Effects of Medicare Part D Coverage Gap on Medication Adherence | Page 2

Entering the Medicare Part D coverage gap does not result in a large reduction in medication adherence for essential drugs.
Published Online: June 11, 2013
Yuting Zhang, PhD; Seo Hyon Baik, PhD; and Judith R. Lave, PhD
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.

In addition, we controlled for duration of time spent in the coverage gap (Figure) in the model because the longer beneficiaries stayed in the gap, the more likely it was that they would change their medication use and medical spending. All analyses were conducted using SAS software, version 9.2 (SAS Institute Inc, Cary, North Carolina) and R: A Language and Environment for Statistical Computing, version 2.12 (http://www.r-project.org).

RESULTS

Table 1 compares the characteristics between each study group and comparison group for beneficiaries whose pharmacy spending exceeded the coverage gap threshold but did not exceed the catastrophic coverage threshold. All the numbers are after adjustment with propensity score weights. After adjustment, all characteristics were comparable (ie, there was no statistically significant difference at P >.05) between each study group and the comparison group.  

Effects of the Coverage Gap on Medication Use and Spending Among the General Population

Table 2 presents the effects of the coverage gap on the probability of using a drug, the mean number of monthly prescriptions filled, and the mean monthly pharmacy spending for all medications for the overall population. There are 3 main findings:

First, relative to the comparison group, there were statistically significant reductions in all of the outcomes (probability of using a drug, mean number of monthly prescriptions filled, and monthly pharmacy spending) in both study groups. However, those with no coverage generally decreased their use of medications more than those with generic drug coverage in the gap.

Second, the overall decrease in monthly medications and spending on drugs was primarily due to the decrease in the use of brand-name drugs. For example, those without drug coverage in the gap reduced their overall medication use by 0.85 medication per month (95% confidence interval [CI], 0.82-0.88); 75% of the reduction was accounted for by brandname drugs and 25% by generic drugs. This group decreased its monthly pharmacy spending by $73.15 (95% CI, $71.66- $74.65), of which $66.65 (95% CI, $65.30-$68.01) was for brand-name drugs and $6.40 (95% CI, $5.88-$6.92) was for generic drugs.  

Third, those with only generic coverage in the gap reduced their use of brand-name drugs but did not increase their use of generic drugs. In fact, they decreased their use of generic drugs slightly but negligibly. Among the general population, relative to the comparison group, the generic-only group reduced the number of monthly prescriptions filled by 0.66 (95% CI, 0.63-0.70); almost all of this decrease was attributable to the reduction in brand-name drugs (0.61 [95% CI, 0.59-0.62]).  

Effects of the Coverage Gap Among Patients With Chronic Conditions

Beneficiaries with heart failure and/or diabetes decreased their overall use of medications, and the overall decrease was similar to that found for the general population (Table 3). They also decreased their use of the drugs specific to their conditions. The relative decreases in the condition-specific drugs were similar to those observed for the drugs overall.  

The decrease in medication use was accompanied by a decrease in medication adherence in both groups, but the decrease in adherence was smaller than the reduction in the number of prescriptions filled (Table 4). In addition, relative to the comparison group, the decrease in medication adherence was always larger in the no-coverage group than in the generic-only group. In the heart failure sample, the MPR for heart failure drugs among the no-coverage group dropped from 0.87 in the pregap period to 0.83 in the within-gap period, while the MPR for those with generic-only coverage dropped from 0.87 to 0.86. Although statistically significant, the relative reduction in adherence for heart failure was negligible. The same pattern was observed for those with diabetes, although decrease in medication adherence was larger in this group.  

DISCUSSION

Medicare beneficiaries decreased their medication use once they entered the gap—and the decrease in use was consistently higher for those who had no coverage in the gap than for those with generic drug coverage in the gap. We hypothesized that beneficiaries would reduce their use of brand-name drugs after entering the coverage gap and if they had generic drug coverage, they would shift their brand-name drugs to generic drugs. We did observe that beneficiaries reduced their use of brand-name drugs substantially more than their use of generic drugs, but those with generic drug coverage did not switch from brand-name to generic drugs. This is partially because those in the generic-only group tended to use more generic drugs in the initial coverage gap period.  

We adjusted the time spent in the gap in our model so the beneficiaries spending less time in the gap contributed less to the regression results. In addition, we conducted sensitivity analysis by excluding those beneficiaries who stayed in the gap for less than 1 month or less than 2 months. The results were robust.  

The overall coverage gap effects in the no-coverage group on medication use were similar to those found in an earlier study examining beneficiaries enrolled in Medicare Advantage prescription drug plans.7 However, the effects for those with generic drug coverage were different: in the earlier study beneficiaries with generic drug coverage actually increased their use of generic drugs in the gap, whereas in this population no such increase was observed.7 This difference may be due to different practices between traditional fee-for-service Medicare and Medicare Advantage plans. For example, Medicare Advantage prescription drug plans might have a better medication management program or chronic disease management program.  

A major strength of this study is that we had a random sample of aged Medicare beneficiaries enrolled in PDPs. We examined the effect of the coverage gap on a national overall sample of Medicare beneficiaries as well as those with 2 prevalent chronic conditions: heart failure and/or diabetes. We included all qualified Medicare beneficiaries, including those in nursing homes. (We estimated the model excluding nursing home residents, and the results were similar.)  

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