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The American Journal of Managed Care August 2017
Health Insurance and Racial Disparities in Pulmonary Hypertension Outcomes
Kishan S. Parikh, MD; Kathryn A. Stackhouse, MD; Stephen A. Hart, MD; Thomas M. Bashore, MD; and Richard A. Krasuski, MD
Evaluation of a Hospital-in-Home Program Implemented Among Veterans
Shubing Cai, PhD; Patricia A. Laurel, MD; Rajesh Makineni, MS; Mary Lou Marks, RN; Bruce Kinosian, MD; Ciaran S. Phibbs, PhD; and Orna Intrator, PhD
The Effect of Implementing a Care Coordination Program on Team Dynamics and the Patient Experience
Paul Di Capua, MD, MBA, MSHPM; Robin Clarke, MD, MSHS; Chi-Hong Tseng, PhD; Holly Wilhalme, MS; Renee Sednew, MPH; Kathryn M. McDonald, MM, PhD; Samuel A. Skootsky, MD; and Neil Wenger, MD, MPH
What Do Pharmaceuticals Really Cost in the Long Run?
Darius Lakdawalla, PhD; Joanna P. MacEwan, PhD; Robert Dubois, MD, PhD; Kimberly Westrich, MA; Mikel Berdud, PhD; and Adrian Towse, MA, MPhil
The Hospital Tech Laboratory: Quality Innovation in a New Era of Value-Conscious Care
Courtland K. Keteyian, MD, MBA, MPH; Brahmajee K. Nallamothu, MD, MPH; and Andrew M. Ryan, PhD
Association Between Length of Stay and Readmission for COPD
Seppo T. Rinne, MD, PhD; Meredith C. Graves, PhD; Lori A. Bastian, MD; Peter K. Lindenauer, MD; Edwin S. Wong, PhD; Paul L. Hebert, PhD; and Chuan-Fen Liu, PhD
Risk Stratification for Return Emergency Department Visits Among High-Risk Patients
Katherine E.M. Miller, MSPH; Wei Duan-Porter, MD, PhD; Karen M. Stechuchak, MS; Elizabeth Mahanna, MPH; Cynthia J. Coffman, PhD; Morris Weinberger, PhD; Courtney Harold Van Houtven, PhD; Eugene Z. Odd
Cost-Effectiveness Analysis of Vagal Nerve Blocking for Morbid Obesity
Jeffrey C. Yu, AB; Bruce Wolfe, MD; Robert I. Griffiths, ScD, MS; Raul Rosenthal, MD; Daniel Cohen, MA; and Iris Lin, PhD
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Impact of Formulary Restrictions on Medication Use and Costs
Xian Shen, PhD; Bruce C. Stuart, PhD; Christopher A. Powers, PharmD; Sarah E. Tom, PhD, MPH; Laurence S. Magder, PhD; and Eleanor M. Perfetto, PhD, MS

Impact of Formulary Restrictions on Medication Use and Costs

Xian Shen, PhD; Bruce C. Stuart, PhD; Christopher A. Powers, PharmD; Sarah E. Tom, PhD, MPH; Laurence S. Magder, PhD; and Eleanor M. Perfetto, PhD, MS
Placing formulary restrictions on brand name drugs shifts use toward generics, lowers the cost per prescription fill, and has minimal impact on overall adherence for antidiabetes, antihyperlipidemia, and antihypertension medications among low-income subsidy recipients in Medicare Part D plans.
ABSTRACT

Objectives: To evaluate the effects of formulary restrictions on utilization and costs of oral hypoglycemic agents (OHAs), statins, and renin-angiotensin system (RAS) antagonists among low-income subsidy (LIS) recipients in Medicare Part D plans.

Study Design: We analyzed a 5% sample of 2012 Medicare data from the Chronic Conditions Data Warehouse together with a customized dataset capturing beneficiaries’ histories of plan assignment. 

Methods: We constructed 3 nonexclusive study cohorts comprising of users of OHAs, statins, and RAS antagonists. Eligible study subjects were LIS recipients randomized to benchmark plans. Formulary restrictions of interest were noncoverage, prior authorization, and step therapy. Study outcomes included generic dispensing rate (GDR), mean cost per prescription fill, and medication adherence based on proportion of days covered (PDC). Random intercept regression models were performed to estimate the effects of formulary restrictions on the study outcomes by drug class. 

Results: After covariate adjustment, beneficiaries who were subject to formulary restrictions on brand name pioglitazone and single-source brand name dipeptidyl peptidase-4 inhibitors (saxagliptin, sitagliptin, and sitagliptin-metformin) had a GDR 3 percentage points higher and a cost per prescription fill $10.8 less, but similar PDC compared with those who faced no restrictions. Restricting access to brand name atorvastatin and single-source brand name statins (rosuvastatin and ezetimibe-simvastatin) was associated with a GDR 14.9 percentage points higher and a cost per prescription fill $29.6 less, but with no impact on PDC. Restricting use of single-source brand name RAS antagonists (olmesartan, valsartan, and valsartan-hydrochlorothiazide) was associated with a GDR 15.0 percentage points higher, a cost per prescription fill $27.2 less, and a PDC 1.3 percentage points lower. 

Conclusions: Placing formulary restrictions on brand name drugs shifts utilization toward generic drugs, lowers the overall cost per prescription fill, and has minimal impact on overall adherence for OHAs, statins, and RAS antagonists among LIS recipients.

Am J Manag Care. 2017;23(8):e265-e274
Takeaway Points

Formulary restrictions—noncoverage, prior authorization, and step therapy—are increasingly employed by Medicare Part D plans to manage medication utilization and costs. This study evaluated the effects of formulary restrictions on utilization and costs of oral hypoglycemic agents (OHAs), statins, and renin-angiotensin system (RAS) antagonists among randomized low-income subsidy (LIS) recipients. 
  • Benchmark Part D plans tend to make generic drugs readily available on formulary and place restrictions on brand name drugs.
  • Formulary restrictions on brand name drugs shift utilization toward generic drugs, lower cost per prescription fill, and have minimal impact on overall adherence for OHAs, statins, and RAS antagonists among LIS recipients.
More than 30 million Medicare beneficiaries obtain prescription drug coverage through Part D each year.1 Although CMS requires Part D plans to include at least 2 drugs from each therapeutic class on their formulary, coverage of specific drugs may differ across plans. In 2010, some plans covered all drugs from the CMS drug reference file whereas other plans included as few as 63% of those drugs.2 Variation in coverage occurred not only to multisource brand name drugs, but also to single-source medications widely used by Medicare beneficiaries. In 2012, 5 of the 10 most frequently prescribed brand name drugs among Medicare beneficiaries were unavailable or excluded from formulary for more than 5% of all beneficiaries enrolled in standalone prescription drug plans (PDPs).3 Furthermore, use of prior authorization (PA) among covered drugs increased from 8% in 2007 to 20% in 2012.3

Formulary noncoverage and utilization management (UM) tools (eg, PA, step therapy [STEP]) are intended to facilitate the efficient use of medications and contain prescription drug costs. Under the Medicare Part D program, an enrollee, an enrollee’s representative, or an enrollee’s prescriber can request that the plan sponsor make a formulary exception or determine whether the enrollee has satisfied a UM requirement. This process is called coverage determination.4 If a plan sponsor issues an unfavorable coverage determination, the decision may be appealed through the Part D appeals process. In the standard process, the adjudication timeframe is 72 hours for a coverage determination and 7 days for the first appeal.5 Prior research suggests that many beneficiaries, particularly those with low socioeconomic status, have difficulty navigating their Part D benefit and understanding the coverage determination and exception process.6,7 These challenges may present hurdles for beneficiaries to obtain and adhere to the medications prescribed to them. However, little is known regarding the impact of noncoverage, PA, and STEP—collectively referred to as "formulary restrictions" in this paper—on medication utilization and costs and whether those formulary restrictions may cause unintended reduction in overall medication adherence.

Two analytic challenges exist in bridging this knowledge gap. First, plans often impose formulary restrictions—either together or complementarily with cost-sharing rules—to control medication utilization and costs; thus, it is analytically complex to isolate the effects of one from the other. Second, factors that influence beneficiaries’ selection of PDPs (eg, health beliefs, risk preferences, and expectations for medication needs) are typically unobservable, but may bias estimates of plan effects.

Our study addressed these 2 analytic challenges by exploiting a natural experiment in which CMS randomly assigned beneficiaries receiving low-income subsidies (LIS) to benchmark PDPs within regions.4,5 More than 90% of all LIS recipients received a full subsidy and paid the same nominal co-pays for prescription drugs regardless of plan cost-sharing rules.8,9 Hence, focusing the analysis on LIS recipients allowed evaluation of the effects of formulary restrictions, independent of plans’ cost-sharing rules. To further ensure that LIS recipients had affordable prescription drug coverage, CMS randomly assigned those who did not voluntarily select a Part D plan to one of the benchmark PDPs in their region. Each year, CMS also reassigns LIS recipients from plans that are terminating or charging premiums above regional benchmark thresholds.4,5,10 Randomization is expected to balance both observable and unobservable beneficiary characteristics among plans, thus creating a unique research opportunity for understanding how formulary restrictions affect medication utilization and costs.

Our study focused on 3 classes of medications commonly used by Medicare beneficiaries: oral hypoglycemic agents (OHAs), statins, and renin-angiotensin system (RAS) antagonists. Adherence outcomes for these 3 drug classes were included as quality measures in the Medicare Star Ratings program and assigned the highest weight in calculating overall Stars. Recent CMS investigations have revealed that receipt of LIS is associated with poorer outcomes for several Star measures, including suboptimal adherence for OHAs, statins, and RAS antagonists.11,12 Findings from this study can also provide Part D plans with valuable insights regarding the extent to which lifting formulary restrictions may improve LIS recipients’ medication adherence for the 3 drug classes.  

METHODS

Data Source


We analyzed a 5% sample of 2012 Medicare data from the Chronic Conditions Data Warehouse (CCW). The CCW formulary file provided detailed information regarding formulary restrictions for the drugs of interest in 2012. The CCW prescription drug event file contained claims for prescription drugs incurred by beneficiaries during the year, as well as information on Part D plan characteristics and beneficiaries’ utilization of inpatient, outpatient, and physician services. More details about the CCW data files can be found elsewhere.13 In addition, we obtained a customized dataset from CMS that captured beneficiaries’ histories of plan assignment, dating back to 2006. Through this file, we were able to identify LIS recipients who were randomly assigned to benchmark PDPs by CMS and who remained in their assigned plan as of January 1, 2012.

Study Cohorts

We constructed 3 nonexclusive study cohorts comprising users of OHAs, statins, and RAS antagonists. Eligible study beneficiaries must have been continuously enrolled in Medicare Parts A, B, and D throughout calendar year 2012 and have received LIS throughout 2012. In addition, eligible beneficiaries for a particular cohort must have had at least 2 prescription fills for the drugs of interest included in the study in 2012. We excluded beneficiaries who made plan choices prior to 2012, but allowed those who switched plans in mid-year 2012 to remain in the data analysis according to the intention-to-approach commonly used in clinical trials. For technical reasons detailed below, we also excluded beneficiaries enrolled in 1 Part D contract (ie, Envision RxPlus Silver; Twinsburg, Ohio) from the analysis.

Measures

The 3 outcome measures were generic dispensing rate (GDR), mean cost per prescription fill, and medication adherence based on the proportion of days covered (PDC) by any drugs in the class. GDR was calculated as annual days of supply for generics divided by annual total days of supply for all drugs in the class. Mean cost per prescription fill was determined by dividing the total drug expenditure by the total prescription fills for all drugs in the class. PDC was computed by dividing days covered by total observation days. Days covered by overlapping prescriptions for the same drug were adjusted by rolling over the overlapping days to cover later days when lack of drug supply was identified. Similarly, days covered during inpatient stays were applied to subsequent days with no drug supply. Days in inpatient facilities were removed from both the numerator and the denominator of PDC.8 PDC was capped at 1.

In our assessment of formulary restrictions, we only considered drugs accounting for at least 1% of overall utilization of the drug class in all LIS recipients. Among those drugs, the scope of analysis was further narrowed down to those with varying degrees of restriction across the benchmark PDPs in 2012. The rationale behind these 2 requirements was that the impact of formulary restriction of a specific drug cannot be detected unless the drug was prescribed in the study population and its access was restricted to different degrees across plans.

Formulary restrictions of interest were noncoverage (ie, off formulary), PA, and STEP. To identify off-formulary medications, we first identified all drug products from the drug lists used by the Medicare Star Ratings program and then cross-checked to see exclusions to each plan.12,14 Indicators for PA and STEP were available from the CCW formulary file. We measured the degree of restriction for each drug identified at the brand level versus the generic and dosage-form levels, which allowed us to examine how restricting access to the brand name version of a multi-source drug or specific dosage forms might impact study outcomes. The degree of restriction for a drug was categorized as: 1) none restricted, if no strengths of that drug were subject to noncoverage, PA, or STEP; 2) partially restricted, if at least 1, but not all, strengths of the drug were subject to noncoverage, PA, or STEP; and 3) all restricted, if all strengths of the drug were subject to noncoverage, PA, or STEP.

Notably, we observed perfect correlations in the degree of restriction among drugs in each of the 3 classes. Specifically, every plan that partially restricted generic simvastatin also partially restricted ezetimibe-simvastatin. Plans either fully restricted brand name pioglitazone, sitagliptin, and sitagliptin-metformin across the board or did not restrict any of the 3 drugs. Plans that fully restricted valsartan also fully restricted valsartan-hydrochlorothiazide. We constructed composite measures that grouped commonly occurring formulary restrictions and estimated the effects of each group on the study outcomes. Table 1 provides a list of drugs selected for analysis, the degrees of restriction observed, and groups of formulary restrictions formed (eAppendix Table 1 displays formulary restrictions on individual drugs [eAppendices available at ajmc.com]). As indicated in the table, 3 OHAs (sitagliptin, sitagliptin-metformin, saxagliptin), 2 statins (ezetimibe-simvastatin, rosuvastatin), and 3 RAS antagonists (olmesartan, valsartan all, valsartan-hydrochlorothiazide) were single-source drugs for which generics were unavailable in 2012.

Key covariates included a variable for count of generics available without charge via mail order, indicators for PDP regions, and a categorical variable representing state laws for generic substitution. Plan cost-sharing rules are generally not applicable to full LIS recipients as they pay flat co-pays for prescription drugs (at most $2.60 for generics and $6.50 for brand name drugs in 2012).8 However, in 2012, several plans provided free mail order prescriptions for commonly used generics in the 3 drug classes of interest. The covariate—the count of generics available for free via mail order—was developed to account for the effect of this practice at the plan level. Last, several PDP regions covered multiple states, some of which could have had more forceful state laws for generic substitution than others. To represent the strictness of the state laws, we constructed a categorical variable consisting of 3 values: 1) state law allows generic substitution, 2) state law mandates generic substitution but allows brand by patient request, and 3) state law mandates generic substitution but allows brand by provider request.

 
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