Currently Viewing:
The American Journal of Managed Care February 2015
A Multidisciplinary Intervention for Reducing Readmissions Among Older Adults in a Patient-Centered Medical Home
Paul M. Stranges, PharmD; Vincent D. Marshall, MS; Paul C. Walker, PharmD; Karen E. Hall, MD, PhD; Diane K. Griffith, LMSW, ACSW; and Tami Remington, PharmD
Quality’s Quarter-Century
Margaret E. O'Kane, MHA, President, National Committee for Quality Assurance
How Pooling Fragmented Healthcare Encounter Data Affects Hospital Profiling
Amresh D. Hanchate, PhD; Arlene S. Ash, PhD; Ann Borzecki, MD, MPH; Hassen Abdulkerim, MS; Kelly L. Stolzmann, MS; Amy K. Rosen, PhD; Aaron S. Fink, MD; Mary Jo V. Pugh, PhD; Priti Shokeen, MS; and Michael Shwartz, PhD
Currently Reading
Did Medicare Part D Reduce Disparities?
Julie Zissimopoulos, PhD; Geoffrey F. Joyce, PhD; Lauren M. Scarpati, MA; and Dana P. Goldman, PhD
Employers Should Disband Employee Weight Control Programs
Alfred Lewis, JD; Vikram Khanna, MHS; and Shana Montrose, MPH
Race/Ethnicity, Personal Health Record Access, and Quality of Care
Terhilda Garrido, MPH; Michael Kanter, MD; Di Meng, PhD; Marianne Turley, PhD; Jian Wang, MS; Valerie Sue, PhD; Luther Scott, MS
Leveraging Remote Behavioral Health Interventions to Improve Medical Outcomes and Reduce Costs
Reena L. Pande, MD, MSc; Michael Morris; Aimee Peters, LCSW; Claire M. Spettell, PhD; Richard Feifer, MD, MPH; William Gillis, PsyD
Decision Aids for Benign Prostatic Hyperplasia and Prostate Cancer
David Arterburn, MD, MPH; Robert Wellman, MS; Emily O. Westbrook, MHA; Tyler R. Ross, MA; David McCulloch, MD; Matt Handley, MD; Marc Lowe, MD; Chris Cable, MD; Steven B. Zeliadt, PhD; and Richard M. Hoffman, MD, MPH
Faster by a Power of 10: A PLAN for Accelerating National Adoption of Evidence-Based Practices
Natalie D. Erb, MPH; Maulik S. Joshi, DrPH; and Jonathan B. Perlin, MD, PhD, MSHA, FACP, FACMI
Differences in Emergency Colorectal Surgery in Medicaid and Uninsured Patients by Hospital Safety Net Status
Cathy J. Bradley, PhD; Bassam Dahman, PhD; and Lindsay M. Sabik, PhD
The Role of Behavioral Health Services in Accountable Care Organizations
Roger G. Kathol, MD; Kavita Patel, MD, MS; Lee Sacks, MD; Susan Sargent, MBA; and Stephen P. Melek, FSA, MAAA
Patients Who Self-Monitor Blood Glucose and Their Unused Testing Results
Richard W. Grant, MD, MPH; Elbert S. Huang, MD, MPH; Deborah J. Wexler, MD, MSc; Neda Laiteerapong, MD, MS; E. Margaret Warton, MPH; Howard H. Moffet, MPH; and Andrew J. Karter, PhD
The Use of Claims Data Algorithms to Recruit Eligible Participants Into Clinical Trials
Leonardo Tamariz, MD, MPH; Ana Palacio, MD, MPH; Jennifer Denizard, RN; Yvonne Schulman, MD; and Gabriel Contreras, MD, MPH
A Systematic Review of Measurement Properties of Instruments Assessing Presenteeism
Maria B. Ospina, PhD; Liz Dennett, MLIS; Arianna Waye, PhD; Philip Jacobs, DPhil; and Angus H. Thompson, PhD
Emergency Department Use: A Reflection of Poor Primary Care Access?
Daniel Weisz, MD, MPA; Michael K. Gusmano, PhD; Grace Wong, MBA, MPH; and John Trombley II, MPP

Did Medicare Part D Reduce Disparities?

Julie Zissimopoulos, PhD; Geoffrey F. Joyce, PhD; Lauren M. Scarpati, MA; and Dana P. Goldman, PhD
This study examined the impact of the Medicare Part D coverage gap on medication use by Hispanics, blacks, and whites with diabetes.
We assessed whether Medicare Part D reduced disparities in access to medication.

Study Design
Secondary data analysis of a 20% sample of Medicare beneficiaries, using Parts A and B medical claims from 2002 to 2008 and Part D drug claims from 2006 to 2008.

We analyzed the medication use of Hispanic, black, and white beneficiaries with diabetes before and after reaching the Part D coverage gap, and compared their use with that of race-specific reference groups not exposed to the loss in coverage. Unadjusted difference-in-difference results were validated with multivariate regression models adjusted for demographics, comorbidities, and zip code–level household income used as a proxy for socioeconomic status.

The rate at which Hispanics reduced use of diabetes-related medications in the coverage gap was twice as high as whites, while blacks decreased their use of diabetes-related medications by 33% more than whites. The reduction in medication use was correlated with drug price. Hispanics and blacks were more likely than whites to discontinue a therapy after reaching the coverage gap but more likely to resume once coverage restarted. Hispanics without subsidies and living in low-income areas reduced medication use more than similar blacks and whites in the coverage gap.

We found that the Part D coverage gap is particularly disruptive to minorities and those living in low-income areas. The implications of this work suggest that protecting the health of vulnerable groups requires more than premium subsidies. Patient education may be a first step, but more substantive improvements in adherence may require changes in healthcare delivery.

Am J Manag Care. 2015;21(2):119-128
We examined the impact of the Medicare Part D coverage gap on medication use by Hispanics, blacks, and whites with diabetes. These findings suggest that the Part D coverage gap was particularly disruptive to medication use for minorities and those of low socioeconomic status.
  • Hispanics, especially those residing in poorer areas, reduced medication use more than whites and blacks when they were required to bear the full costs of their medications.
  • Hispanics and blacks were more likely than whites to discontinue a therapy after reaching the coverage gap, but more likely to resume once coverage restarted.
  • Improving medication adherence and the health of vulnerable groups requires more than premium subsidies.
The primary objective of the Medicare Prescription Drug, Improvement, and Modernization Act was to provide seniors with affordable coverage for their prescription medications through the new Medicare Part D prescription drug benefit. This aim has largely been achieved as more than 35 million Medicare beneficiaries are now enrolled in Part D plans, and approximately 9 out of 10 report being satisfied with their plan.1 While Part D has reduced the financial burden of prescription drug spending for beneficiaries—particularly those with low incomes or extraordinarily high out-of-pocket drug expenses—whether the gap in coverage induced beneficiaries to change their use of medications or discontinue use of an effective therapy, and for whom the gap induced this behavior, is an empirical question.

The Part D benefit has a well-known gap in coverage commonly referred to as the “donut hole.” Under the standard benefit, beneficiaries who do not qualify to receive a Low-Income Subsidy (LIS) face a deductible, followed by a 25% coinsurance rate; but once they have spent up to a designated level on medications in a year ($2960 in 2015), they must start paying full price for their drugs. Only after a beneficiary reaches the “catastrophic” limit in out-of-pocket spending ($4700 in 2015) does coverage resume with minimal cost sharing thereafter. This nonlinear design is more complicated than a simple increase in patient cost sharing, as it alters both the current and future price of a drug. Once a non-LIS beneficiary reaches the coverage gap, each prescription he fills is likely to cost more. Yet, at the same time, each fill increases the likelihood of reaching the catastrophic threshold, which lowers the expected price of future prescriptions that year. Further, any price change in the gap is temporary since benefits reset at the beginning of the next calendar year. How beneficiaries— particularly those with low levels of education and resources— respond to changes in coverage over the course of the year is largely unknown.

Recent work finds that the Part D coverage gap reduces beneficiaries’ use of essential medications,2 but does not examine the differential responses of minorities and the nearpoor who do not qualify for federal subsidies. Racial and ethnic minorities have higher rates of chronic illness than nonminorities, and members of lower socioeconomic status (SES) groups are frequently less able to manage the complex treatment regimens often required in managing a disease.3 Indeed, black and Hispanic enrollees report greater difficulty obtaining information and purchasing needed medications in Part D.4

In this paper, we examined the effects of cycling in and out of coverage on the prescription drug use of racial and ethnic minorities and other vulnerable subgroups of Medicare beneficiaries. We compared changes in prescription drug use of white, black, and Hispanic beneficiaries before and after reaching the coverage gap for 2 different beneficiary groups: 1) those eligible for the full LIS who face minimal cost sharing and thus are unaffected by the coverage gap; and 2) nonsubsidized beneficiaries who pay the full cost of medications in the coverage gap (non-LIS). We estimated changes in medication use after reaching the gap separately by race, and we focused on beneficiaries with diabetes because it disproportionately affects ethnic minorities and is a major risk factor for a wide range of other health conditions. If the gap is prompting beneficiaries to use pharmaceuticals differently—especially if it leads them to discontinue an effective therapy—it should have been evident in this sample.



We used a 20% random sample of Medicare beneficiaries enrolled in Part D. This data set links enrollment and Parts A and B claims for traditional fee-for-service Medicare enrollees (2002 to 2008) to Part D claims (2006 to 2008). The Part A data includes information about inpatient hospital stays, including length of stay, diagnosis-related group, department-specific charges, and up to 10 individual procedure codes and diagnostic codes. Part B information includes claims submitted by physicians and claims from other healthcare providers and facilities for services reimbursed by Part B. Each claim contains diagnostic International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and Current Procedure Terminology-4 codes, dates of service, demographic information on beneficiaries, and a physician identification number.

The pharmacy data include all of the key elements related to prescription drug events (eg, drug name; National Drug Code, dosage, supply, date of service). Each pharmacy claim includes the amount of the LIS; the true out-of-pocket amount; and a field that indicates in which benefit phase a claim was made: deductible, precoverage gap, coverage gap, or catastrophic phase (or whether the claim straddles 2 of these phases). The Part D data identify the exact date that non-LIS beneficiaries entered and exited the coverage gap, as well as when LIS beneficiaries—not subject to the gap—reached the same levels of prescription drug spending associated with entrance into and exit from the gap.

The denominator file contains demographic information about each beneficiary including date of birth, gender, beneficiary type (eg, recipient, or not, of the LIS), and zip code of residence. We linked 5-digit zip codes to the American Community Survey to measure neighborhood socioeconomic status, including education (ie, level of schooling attained) and median household income. The Medicare data also include externally validated measures of race/ethnicity. Self-reported measures on race/ethnicity were refined using Research Triangle Institute estimates based on geography and first and last names.


The study sample consisted of Medicare beneficiaries 65 years and older with diabetes. Persons with diabetes commonly take medications for glycemic control, hypertension, and dyslipidemia, and proper medication adherence is associated with large reductions in both macro- and microvascular complications. Clinical trials consistently show that complications from this disease can be avoided or deferred with tight glycemic control.3,5 We identified beneficiaries with diabetes based on at least 1 inpatient or skilled nursing facility diagnosis, or 2 or more outpatient diagnoses of diabetes. We also assumed that a beneficiary with a Part D claim for insulin has diabetes. Once identified, beneficiaries were assumed to have diabetes in subsequent years.

We restricted our analysis to those enrolled in traditional fee-for-service Medicare and a stand-alone Part D drug plan. Individuals were required to have the same Part D contract/plan for the entire year. Our sample included 2 groups of beneficiaries: those receiving the full LIS and those not receiving any type of subsidy (non-LIS) and who thus had no gap coverage. LIS beneficiaries do not pay Part D premiums and face minimal cost sharing throughout the year. As a result, they are not subject to the coverage gap even when their level of drug spending reached the coverage gap threshold (eg, $2250 in 2006) and should not have reason to change their medication use before and after reaching the various (hypothetical) coverage thresholds. We used the LIS as controls and compared their medication use before and after reaching the gap to that of non- LIS beneficiaries, who face vastly different prices over the course of the year and spending distribution.

Given that 2006 was the initial year of the program and that beneficiaries could enroll up until May 15, we restricted our analyses to 2007 and 2008. Nonetheless, we used the 2006 data for risk adjustment, categorization of beneficiaries, and to compute medication use in 2007 for medications initiated in 2006 or earlier. In 2007, the study sample included 557,756 beneficiaries: 416,495 whites, 69,947 blacks, and 71,314 Hispanics.

Statistical Analysis

Our strategy was to estimate the difference in medication use before and after the coverage gap for a treatment (non-LIS) and control group (LIS), by drug class and race/ethnicity. We estimated race-specific changes in medication use before and after reaching the coverage gap for the non-LIS, and benchmarked these changes to race-specific changes in the medication use of LIS beneficiaries at similar levels of drug spending (ie, before and after reaching the “hypothetical” threshold of the coverage gap). We used multivariate regression to control for the variation in demographic and socioeconomic characteristics, and interacted binary indicators for each beneficiary group (LIS/non-LIS) with race/ethnicity. Standard errors were clustered at the individual level and computed using bootstrapping.

Our key outcome measure was medication adherence. We measured adherence using the Medication Possession Ratio (MPR), which is the fraction of days that a patient “possesses” or has access to medication, as measured by prescription fills. For example, a patient who filled a 30-day script on April 1 and refilled the prescription on May 10 would have an MPR of 75% for that period since they possessed 30 pills over a 40-day span. For each drug class, we computed the total days’ supply of medications before and after reaching the coverage gap to compute the percentage of compliant days for each individual in the sample. The remaining days’ supply at the end of 1 year was carried over to the subsequent year. We estimated changes in the rate of MPR, overall and by therapeutic class, as well as the proportion of all prescriptions dispensed as generic.

We also examined the fraction of white, black, and Hispanic beneficiaries who stopped using a class of medication after reaching the gap, and the fraction that resumed use in the first 90 days of the next year. Discontinuation was measured by comparing medication use within a therapeutic class in the 90 days prior to a beneficiary’s gap entry date and after reaching the gap. For example, a beneficiary observed taking an oral hypoglycemic, an antihypertensive, and a statin before reaching the gap, but only an oral hypoglycemic and an antihypertensive after entering the gap (for the remainder of the year) would be categorized as having discontinued 1 medication within the relevant classes. We also examined the extent to which beneficiaries switched medications after reaching the gap (from brand to generic), for classes that were neither brand- nor generic-dominated.

Copyright AJMC 2006-2019 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
Welcome the the new and improved, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up