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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
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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
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.
Before matching, the Student’s t tests were used to detect differences in patient characteristics between the exposure and the control cohorts for continuous variables (eg, age, CCI score), and the χ2 test was used for categorical variables, including demographics (eg, gender, ethnicity) and comorbidities (eg, diabetes, hypertensive disease). After matching, McNemar’s tests were used for categorical variables and the paired t test for continuous variables.

Multivariable Analysis

After matching, a conditional logistic regression model was constructed with adherence (1 if PDC ≥80%, 0 if PDC <80%) as the dependent variable. More than 50% of the beneficiaries had repeated observations for 2 or more years from 2007 to 2010. Therefore, a generalized estimating equation technique was applied in the multivariable models to correct for the correlation between repeated observations of a patient.20,21 All analyses were performed using SAS version 9.2 (SAS Institute Inc, Cary, North Carolina). P values less than .05 were considered to be statistically significant.

RESULTS

Sample Size


Application of the patient selection criteria resulted in 5366, 5650, 5991, and 6268 unique beneficiaries diagnosed with COPD and treated with LABDs for the years 2007 to 2010, respectively (Table 1). Figure 2 depicts the patient selection flow and the sample size at different steps. Each year, nearly 20% of those beneficiaries enrolled with Part D benefit were not subject to the coverage gap (ie, assigned into the control cohort), and the remaining beneficiaries were at risk of the coverage gap (ie, assigned into the overall exposure cohort).

From 2007 to 2010, respectively, the final control cohort contained 1011, 1012, 1145, and 1176 beneficiaries, and the final exposure cohort (the mid-gap + the late-gap subgroups) contained 2786, 2746, 2721, and 2751 beneficiaries. Combined across all years, there were 4344 patient-year observations in the control cohort and 11,004 patient-year observations in the exposure cohort before implementation of PSM. After the 1:1 matching, both cohorts included 4147 patient-year observations, which was the final sample for analysis.

Descriptive Analysis

Overall, the mean age of the patients was 77.4 years (standard deviation [SD] = 7.6), the majority (71%) was female, and more than 90% were Caucasians (Table 2). Beneficiaries were heavily concentrated in the South, while beneficiaries in the West were underrepresented in the study. The mean CCI score was approximately 2.2. The most common comorbidity in the baseline period was hypertension (>65%), followed by heart disease (>50%) and hyperlipidemia (>45%). More than 70% of the patients had used a LABD, and a large proportion of them received oxygen therapy or oral corticosteroids (about 30%) in the baseline period. Beneficiaries had substantial medication burden in the baseline period, with an average of more than 10 different classes of medications.

Prior to matching, the control and the exposure cohorts were significantly different in almost all of the demographic and baseline characteristics, except for several baseline comorbidities. After matching, the cohorts were generally balanced in demographic and baseline characteristics, with statistical differences observed only for the prevalence of several baseline comorbidities. The standardized differences were generally small, with most of the absolute values less than 10% (except several baseline comorbidity variables), indicative of acceptable balance between the matched cohorts.22

After cohort matching, the mean annual PDC in the matched control cohort was 0.70 (SD = 0.25); it was 0.69 (SD = 0.24) in the matched exposure cohort. About 46% of the matched control cohort was adherent versus 42% for the matched exposure cohort.

Multivariable Analysis

Unadjusted results showed the matched exposure cohort had lower adherence rates than the matched control cohort. After adjusting for age, gender, and the unbalanced covariates, beneficiaries who reached the coverage gap had lower odds of adherence compared with beneficiaries who were not exposed to the coverage gap. Specifically, beneficiaries in the late-gap subgroup had nearly 40% lower odds of adherence than beneficiaries in the control cohort (OR, 0.603; 95% CI, 0.493-0.738). Beneficiaries in the mid-gap subgroup also had lower odds of adherence, although this relationship was not statistically significant (OR, 0.931; 95% CI, 0.846-1.024). In addition, hyperlipidemia, depression, and diseases of the musculoskeletal system and connective tissues were found to be associated with lower likelihood of adherence (P <.05) (Table 3).

DISCUSSION

The study results suggest that reaching the Part D coverage gap may be negatively associated with medication adherence among Medicare patients with COPD, and the association was stronger among the beneficiaries who reach the coverage gap later (ie, on or after November 1). One explanation for these findings may be that when Medicare beneficiaries enter the coverage gap, they bear a higher economic burden to obtain their medications and they may be more likely to choose nonadherence to more expensive brand name drugs. In addition, we assessed patients’ use of short-acting bronchodilators (SABDs) before and after they hit the coverage gap and did not observe a remarkable shift (results not reported here). One possible reason is that SABDs are a class of medications that are usually used to relieve acute symptoms, not a substitute for long-term maintenance therapy.

The results are consistent with previous studies finding that the Part D coverage gap is associated with reduced medication adherence.23-26 For example, Fung et al (2010) found that the odds of adherence among patients with diabetes with the Part D coverage gap decreased by 17% compared with those patients without the Part D coverage gap.23 Likewise, Stuart and colleagues (2013) found that the PDC was 7.8% lower for statins, 7.0% lower for clopidogrel, or 5.9% lower for beta-blockers for beneficiaries exposed to the coverage gap compared with those not exposed.

To date, this is the first study to evaluate the impact of the Part D coverage gap on medication adherence for beneficiaries with COPD using the longitudinal national Medicare claims data, and it is 1 of few studies to explore the impact of hitting the coverage gap at different times of the year.8,27,28 In contrast, most research has assessed the impact of the coverage gap from the perspective of in versus out of the coverage gap. Our study suggests that such temporal distinctions may have important implications for patient behaviors, such as adherence to prescription drugs.

Another strength of this study is that the high-dimensional PSM was adopted to mitigate potential selection biases and to adjust for the observed confounding effect between the exposure and the control cohorts, which extends beyond traditional PSM by maximizing the utilization of the information provided by claims data. Compared with traditional PSM methods that use “typical” covariates only (eg, age, gender, comorbidities), matching 2 cohorts in this way may provide estimates closer to those of randomized trials.17

This study has several limitations. First, the medical and pharmacy claims data used in this analysis were primarily used for administrative purposes to obtain reimbursement; therefore, there is potential for coding errors that may cause diagnostic and procedural misclassification. In addition, adherence was measured based on the assumption that patients took the drugs after they filled the prescriptions when using pharmacy claims data.

Second, the study is subject to the limitations of retrospective observational studies, and the findings can only be interpreted as association and no causality can be concluded. Although multiple strategies were applied to minimize selection bias and confounding effects, they were unable to control for unobserved factors (eg, health literacy level) or beneficiary behaviors (eg, self-selection of a high-premium plan to avoid or reduce the burden produced by the coverage gap).

Third, COPD patients in the exposure cohort who did not reach the coverage gap or reached the gap prior to March 1 were excluded from the analysis, assuming the 2 groups represent the 2 extremes of the spectrum of health status. No similar exclusion was done for the control cohort not exposed to the coverage gap. However, we believe that the use of HDPS analysis provides some assurance of the comparability of the 2 cohorts. Nevertheless, future research that takes into consideration the beneficiaries with very low drug spending who were subjected to the coverage gap may help to further balance the comparative cohorts.

Lastly, the study cohort was composed of beneficiaries from the Medicare fee-for-service program, so the results might not be generalizable to the Medicare population enrolled with Medicare Advantage plans. Similarly, the study period ended in 2010 due to data availability, and it is not clear if the effect of the coverage gap identified in this study remained after 2010. Studies of the Medicare managed care population or studies using more recent data could provide additional insights into these issues.

Medicare benefit cycles restart at the beginning of each calendar year. Beneficiaries are aware of the coverage gap at that time and have an anticipation of the likelihood of hitting the gap during the year. Although being adherent is likely to lead beneficiaries to enter the coverage gap or enter the gap earlier, the effect of the coverage gap is larger from a perspective of behavior adjustment, based on information and projection.

The coverage gap is planned to close out by 2020. At that point, beneficiaries will pay 25% of the total cost for covered brand name and generic drugs during the gap. Although the cost of closing the coverage gap may present a serious challenge to policy makers in the current fiscal climate, it is expected that the coverage gap closure will benefit beneficiaries. Prior to the close-out of the Part D coverage gap, healthcare administrators and health plans should make efforts to help beneficiaries transition through the gap smoothly and minimize the risk of experiencing high out-of-pocket costs and preventable adverse outcomes from medication nonadherence. Health plans can take more proactive approaches to raising awareness of the coverage gap among beneficiaries and physicians, and providing beneficiaries with personalized information on cost-saving options that may help delay their entry into the gap. Health plans and policy makers may also want to consider educating beneficiaries, in collaboration with healthcare providers, about the importance of adherence to help patients remain compliant with their medication regimens.

 

CONCLUSIONS


 
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