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The American Journal of Managed Care August 2016
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Association of Part D Coverage Gap With COPD Medication Adherence
Yanni F. Yu, DSc, MA, MS; Larry R. Hearld, PhD; Haiyan Qu, PhD; Midge N. Ray, PhD; and Meredith L. Kilgore, PhD
Opinions on the Hospital Readmission Reduction Program: Results of a National Survey of Hospital Leaders
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Association Among Change in Medical Costs, Level of Comorbidity, and Change in Adherence Behavior
Steven M. Kymes, PhD; Richard L. Pierce, PhD; Charmaine Girdish, MPH; Olga S. Matlin, PhD; Troyen Brennan, MD, JD, MPH; and William H. Shrank, MD, MSHS

Association of Part D Coverage Gap With COPD Medication Adherence

Yanni F. Yu, DSc, MA, MS; Larry R. Hearld, PhD; Haiyan Qu, PhD; Midge N. Ray, PhD; and Meredith L. Kilgore, PhD
Using longitudinal Medicare claims data, this study quantified the association of the Medicare Part D coverage gap with medication adherence among beneficiaries with chronic obstructive pulmonary disease.

Objectives: This study assessed the association of the Medicare Part D coverage gap with medication adherence among beneficiaries with chronic obstructive pulmonary disease (COPD).

Study Design: Retrospective observational study based on Medicare claims data.

Methods: A 5% random sample of Medicare claims data (2006-2010) was used in this study. Beneficiaries diagnosed with COPD and treated with long-acting bronchodilators (LABDs) were assigned to an exposure cohort (at risk of the coverage gap) or a control cohort (otherwise). The exposure and control cohorts were matched using high-dimensional propensity scores. Adherence was defined as ≥80% of the proportion of days covered by LABDs. Logistic regressions controlling for unbalanced covariates post matching were applied to assess the association of the coverage gap with adherence.

Results: The final matched exposure and control cohorts each included 4147 patient-year observations with about 42% and 46% of them adherent to LABDs, respectively. About 17% of the exposure cohort hit the coverage gap after October 31. Logistic regression showed that, compared with the control cohort, the beneficiaries in the exposure cohort had a significantly lower likelihood of being adherent if they hit the coverage gap later in the year (odds ratio [OR], 0.603; 95% CI, 0.493-0.738), or had a lower likelihood without statistical significance if otherwise (OR, 0.931; 95% CI, 0.846-1.024).

Conclusions: The findings suggest that the Part D coverage gap was associated with lower adherence in patients with COPD, which may serve as evidentiary support for phasing out the
coverage gap by 2020.

Am J Manag Care. 2016;22(8):e275-e282

Take-Away Points
  • Medication adherence, in general, is not optimal in beneficiaries with chronic obstructive pulmonary disease (COPD). 
  • The Medicare Part D coverage gap is associated with lower adherence to COPD medication, with greater negative impact among the beneficiaries who reach the coverage gap later in the year. 
  • The timing of reaching the Part D coverage gap has effects on the level of medication adherence.
Chronic obstructive pulmonary disease (COPD) is characterized by progressive airway narrowing or airflow obstruction, that causes breathing difficulties, reduced exercise capacity, and physical limitation.1 Chronic lower respiratory disease, which primarily includes COPD, has become the third leading cause of death in the United States.2 COPD occurs more often in females and in the elderly, with almost half of all patients with COPD being 65 years or older.3 Pharmacotherapy is a cornerstone of COPD management, and maintenance medications are effective in controlling symptoms, maintaining lung function, and preventing COPD exacerbations.1,4

Previously, access to appropriate pharmacologic therapies to manage COPD was a challenge for Medicare beneficiaries due to the lack of coverage for prescription drugs. However, Part D, the Medicare prescription drug benefit, went into effect on January 1, 2006, and greatly expanded access to pharmacological therapies by subsidizing the cost of prescription drugs and prescription drug insurance premiums for Medicare beneficiaries. Pharmacologic treatments for COPD, such as bronchodilators and inhaled steroids, are now covered by Medicare Part D, with patients responsible for deductibles and co-payments. However, as per policies designed to keep the program financially sustainable, Part D does include a coverage gap for beneficiaries. Specifically, beneficiaries are financially responsible for the full cost of prescriptions once a certain dollar threshold has been reached, up to a maximum amount when catastrophic coverage begins, and then the Part D plan assumes 95% of the cost of prescriptions. These thresholds are established each year by Medicare at CMS.

In 20065 and in 2007,6 approximately 1.5 million and more than 3 million beneficiaries, respectively, were estimated to reach the coverage gap. Multiple studies have assessed the effect of the coverage gap on medication adherence in elderly patients7 and in those with different chronic diseases.8-14 These studies have shown that the use of brand name medications significantly decreased for most conditions during the coverage gap and substituted by generic drugs. However, there were no generic versions of long-acting bronchodilators (LABDs) available as a long-term maintenance therapy for COPD in the United States. No published studies have evaluated the effect of the Part D coverage gap on medication utilization among Medicare beneficiaries with COPD or explored whether patients in the coverage gap may behave differently due to the lack of generic options. This omission is problematic given the high prevalence of COPD among the elderly3 and the negative health and economic consequences of nonadherence.15,16 Therefore, the objective of this study was to assess the association between the Part D coverage gap among Medicare beneficiaries diagnosed with COPD and their adherence to COPD maintenance medications, LABDs.



Data Source

A 5% random sample of Medicare beneficiaries was used for this study. The Medicare administrative claims database is a comprehensive data source covering all beneficiaries who were enrolled in Medicare, capturing information on demographic characteristics, enrollment, prescription drug events, medical encounters in inpatient and outpatient settings, and health services incurred in other facilities, such as hospice or skilled nursing homes. This study was approved by the institutional review board and by the CMS Privacy Board.

Sample Selection

Inclusion and exclusion criteria. Because the coverage gap thresholds varied by calendar year, patient selection and outcome measures were employed at a calendar-year level. Considering that many Medicare beneficiaries did not have a full-year benefit in 2006, only data files from 2007 to 2010 were used for analysis, and the 2006 data file was used to describe patient baseline characteristics. Beneficiaries who met all of the following inclusion criteria were selected to form a general patient pool: a) had “of age” listed as the reason for Medicare eligibility (ie, age is ≥65 years as of 6 months prior to January 1 of a calendar year), b) had a full year’s eligibility during a respective calendar year and 6 months of eligibility prior to January 1 of the respective calendar year (the 6 months were defined as baseline period), c) had at least 2 outpatient claims with a diagnosis of COPD on different dates or at least 1 emergency department (ED) or inpatient claim with COPD as the primary diagnosis during a respective calendar year, and d) had at least 2 prescriptions for LABDs filled on different dates during a respective calendar year (LABDs are listed in eAppendix Table 1 [eAppendices available at]).

Beneficiaries were excluded from the study if they met at least 1 of the following criteria: a) were enrolled with a Medicare Advantage plan in any month during a respective calendar year; b) had a diagnosis of asthma during a respective calendar year, because some of the LABD medications are also indicated for asthma; c) had a diagnosis of cancer during a respective calendar year, because patients with cancer likely have different medication utilization and spending patterns compared with other Medicare beneficiaries; or d) had a disability or end-stage renal disease during a respective calendar year, because the benefits of such patients can differ substantially from those of other Medicare beneficiaries.

Study Cohorts

Beneficiaries who met the above selection criteria were divided into 2 study cohorts.

Control cohort. Beneficiaries were assigned to the control cohort if they fell into 1 of the following categories: a) had Medicare-Medicaid dual eligibility for the whole year, b) qualified for Part D low-income subsidies (LIS) (ie, received LIS for at least 1 month before and after they entered the coverage gap), or c) had additional benefits covering brand and generic drugs during the gap.

Exposure cohort and subgroups in exposure cohort. If beneficiaries did not have dual eligibility or low-income subsidies or full benefits to help with the coverage gap during a calendar year, they were assigned to the exposure cohort.

The exposure cohort was further categorized into 4 subgroups. Beneficiaries who did not reach the coverage gap in a respective year were identified as “no-reaching-gap subgroup.” This subgroup was not included in the final exposure cohort based on the assumption that beneficiaries who were relatively healthy were much less likely to reach the coverage gap; therefore, their medication-taking behavior was not expected to noticeably change as a result of presence of the coverage gap. Beneficiaries who reached the coverage gap before March 1 were identified as the “early-gap subgroup.” Similar to the “no-reaching-gap subgroup,” the early-gap subgroup was not included in the final exposure cohort based on the assumption that beneficiaries who reached the coverage gap early may have been very sick and wanted to maximize their medication usage during the gap period to enter the catastrophic phase sooner. This group of patients was anticipated to be small and to respond to the coverage gap differently than other subgroups.

Beneficiaries who reached the coverage gap between March 1 and October 31 were identified as the “mid-gap subgroup.” Finally, beneficiaries who reached the coverage gap on or after November 1 were identified as the “late-gap subgroup.” Thus, the final exposure cohort only included the mid-gap and the late-gap subgroups. Figure 1 depicts the subgroup designation within the exposure cohort.


Adherence was measured as the proportion of days covered (PDC). Yearly PDC was defined as the proportion of days covered by LABDs relative to the treatment period during a calendar year. The treatment period was calculated as the duration from the fill date of the first LABD prescription until the end of the year. A dichotomous variable for adherence was constructed as 1 if PDC ≥80%, and 0 otherwise.

The independent variable of interest was a dichotomous indicator of membership in the exposure or the control cohort (1 = exposure cohort, 0 = control cohort). In addition, the following demographic and clinical variables in the baseline period were also assessed: age, gender, ethnicity, residence region, Charlson Comorbidity Index (CCI) score (eAppendix Table 2), presence of major comorbidities (eg, diabetes, heart disease) (specified in eAppendix Table 3), number of unique prescription drugs defined by the first 9 digits of the national drug codes, number of all-cause ED visits, number of all-cause inpatient visits, previous COPD diagnosis, previous use of LABDs, previous supplemental oxygen therapy (eAppendix Table 4), and previous use of oral corticosteroids (eAppendix Table 5).

High-Dimensional Propensity Score Matching

In observational studies, selection bias is an important issue when comparing groups and exposure is not randomly assigned. Propensity score matching (PSM) is commonly used to generate comparable exposure and control cohorts with balanced demographic and clinical characteristics. In a traditional approach to generating propensity scores, a number of relevant confounders or covariates included in a logistic model are defined based on available data, and they are primarily guided by knowledge related to exposure and the study population characteristics. In a typical claims database, important attributes are unavailable (eg, laboratory results, functional status, smoking status, over-the-counter medication); therefore, empirically identifying appropriate proxies for patient health status out of a large number of variables in claims data is a significant challenge. High-dimensional propensity score (HDPS) analysis is an automated algorithm developed by Schneeweiss and colleagues (2009) to set up proxies by assessing diagnosis codes, procedure codes, and prescribed medication codes,17 which helps to overcome the aforementioned challenges in the process of reducing selection bias and controlling for confounding effects.

HDPS analysis was employed in this study with the diagnosis code and the procedure codes in outpatient, ED, and inpatient settings specified as data dimensions. In each data dimension, the 200 most prevalent codes were used, and then the possible amount of confounding was calculated for each variable based on a multiplicative model to sort all variables in a descending order. The top 300 variables were selected to construct a logistic model and generate the propensity score.17,18 Lastly, PSM was conducted at 1:1 between the exposure cohort and the control cohort using the Greedy 5-to-1 digit technique.19

Statistical Analysis

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