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.
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
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.
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.
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.
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.
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.
We also evaluated the impact of CMS assignment methods by comparing enrollee characteristics across the benchmark PDPs within each of the 3 largest PDP regions: New York, Texas, and California. We expected comparable enrollee characteristics between plans, except for assignment year and age because some plans had operated as benchmark plans for more years and, thus, would have received more random assignees compared with plans that achieved benchmark status in recent years. These older benchmark plans would also have retained an older pool of enrollees. Beneficiary age and assignment year were also included as covariates in the analysis.
In univariate analysis, we performed type 3 tests to examine the overall effect of the composite groups of formulary restrictions on each study outcome. We estimated 3 sets of random intercept regression models in which dependent variables were GDR, mean cost per prescription fill, and PDC. In each regression model, we included a random effect to account for unexplained variability between plans. Formulary restrictions were modeled as fixed effects. Count of generics available without charge via mail order, state law for generic substitution, PDP region, assignment year, and beneficiary age were included as covariates in the models and treated as fixed effects. We used SAS Version 9.3 (SAS Institute; Cary, North Carolina) for all statistical analyses.
A total of 28,082 beneficiaries were eligible for the OHA cohort; 53,864 for the statin cohort; and 57,289 for the RAS antagonist cohort (see eAppendix Figure for details about cohort selection). Approximately 30% of all study subjects resided in New York, Texas, and California in 2012 (eAppendix Table 2). As expected, enrollee characteristics were largely comparable across benchmark PDPs within each of these 3 regions except for assignment year and age (eAppendix Tables 3-11).
From our assessment of the benchmark PDP formulary designs in 2012, we found consistent patterns in formulary restrictions for OHAs, statins, and RAS antagonists. For most of the drugs analyzed, plans either fully restricted their use (all strengths restricted) or applied no restrictions at all (all strengths available). Formulary restrictions were mostly placed on brand name drugs, whereas almost all generic drugs were readily available on formulary. In addition, the benchmark PDPs appeared to have 3 formulary approaches for handling brand name drugs (Tables 2-4). From most restrictive to most generous, these directives were: 1) placing restrictions on all brand name drugs, 2) selectively covering brand name drugs without restrictions, and 3) covering all single-source brand name drugs and commonly used multi-source brand name drugs without restrictions.
The top panels in Tables 2-4 present descriptive statistics for annual days of supply for every drug that accounted for at least 1% of overall utilization in the LIS population, beginning with OHAs (Table 2), statins (Table 3), and RAS antagonists (Table 4). These statistics are displayed by the groups of formulary restrictions described in Table 1. Placing formulary restrictions on a drug was associated with lower utilization of that medication, and the impact was more pronounced among statins and RAS antagonists than among OHAs.
Utilization of generic drugs was much higher among beneficiaries enrolled in plans that restricted access to brand name drugs. Compared with those enrolled in plans that placed no formulary restrictions on the 4 statins under study (Table 3), beneficiaries who faced restrictions in obtaining rosuvastatin (single-source brand name), atorvastatin (multi-source brand name), and ezetimibe-simvastatin (single-source brand name) not only had considerable fewer annual days of supply for the 3 drugs—rosuvastatin (9.10 for enrollees in plans with restrictions vs 35.41 for patients in plans with no restrictions), brand name atorvastatin (1.61 vs 20.52, respectively), and ezetimibe-simvastatin (1.13 vs 7.99)—but they also had higher use of generic atorvastatin (58.99 vs 45.86), generic lovastatin (21.27 vs 16.66), generic pravastatin (48.72 vs 39.21), and generic simvastatin (136.30 vs 120.77). Similarly, beneficiaries who were subject to restrictions in accessing single-source brand name angiotensin II receptor blockers (ARBs), including olmesartan, valsartan, and valsartan-hydrochlorothiazide, had more days of supply for generic ARBs versus patients in plans not requiring plan approval (losartan: 50.18 vs 27.91, respectively; losartan-hydrochlorothiazide: 14.89 vs 8.57, respectively) (Table 4).
The bottom panels of Tables 2-4 present mean values for each of the study outcomes. The mean GDRs for the 3 OHA restriction groups varied from 0.83 for plans with no restrictions to 0.84 for plans restricting only saxagliptin (single-source brand name) and 0.88 for plans restricting brand name pioglitazone and single-source brand name dipeptidyl peptidase-4 (DPP-4) inhibitors (ie, saxagliptin, sitagliptin, and sitagliptin-metformin). The mean GDR for statins was also lowest for plans with no formulary restrictions (0.77), climbing to 0.95 for plans placing restrictions on brand name atorvastatin and single-source brand name statins (ie, rosuvastatin and ezetimibe-simvastatin). For RAS antagonists, plans with no restrictions again exhibited the lowest mean GDR (0.80), with the highest mean GDR (0.95) observed among plans restricting single-source brand name ARBs (eg, olmesartan, valsartan, and valsartan-hydrochlorothiazide). Mean costs per prescription fill were inversely related to GDR. The range for OHAs was $54.54 in plans with the most restrictions to $71.70 in plans with no formulary restrictions for the 4 OHAs under study. The range for statins was $40.49 to $73.04, respectively, and for RAS antagonists, $20.78 to $45.74, respectively. The differences in PDCs across plans by formulary restriction were small; in no instance was the difference greater than 0.04.
These relationships persisted after covariate adjustment for assignment year, beneficiary age, count of generics available for free via mail order, PDP region, and state law for generic substitution (Table 5). Regarding OHAs, restricting the use of brand name pioglitazone and single-source brand name DPP-4 inhibitors was associated with a GDR that was 3.0 percentage points higher (P <.0001) and a cost per prescription fill that was $10.80 lower (P = .0001) for OHAs. Restricting access to brand name atorvastatin and single-source brand name statins was linked to a GDR that was 14.9 percentage points higher (P <.0001) and a cost per prescription fill that was $29.60 lower (P <.0001) for statins. Restricting single-source brand name statins was associated with a cost per prescription fill that was $25.60 lower (P = .0158), and restricting brand name atorvastatin and single-source brand name ezetimibe-simvastatin was related to a reduction of $12.40 (P = .0399). Placing restrictions on single-source brand name ARBs was related to a GDR that was 15.0 percentage points higher (P <.0001) and a cost per prescription fill that was $27.20 lower (P <.0001) for RAS antagonists. Restricting only olmesartan was linked to a GDR that was 3.8 percentage points higher (P = .0434) for RAS antagonists.
The estimated effects of formulary restrictions on overall adherence were minimal. Restricting access to brand name pioglitazone and single-source brand name DPP-4 inhibitors was linked to a PDC that was 0.4 percentage points higher (P = .3508), but the association was not statistically significant. Beneficiaries facing formulary restrictions of brand name atorvastatin and single-source brand name statins, on average, had a PDC that was 0.9 percentage points lower (P = .1197) compared with those that were not subject to such restrictions. Restricting the use of single-source brand name ARBs was related to a PDC that was 1.3 percentage points lower (P = .0211).
Stringent state laws for generic substitution (mandating generic substitution and only allowing brand name drugs by provider request) were associated with a GDR that was 3.3 percentage points higher for OHAs (P = .0120) and 3.0 percentage points higher for RAS antagonists (P = .0091). Providing free mail order prescriptions for 1 additional generic statin was associated with a GDR that was 1.5 percentage points higher (P = .0360).
We found that placing formulary restrictions on brand name drugs generally shifted utilization away from brand name agents toward inexpensive generics. The effects were consistent across the 3 drug classes, but the magnitude of impact was smaller for OHAs than for statins or RAS antagonists. LIS recipients who faced restrictions in accessing brand name drugs had 34.9% (38.80 vs 25.25 days) fewer annual days of supply for sitagliptin than their counterparts who could freely access these drugs, 74.3% (34.41 vs 9.10 days, respectively) fewer days of supply for rosuvastatin, and 95.7% (28.06 vs 1.22 days, respectively) fewer days of supply for valsartan (Tables 2-5). Correspondingly, the difference in GDR between the restricted and unrestricted groups was smaller for OHAs (0.88 vs 0.83, respectively) than for statins (0.95 vs 0.77) and RAS antagonists (0.95 vs 0.80).
This varying magnitude of impact might be due to differences in the availability of therapeutically equivalent agents. There were multiple generic statins and generic ARBs available in 2012, whereas all DPP-4 inhibitors were single-source brand name drugs. Clinical guidelines on diabetes management recommend adding a DPP-4 inhibitor, sulfonylurea, or thiazolidinedione (TZD) as a second oral agent for patients who cannot achieve their glycemic target with metformin monotherapy.15 We observed that beneficiaries who either did or did not face restrictions on DPP-4 inhibitors had comparable utilization of sulfonylureas and TZDs, indicating lack of substitution. Although the findings from the regression analysis suggested that restricting access to DPP-4 inhibitors has minimal impact on overall OHA adherence, further study is needed to investigate concurrent adherence among patients treated with multiple OHAs and the clinical impact of formulary restrictions.
This study addressed the intended and unintended consequences of formulary restrictions, an understudied but important research topic given the near-ubiquity of these tools in modern formulary management.2 The lack of literature is partially due to complexity in deciphering formulary designs, especially if a large number of drug products are involved. Most prior studies are limited to providing descriptive statistics regarding shares of restricted drugs or evaluating formulary policy changes for particular drugs.2,16,17 Our analysis of the benchmark for PDPs formularies contributed valuable insight in identifying meaningful formulary restrictions and understanding how they may influence medication utilization patterns. To the best of our knowledge, this is the first study that has systematically evaluated formulary restrictions and their impact on medication utilization and costs in the context of an entire drug class. It is a methodological advancement because utilization of a drug is not only influenced by its own ease of access, but also formulary availability of other agents in the same therapeutic class.
The study estimates for the effects of formulary restrictions have high internal validity. By focusing on the randomized LIS recipients, we were able to minimize potential bias by isolating the effects of formulary restrictions from that of cost-sharing rules and removing confounding effects of virtually all beneficiary characteristics we could observe—and, by extension, other unobserved factors that represented critical threats to internal validity in conventional observational studies.
Additionally, LIS recipients represent about 40% of Part D enrollees, yet account for over 55% of total drug spending.18 This is a socially and economically disadvantaged population that often lives with disabilities and multiple chronic conditions. They tend to have worse health outcomes, yet significantly higher healthcare expenditures.9 Such disparities suggest the need to improve both quality and efficiency of care for this vulnerable population.
In 2012, full LIS recipients paid at most $2.60 for generics and $6.50 for brand name medications.8 We found that neither the availability of free generics via mail order nor generous coverage of brand name medications seemed to meaningfully affect LIS recipients’ overall adherence to OHAs, statins, and RAS antagonists. Hence, LIS recipients’ suboptimal adherence may be due to behavioral or clinical causes rather than costs or access issues.9,19 Part D plan sponsors and other healthcare decision makers may consider behavioral interventions to improve LIS recipients’ perceived importance of medication adherence and disease management skills.
This study has a few limitations. First, the study subjects were randomized to plans rather than varying formulary restrictions. Hence, the observed effects of formulary restrictions may be confounded by other plan-level policies that also affect medication utilization patterns. We observed that several benchmark PDPs provided free mail order prescriptions for commonly used generics, which would incentivize beneficiaries to take generics instead of brand name drugs and use more medications overall. We also observed that Envision RxPlus Silver did not offer 90-days-supply prescriptions in 2012, whereas all the other benchmark PDPs did. Conceptually, shorter length of drug supply per prescription fill presents more opportunities for filling prescriptions late and may lead to gaps in medication usage. To eliminate these 2 plan-level policies as alternative explanations for the observed effects of formulary restrictions on the study outcomes, we excluded beneficiaries enrolled in the Envision RxPlus Silver plan from all analyses and included a covariate for count of generics available for free via mail order in the regression models. In addition, we estimated random intercept regression models to recognize variability in unobserved plan attributes.
Second, our randomized cohorts of LIS beneficiaries were not strictly comparable from plan to plan within PDP regions. Because Part D regional benchmark thresholds are determined annually through a competitive bidding process, plans can lose or gain benchmark status from year to year. When a plan loses benchmark status, its random assignees would be reassigned to other plans (however, there is an exception when the plan premium is just above the benchmark threshold). Additionally, the LIS recipients were randomized at different times. This dynamic randomization process resulted in small differences in beneficiary assignment year and age. To mitigate the potential confounding effects of beneficiaries’ tenure in the plan and age, we included both assignment year and age as covariates in the regression analysis.
Readers should use caution in generalizing the findings of this study to non-LIS beneficiaries. Compared with non-LIS beneficiaries, LIS recipients tend to be younger, more socioeconomically disadvantaged, and more likely to live with disabilities and multiple chronic conditions. Nevertheless, both LIS and non-LIS beneficiaries in each plan faced the same formulary policies and appeal process for requesting coverage exceptions. Although the impact of formulary restrictions on the study endpoints may differ in magnitude, we expected the nature of the relationships to hold in non-LIS populations. Last, our study focused on existing users of OHAs, statins, and RAS antagonists. Future research may consider evaluating the impact of formulary noncoverage and UM tools on treatment initiation among individuals who are candidates for those chronic medications.
Placing formulary restrictions on brand name drugs shifts drug utilization toward generics and lowers cost per prescription fill, but has minimal impact on overall adherence for OHAs, statins, and RAS antagonists among Medicare Part D enrollees receiving LIS. Author Affiliations: Department of Pharmaceutical Health Services Research (XS, BCS, SET, EMP), and The Peter Lamy Center on Drug Therapy and Aging (BCS), and Department of Epidemiology and Public Health (LSM), University of Maryland, Baltimore, MD; Information Products Group, Office of Information Products and Data Analytics, Center for Medicare and Medicaid Services (CAP), Baltimore, MD.
Source of Funding: This work was supported by a Young Investigator Adherence Award funded by the Pharmaceutical Research and Manufacturers of America (PhRMA) Foundation. The funder played no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. The views expressed in this paper are those of the authors and no official endorsement by HHS or CMS is intended or should be inferred.
Authorship Information: Concept and design (XS, LSM, EMP); acquisition of data (XS, BCS); analysis and interpretation of data (XS, LSM, EMP); drafting of the manuscript (XS, LSM); critical revision of the manuscript for important intellectual content (XS, BCS, CAP, SET, LSM, EMP); statistical analysis (XS, LSM); obtaining funding (XS); administrative, technical, or logistic support (CAP); and supervision (BCS, CAP, SET).
Address Correspondence to: Xian Shen, PhD, University of Maryland, Baltimore, 220 Arch St, Rm 12-328, Baltimore, MD 21201. E-mail: firstname.lastname@example.org.REFERENCES
1. Report to the Congress: Medicare payment policy. chapter 13: status report on Part D. Medicare Payment Advisory Commission website. http://www.medpac.gov/docs/default-source/reports/chapter-13-status-report-on-part-d-march-2016-report-.pdf. Published March 2016. Accessed June 29, 2017.
2. Hargrave E, Hoadley J, Summer L, et al. Medicare Part D formularies, 2006-2010: a chartbook. The Kaiser Family Foundation website. http://22.214.171.124/documents/Oct10_PartDFormulariesChartBook_CONTRACTOR_RS.pdf. Published September 2010. Accessed June 28, 2017.
3. Hoadley J, Summer L, Hargrave E, Cubanski J, Neuman T. Analysis of Medicare prescription drug plans in 2012 and key trends since 2006. Kaiser Family Foundation website. www.kff.org/medicare/report/medicare-rx-drug-plans-2012-and-key-trends/. Published September 1, 2012. Accessed July 13, 2017.
4. Auto- and facilitated enrollment of low income beneficiaries. CMS website. https://www.cms.gov/Medicare/Eligibility-and-Enrollment/LowIncSubMedicarePresCov/AutoandFacilitatedEnrollmentofLowIncomeBeneficiaries.html. Published March 26, 2012. Updated March 26, 2012. Accessed June 29, 2017.
5. Reassignment. CMS website. https://www.cms.gov/Medicare/Eligibility-and-Enrollment/LowIncSubMedicarePresCov/Reassignment.html. Updated April 18, 2017. Accessed June 29, 2017.
6. Summer L, Nemore P, Finberg J. Medicare Part D: how do vulnerable beneficiaries fare? Issue Brief (Commonw Fund). 2008;35:1-11.
7. Aruru MV, Salmon JW. Development of a Medicare beneficiary comprehension test: assessing Medicare Part D beneficiaries’ comprehension of their benefits. Am Health Drug Benefits. 2013;6(8):453-461.
8. Medicare prescription drug benefit manual. Chapter 13—premium and cost-sharing subsidies for low-income individuals. CMS website. https://www.cms.gov/regulations-and-guidance/guidance/transmittals/downloads/chapter13.pdf. Published July 29, 2011. Accessed June 29, 2017.
9. Wei II, Lloyd JT, Shrank WH. The relationship between the low-income subsidy and cost-related nonadherence to drug therapies in Medicare Part D. J Am Geriatr Soc. 2013;61(8):1315-1323. doi: 10.1111/jgs.12364.
10. Medicare prescription drug benefit manual. Chapter 3—eligibility, enrollment and disenrollment. CMS website. https://www.cms.gov/Medicare/Eligibility-and-Enrollment/MedicarePresDrugEligEnrol/Downloads/CY-2015-PDP-Enrollment-and-Disenrollment-Guidance.pdf. Published August 19, 2011. Accessed June 29, 2017.
11. Updated research findings on the impact of socioeconomic status on Star Ratings. CMS website. https://www.cms.gov/Medicare/Prescription-Drug-Coverage/PrescriptionDrugCovGenIn/PerformanceData.html. Published September 8, 2015. Accessed June 29, 2017.
12. Part C and D performance data. CMS website. https://www.cms.gov/Medicare/Prescription-Drug-Coverage/PrescriptionDrugCovGenIn/PerformanceData.html. Accessed June 29, 2017.
13. Jung K, McBean AM, Kim JA. Comparison of statin adherence among beneficiaries in MA-PD plans versus PDPs. J Manag Care Pharm. 2012;18(2):106-115.
14. Medicare 2014 Part C & D Star Rating technical notes. CMS website. http://www.cms.gov/Medicare/Prescription-Drug-Coverage/PrescriptionDrugCovGenIn/PerformanceData.html. Accessed June 29, 2017.
15. American Diabetes Association. Standards of medical care in diabetes—2014. Diabetes Care. 2014;37(suppl 1):S14-S80. doi: 10.2337/dc14-S014.
16. Cox ER, Kulkarni A, Henderson R. Impact of patient and plan design factors on switching to preferred statin therapy. Ann Pharmacother. 2007;41(12):1946-1953.
17. Lu CY, Law MR, Soumerai SB, et al. Impact of prior authorization on the use and costs of lipid-lowering medications among Michigan and Indiana dual enrollees in Medicaid and Medicare: results of a longitudinal, population-based study. Clin Ther. 2011;33(1):135-144. doi: 10.1016/j.clinthera.2011.01.012.
18. Hoadley J, Summer L, Hargrave E, Stromberg S, Cubanski J, Neuman T. To switch or be switched: examining changes in drug plan enrollment among Medicare Part D low-income subsidy enrollees. Kaiser Family Foundation website. http://kff.org/medicare/report/to-switch-or-be-switched-examining-changes-in-drug-plan-enrollment-among-medicare-part-d-low-income-subsidy-enrollees/. Published July 17, 2015. Accessed June 29, 2017.
19. Stuart B, Yin X, Davidoff A, et al. Impact of Part D low-income subsidies on medication patterns for Medicare beneficiaries with diabetes. Med Care. 2012;50(11):913-919.