneity in patient response to VBID programs.

(Am J Manag Care. 2012;18(5):265-274)
We showed significant clinical improvements in medication adherence among patients who experienced a reduction in copayments following the implementation of a valuebased insurance design (VBID) copayment program by a larger private insurer.

  •  VBID adherence improvements were sustained and improved to a greater extent 2 years into policy adoption and were greatest among patients with poorer adherence prior to policy implementation.

  • Further studies examining the economic effect of VBID medication copayment policies are needed to better understand the overall effect of these policies on managed care decision makers and patients.
Adherence to essential medications is suboptimal for many patients with chronic medical conditions,1,2 resulting in significant morbidity and mortality.3,4 Cost-related nonadherence (CRN) has been well documented as a significant problem in uninsured patients.5-7 Cost-related nonadherence leads patients to forgo medication, skip doses, ration medication by splitting tablets, or forgo other necessities. Insured patients also face CRN due to ever-increasing medication copayments and coinsurance.8,9

One policy innovation that targets CRN in the context of health insurance is value-based insurance design (VBID). Value-based insurance design posits that cost-sharing should be set according to a medication’s clinical value instead of its acquisition cost.10 Following principles of VBID, policy makers may set copayments lower for medications that are more effective or more cost-effective than other medications in the same drug class. An alternative VBID approach would reduce cost sharing for certain populations of patients or subgroups who are most likely to benefit from improved access to treatment.11 The removal of cost barriers for high-value medications under VBID should lead to better medication adherence, better disease management, and lower healthcare spending. Existing VBID evaluations suggest that eliminating copayments for high-value services may result in 1-year adherence improvements ranging from 1.5% to 3.8%, depending on the therapeutic category examined.12,13 However, few studies have examined adherence changes beyond 1 year,14,15 which would help insurers optimize patients’ behavioral response to VBID.

The objective of this study is to determine whether participation in a population-based VBID program was associated with improved adherence in 8 drug classes 2 years after implementation. We also wanted to address the concerns of VBID critics who argue that statistically significant, yet modest, improvements in adherence may not be clinically meaningful by examining adherence changes among populations with varying levels of prepolicy adherence.16,17 Previous research suggested a clinical threshold for adherence at 80%18-20 so we examined whether adherence changes differed between patients who were fully adherent (>80%), somewhat adherent (between 50% and 80%), or nonadherent (<50%) in the year before VBID implementation. The potential heterogeneity in patient response may reflect subgroups of patients for whom CRN is the underlying reason for nonadherence, and who might be exceptionally responsive to VBID. The results from this study extend a 1-year adherence analysis13 and identify which patients would most benefit from VBID programs. This is particularly relevant as the Secretary of Health and Human Services develops guidelines for VBID implementation under healthcare reform as stipulated in section 2713 of the Patient Protection and Affordable Care Act.


VBID Program

In January 2008, BlueCross BlueShield of North Carolina (BCBSNC) instituted a VBID program termed “Medication Dedication” for medications to treat diabetes, hypertension, hyperlipidemia, and congestive heart failure. Generic copayments for these medications were waived for all fully underwritten employers and for a subset of self-funded employers who opted in to this program. All employees and dependents at the employers who participated had the new benefit applied. In addition, brand-name copayments for 8 different therapeutic classes of medication (metformin, HMG-CoA reductase inhibitors [statins], thiazide diuretics, angiotensinconverting enzyme inhibitors [ACEIs], beta-blockers, calcium channel blockers [CCBs], angiotensin receptor blockers [ARBs], and cholesterol absorption inhibitors [CAIs]) were lowered from tier 3 to tier 2 for all enrollees. As a result of this policy, per prescription copayments for VBID participants compared with non-VBID participants declined on average from $15.57 to $2.42 versus $16.23 to $12.91, respectively, for ACEI users; from $15.05 to $2.07 versus $15.63 to $12.74, respectively, for beta-blocker users; from $13.13 to $5.17 versus $14.19 to $14.16, respectively, for metformin users; from $21.93 to $6.14 versus $23.95 to $16.28, respectively, for CCB users; from $24.89 to $19.46 versus $27.15 to $25.66, respectively, for statin users; from $16.91 to $9.14 versus $17.63 to $16.00, respectively, for thiazide users; from $36.31 to $32.28 versus $38.42 versus $32.65, respectively, for ARB users; and from $37.09 to $32.90 versus $40.41 to $33.90, respectively, for CAI users. This policy was population based because it was made available to all employers offering health benefits through BCBSNC in 2008. This study was approved by institutional review boards at both Duke University and the University of North Carolina at Chapel Hill.

Study Design and Sample

This VBID evaluation used a retrospective pre-post quasi-experimental study design with a nonequivalent control group. The 12 months prior to program implementation (January-December 2007) was the pre-period. Administrative claims data were used by BCBSNC to create annual observations for each of the variables described below between 2007 and 2009. Data for the post-period were drawn from the subsequent 24 months (2008 and 2009) to examine adherence changes 1 and 2 years after program implementation. The unit of analysis was the person-year with 3 observations per person.

VBID participants and nonparticipants were included if they were continuously enrolled from January 2007 through December 2009, did not have a change in their VBID enrollment status from 2008 to 2009, were 18 years or older in 2007, and were taking at least 1 of the 8 classes of drugs previously indicated in 2007. The comparison group of nonparticipants was selected from BCBSNC members enrolled in Administrative Services Only benefits. These patients also experienced a reduction in copayments for prescriptions in the 8 therapeutic categories examined from tier 3 to tier 2. However, copayments for generic medications were not eliminated. The same enrollee could be included in analyses for more than 1 class of drugs if using medications from 2 or more of the 8 classes. Given that these medications are all used for chronic health conditions, we used an intention to treat approach whereby patients in the analytical cohort were followed until the end of the study (2009).

After applying these criteria, we identified 5020 participants and 2883 nonparticipants taking metformin; 16,771 participants and 10,204 nonparticipants taking diuretics; 14,978 participants and 8234 nonparticipants taking ACEIs; 12,164 participants and 7298 nonparticipants taking beta-blockers; 21,635 participants and 12,804 nonparticipants taking statins; 8045 participants and 4834 nonparticipants taking CCBs; 3301 participants and 2073 nonparticipants taking CAIs; and 8688 participants and 5705 nonparticipants taking ARBs.

Of the 8 classes, 2 (CAIs and ARBs) did not have any generic options during the period of observation, so VBID participants and nonparticipants both experienced the same copayment reduction from tier 3 to tier 2. We expected similar changes in CAI and ARB adherence between VBID participants and nonparticipants because the copayments were statissimilar for both groups, so CAIs and ARBs were added as nonequivalent dependent variables to strengthen the design of the study.

Medication Adherence Outcome

Medication adherence was assessed using the continuous medication possession ratio (MPR), calculated as days of supply for a specific therapeutic class during each of the 3 annual observation periods. The MPR was calculated as the number of days of supply dispensed per year over the number of days observed in the year (365) and was capped at 1 for patients filling more days of supply than days observed. Adjustments to the days of supply in the MPR were made to account for carryover from previous medication fills, including carryover for medication fills that preceded each observation period including the pre-period (2007). If there were no fills for the drug during the 90 days before the start of the period, then the start date was the date of the member’s first fill for the drug during the period. The MPR accounted for medication switching between different drug therapeutic classes (eg, from a CCB to an ACEI) to avoid undercounting the supply of the drug (eg, a CCB) that members were no longer taking.

Explanatory Variables

There were 3 explanatory variables of interest: (1) an indicator of VBID participation, (2) a time indicator to reflect the pre-period or post-period(s) around VBID implementation, and (3) an interaction of the VBID participation and pre-post indicators. This interaction term indicates whether the pre-post adherence trends differed significantly between program participants and nonparticipants (eg, a differencein- difference analysis). Separate comparisons were made between 2007 and 2008 as well as 2007 and 2009 to understand the potential for VBID to result in both near-term and sustained adherence benefits.


Consistent with past research, each of the models included age in years, male sex, and comorbidity burden (measured as Episode Risk Groups) as covariates. In addition, we controlled for several covariates not accounted for in prior VBID analyses that reduced the extent of unobserved confounding by controlling for the count of unique medications filled, the average generic copayment per 30-day supply, the average brand-name copayment per 30-day supply, whether a patient filled at least 1 prescription with a 90-day supply, and the generic dispensing rate. In addition, patient use of case management or disease management during baseline was included to control for the influence of additional program participation on medication adherence.

Statistical Model Specification

To account for nonnormality in the adherence outcome, we used generalized estimating equations (GEEs) with a gamma distribution, inverse square root function link, robust standard errors to account for repeated measures, and identical covariate specifications for each of the 8 medication classes. Based on GEE results, the impact of the VBID program on adherence was assessed by comparing the difference in program participants’ predicted adherence and their predicted adherence had they not participated in the VBID program. The difference in these 2 predictions (done only for program participants) represents the “treatment effect for the treated.”

To reduce the nonequivalence of the control groups from the imbalance in observed covariates, one-to-one propensity score matching was conducted by iteratively matching program participants to nonparticipants from the eighth to the second digit of the propensity score in the mean adherence models. In the propensity score analysis, we included the covariates described above and 4 interaction terms (case management and disease management, male and case management, male and disease management, and total number of unique medications used and disease management). We present propensity-matched results, because our prior work found concordance between the unmatched adjusted results and matched results.13

Subgroup Analysis

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