Currently Viewing:
The American Journal of Managed Care June 2013
Population Health Approach for Diabetic Patients With Poor A1C Control
Ted Courtemanche, MHA; Guy Mansueto, MBA; Richard Hodach, MD, MPH, PhD; and Karen Handmaker, MPP
Cervical Cancer Screening Overuse and Underuse: Patient and Physician Factors
Cristina M. Almeida, MD, MPH; Michael A. Rodriguez, MD, MPH ; Samuel Skootsky, MD; Janet Pregler, MD; Neil Steers, PhD; and Neil S. Wenger, MD, MPH
Identifying Groups of Nonparticipants in Type 2 Diabetes Mellitus Education
Ingmar Schäfer, Dipl-Soz; Claudia Küver, MD; Birgitt Wiese, Dipl-Math; Marc Pawels, Dipl-Psych; Hendrik van den Bussche, Prof; and Hanna Kaduszkiewicz, MD
Shifting the Dialogue From Hospital Readmissions to Unplanned Care
Rosa R. Baier, MPH; Rebekah L. Gardner, MD; Eric A. Coleman, MD, MPH; Steven F. Jencks, MD, MPH; Vincent Mor, PhD; and Stefan Gravenstein, MD, MPH
Estimating the Staffing Infrastructure for a Patient-Centered Medical Home
Mitesh S. Patel, MD, MBA; Martin J. Arron, MD, MBA; Thomas A. Sinsky, MD; Eric H. Green, MD; David W. Baker, MD; Judith L. Bowen, MD; and Susan Day, MD, MPH
Accountable Care Organizations: How to Dress for Success
Arthur P. Hayen, MSc; Michael J. van den Berg, PhD; Bert R. Meijboom, PhD; and Gert P. Westert, PhD
Consumer Cost Sharing and Use of Biopharmaceuticals for Rheumatoid Arthritis
James C. Robinson, PhD, MPH
Currently Reading
Effects of Medicare Part D Coverage Gap on Medication Adherence
Yuting Zhang, PhD; Seo Hyon Baik, PhD; and Judith R. Lave, PhD
Affordability in a Mandated Environment
Jill M. Yegian, PhD; and Grace Wang, PhD, MPH
Medicare and Commercial Inpatient Resource Use: Impact of Hospital Competition
Rachel Mosher Henke, PhD; Jared Lane Maeda, PhD; William D. Marder, PhD; Barry S. Friedman, PhD; and Herbert S. Wong, PhD

Effects of Medicare Part D Coverage Gap on Medication Adherence

Yuting Zhang, PhD; Seo Hyon Baik, PhD; and Judith R. Lave, PhD
Entering the Medicare Part D coverage gap does not result in a large reduction in medication adherence for essential drugs.
Objectives: To evaluate the effects of the Medicare Part D coverage gap on pharmacy use among a national sample of Medicare beneficiaries and on medication adherence among 2 subsamples with heart failure and/or diabetes.  

Study Design: Pre-post design, with comparison group and propensity score weighting.  

Methods: We used a 5% random sample of elderly Medicare beneficiaries enrolled in stand-alone Part D plans in 2007. The comparison group had full coverage in the gap, whereas the 2 study groups had either no coverage or generic-only coverage in the gap. Main outcomes included probability of filling a prescription, monthly pharmacy spending and number of prescriptions filled, and adherence measured by medication possession ratios.

Results: Relative to the comparison group, beneficiaries without drug coverage in the gap reduced the number of prescriptions filled per month by 16.0% (95% confidence interval [CI], 15.5%-16.5%); those with generic drug coverage in the gap reduced it by 10.8% (95% CI, 10.3%-11.4%). Most of the reduction was attributable to reduced use of brand-name drugs. Beneficiaries with heart failure reduced adherence to heart failure drugs by 3.6% (95% CI, 2.9%-4.2%) and beneficiaries with diabetes reduced antidiabetic medication adherence by 10.3% (95% CI, 9.4%-11.3%).  

Conclusions: Medicare beneficiaries reduced medication use (mainly brand-name drugs) after entering the coverage gap. This result suggests that while beneficiaries’ financial burden would continue because of the coverage gap, the gap would not result in a large reduction in medication adherence for essential drugs for diabetes and heart failure.  

Am J Manag Care. 2013;19(6):e214-e224
We examined the effect of the coverage gap on a national sample of Medicare beneficiaries as well as those with heart failure and/or diabetes. Although beneficiaries’ financial burden in the coverage gap will continue until 2020, the gap will not result in a large reduction in medication adherence for essential drugs.
 
  • Medicare beneficiaries reduced medication use (mainly brand-name drugs) after entering the coverage gap.

  • Beneficiaries with generic drug coverage in the gap reduced their drug use less.

  • However, medication adherence was reduced to a much smaller degree for essential drugs used to treat heart failure and diabetes.
Medicare Part D offers prescription drug coverage for Medicare beneficiaries. Since the program’s inception in 2006, many beneficiaries have obtained improved drug coverage and increased their use of medications.1-3 The standard Part D benefit in 2007 included an initial $265 deductible, a period in which beneficiaries paid 25% of drug costs between $265 and $2400, a coverage gap in which they paid 100% of costs until their total out-of-pocket spending reached a catastrophic limit of $3850, and a catastrophic coverage period in which they paid 5%.4

A major concern is the large coverage gap in the standard Part D design. About one-third of all Medicare beneficiaries enter this coverage gap each year.5 Faced with a sudden increase in medication costs, beneficiaries might cut back on their medication use and reduce medication adherence for the essential drugs.6-8 That, in turn, might put their health at risk, leading to subsequent increases in hospitalizations and medical spending.9 Because of the high cost of Medicare and the potential implications for health outcomes, it is important to determine whether the coverage gap leads to reduction in medication adherence for essential drugs.

A few studies have examined the effects of the coverage gap on medication use, but the majority of these studies used pharmacy data either from 1 local Medicare-Advantage Part D plan7,10,11 or for 1 specific condition—diabetes.6,10,12-14 One recent study used Medicare Part D data to examine the effects of the coverage gap among 2 subsamples with hypertension and hyperlipidemia.15 They found that the coverage gap wasassociated with reduction in use of and adherence to medications for hypertension and hyperlipidemia. Another recent study looked at aged Medicare beneficiaries with cardiovascular disease enrolled in a standalone Part D plan (PDP) or a retiree drug plan that was administered by CVS Caremark.16 They found that beneficiaries with no financial assistance during the coverage gap were more likely to discontinue their cardiovascular drugs.

In this study, we evaluated the effects of the coverage gap on medication spending and use among aged Medicare beneficiaries (those 65 years or older) in the national Part D data. In addition, we selected 2 subsamples of those with heart failure and/or diabetes to evaluate the gap effect on medication spending and adherence. We selected these 2 conditions because they are common in Medicare beneficiaries and because the medication treatment is very important— premature discontinuation may lead to increases in subsequent spending on medical care.17,18 We determined how beneficiaries with these chronic conditions responded to the coverage gap compared with the general Medicare population.

Our analyses were guided by a number of hypotheses: (1) Medicare beneficiaries will decrease all their medication use regardless of drug classes once they enter the gap— and that the decrease will be higher for those with no drug coverage in the gap than for those with generic coverage in the gap.19,20

However, those who anticipate going through the gap and entering the catastrophic period will not reduce their medication use in the gap.7 (2) Beneficiaries will decrease their use of all brand-name drugs more than generic drugs, and if beneficiaries have generic drug coverage they will shift from brand-name to generic drugs.19 (3) Among those with heart failure and/or diabetes, adherence to cardiovascular and diabetic drugs will decrease in the coverage gap.10

METHODS

Data and Study Population

We obtained beneficiary demographic and enrollment information, plan benefits, prescription drug events, and medical claims for a 5% random sample of all aged Medicare beneficiaries who were continuously enrolled in PDPs in 2007. We identified 2 subpopulations of beneficiaries: those with heart failure and/or diabetes. Each subpopulation was identified by 2 criteria: (1) having a diagnosis for the condition before January 1, 2007, defined by 2006 Centers for Medicare & Medicaid Services Chronic Condition Warehouse indicators (Appendix A); and (2) use of at least 1 medication for the condition in the initial coverage period so we could ensure that beneficiaries were on medications of interest before entering the coverage gap (Appendix B  provides the list of medications). The study design was approved by the institutional review board at the University of Pittsburgh.

Study Design 

Although the standard Part D benefit includes the 4 phases described in the first paragraph of this study, some companies offering PDPs modified the design and offered either “actuarially equivalent” or enhanced plans. In 2007, 72% of stand-alone PDPs had the standard coverage gap, 27% of PDPs had some generic coverage in the gap, and fewer than 1% offered coverage for both brand-name and generic drugs.21 In addition, beneficiaries with low incomes who are eligible for subsidies had a lower copayment throughout the year and did not face a coverage gap.4

Using this variation in drug coverage, we identified 2 study groups based on the type of coverage they had in the gap (no coverage, generic only) and 1 comparison group. The comparison group consisted of beneficiaries who had either 12-month Medicaid coverage or 12-month low-income subsidies (LIS group). Thus, the drug coverage for the comparison group did not change throughout the year; that is, they did not face the gap in coverage. Even though the LIS group was different from the 2 study groups in socioeconomic characteristics, they could still serve the purpose of controlling for the underlying time trends in medication use, because their drug coverage did not change while the study groups had a sudden increase in drug costs once entering the coverage gap.

We used a pre-post study design with a comparison group to assess the impact of the coverage gap on medication and medical care use. This approach uses a difference-in-difference estimate, comparing the pregap and within-gap change for each study group with the pregap and within-gap change in the comparison group. This design does not require that study and comparison groups have the same baseline characteristics. As long as different groups have similar baseline trends, we could get unbiased results.22 We tested the baseline pregap trends in overall medication use across 3 groups, and they were similar.

We hypothesized that beneficiaries who went through the coverage gap and entered the catastrophic coverage period would not reduce their medication use in the gap.7 We ran regressions to test this hypothesis and found that those entering the catastrophic period did not change their medication use in the gap (Appendix C). Thus, for this study we primarily focused on beneficiaries who entered the gap but did not go through it. That is, each individual in our study sample had a pregap and a within-gap period.

To define pregap and within-gap periods, we first identified the index date as the first day that the beneficiary’s total drug spending reached the coverage gap threshold. The pregap period was defined as January 1, 2007, to the index date. The within-gap period was defined as the time from the first day after the index date until December 31, 2007. The index date was included in the pregap period because almost all prescriptions filled by beneficiaries on that day were subject to copayment levels in the initial coverage period. We compared the change in each outcome in the pregap and withingap periods between each study group and the comparison group: no coverage versus LIS and generic only versus LIS. We used a propensity score weighting mechanism to balance each study group with the comparison group. The propensity score weighting was conducted separately for each group: the general population and each subgroup of those with heart failure and/or diabetes.

Outcomes of Interest

For the pregap and within-gap periods separately, we defined 4 outcomes to measure medication use: (1) probability of using a medication (1 = used a medication; 0 = did not use a medication in the study period); (2) the mean number of monthly prescriptions filled per month, defined as the total number of prescriptions standardized by 30-day supply (ie, a prescription with a 90-day supply would count as 3 monthly drugs) divided by the number of months in the study period; (3) mean monthly pharmacy spending per month; and (4) medication adherence measured by medication possession ratio (MPR). For the general population, we focused on the first 3 outcomes. We measured these outcomes for all medications and then for brand-name and generic drugs separately.

For the patients with chronic conditions, we focused on the mean number of monthly prescriptions filled and the MPR. We examined the mean number of monthly prescriptions filled overall as well as the number of monthly prescriptions for disease-specific drugs. We defined the MPR as the proportion of days during a given period (eg, either the pregap or the within-gap period) that a subject had possession of any drugs used to treat the chronic illness. The prescriptions filled on the day the patient entered the coverage gap were included in calculating the within-gap MPRs for 2 reasons: (1) because these drugs were used in the within-gap period and (2) because inclusion of these drugs was consistent with the pregap MPR definition where drugs filled on the first day (January 1, 2007) were included in the calculation. Similarly, to be consistent with the MPR calculation in the pregap period where prescriptions filled before January 1, 2007, were not included, prescriptions filled before entering the gap were not included in the calculation for the withingap adherence. However, the definitions for MPRs should not change the difference-in-difference results because we applied the same rules for the study and comparison groups.

Statistical Analysis 

To implement the propensity score weighting mechanisms, we conducted a 2-stage analysis. In the first stage, we ran 2 logistic regression models to predict the probability of being in a study group relative to the comparison group, controlling for age, sex, race, number of Elixhauser comorbidities,23 and Prescription Drug Hierarchical Condition Category, the beneficiary risk adjuster used by Centers for Medicare & Medicaid Services to adjust payment to plans for pharmacy costs.24

In the second stage, we ran a difference-in-difference model with the inverse of the propensity score as a weight. This effectively assigned a higher weight to individuals in the comparison group who had characteristics similar to those of individuals in the study group. In this model, the dependentvariable was the difference between within-gap and pregap periods for each previously defined outcome. Because pregap and within-gap measures were likely to be correlated, the advantage of this approach versus using 2 interrelated outcomes is that we could simply eliminate the correlated structure in 2 outcomes. The key independent variable was the indicator for being in the study group relative to the comparison group. All the covariates used in calculating propensity scores were included in the model.

 
Copyright AJMC 2006-2017 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
x
Welcome the the new and improved AJMC.com, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up
×

Sign In

Not a member? Sign up now!