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The American Journal of Managed Care December 2014
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Medicare Star Excludes Diabetes Patients With Poor CVD Risk Factor Control
Julie Schmittdiel, PhD; Marsha Raebel, PharmD; Wendy Dyer, MS; John Steiner, MD, MPH; Glenn Goodrich, MS; Andy Karter, PhD; and Gregory Nichols, PhD
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Medicare Star Excludes Diabetes Patients With Poor CVD Risk Factor Control

Julie Schmittdiel, PhD; Marsha Raebel, PharmD; Wendy Dyer, MS; John Steiner, MD, MPH; Glenn Goodrich, MS; Andy Karter, PhD; and Gregory Nichols, PhD
The Medicare STAR medication adherence measures exclude diabetes patients at high risk for poor cardiovascular outcomes, and underestimate the prevalence of medication nonadherence in diabetes.
A total of 9% of patients prescribed a medication in the ACE inhibitor/ARB therapeutic grouping were considered “primary nonadherent” and did not fill their medication at all (2%), or were “early nonpersistent” and filled only once (7%), and therefore were excluded by CMS. Among those patients on a statin, 10% were excluded (2% for having 0 fills of an ordered medication, and 8% for having 1 fill). Twenty-eight percent of patients who were ordered an oral diabetes medication were excluded: 2% due to never filling an ordered medication, 7% for filling only once, and 19% because they filled the oral medication at least twice in 2010, but also had at least 1 fill for insulin. A total of 34,514 (27%) of the patients in our diabetes cohort were excluded from all medication adherence monitoring by the Star measures, based on CMS criteria.

Patients who were adherent to medications based on the Medicare Star metric had higher CVD risk factor control than those who were nonadherent based on the Star metric and those who were excluded from the Star metric, in all 3 therapeutic groupings of CVD risk factor medications (Table 3). After adjustment, nonadherence based on the Star metric was associated with suboptimal CVD risk factor control: A1C (risk ratio [RR], 0.95; 95% CI, 0.94-0.96), LDL-C (RR, 0.84; 95% CI, 0.83-0.85), and SBP control (RR, 0.96; 95% CI, 0.95-0.96), respectively (Figure 2). Exclusion from the Star metric based on early nonadherence was also negatively associated with risk factor control: A1C (RR, 0.83; 95% CI, 0.82-0.85), LDL-C (RR, 0.56; 95% CI, 0.55-0.58), and SBP control (RR, 0.87; 95% CI, 0.86-0.89), respectively. Exclusion from the Star metric for concurrent insulin use was negatively associated with A1C control among patients ordered or filling oral diabetes medications: RR, 0.78; 95% CI, 0.77-0.79.

DISCUSSION

This study is the first to examine levels of medication adherence among Medicare beneficiaries with diabetes based on the new CMS Medicare Star adherence metrics, and to assess the proportion of patients excluded from these metrics. In a cohort of 129,040 patients 65 years or older, we found that 59% to 71% of patients who had evidence their physician had put them on a medication were considered adherent by the CMS metric, depending on therapeutic grouping. However, between 9% and 28% of these patients were excluded from the Star adherence measures by CMS.

The CMS specifications excluded 27% of our overall diabetes cohort of patients 65 years and older from being covered by any of these measures. Since current government estimates find that up to 11 million Americans 65 years and older have diabetes,28-29 our study suggests that up to 2.93 million Medicare-age patients with diabetes may be excluded from these measures nationally. It is unclear why CMS chose these particular exclusion criteria: for example, while measuring insulin adherence might require different data and methods, the oral diabetes medication adherence for those patients concurrently taking insulin can be measured using the current PDC-based methodology. Further discussion with CMS as to the rationale for excluding these patients, and finding a path toward monitoring the quality of CVD risk factor management in these excluded patients has the potential to improve care for millions of diabetes patients.

Adherence to medications is a process measure for assessing the quality of healthcare, since it does not measure clinical outcomes directly.30 The most meaningful process measures of healthcare quality should be tightly linked to clinical outcomes.31 In this case, attempts to measure the process of taking CVD risk factor medications appropriately should be strongly correlated with CVD risk factor control. Our study shows that the Medicare Star adherence metrics achieve this linkage with CVD risk factor control among patients covered by the measure. However, our study also demonstrates that exclusion from the metric based on early nonadherence is also strongly associated with poor CVD risk factor control. These findings suggest that the underuse of cardiometabolic medications is a significant barrier to CVD risk factor control in diabetes patients,32 and suggest that the current Star measures underestimate medication nonadherence in the diabetes population.

Currently, the difference between a 5-star rating for adherence to oral antidiabetes medications and a 3-star rating for Medicare Advantage plans is less than 5% (≥79% adherent vs <75.7% adherent).6 While changes to the way CMS measures adherence that would account for early nonadherence could temporarily lower health plan ratings on these measures, considering the importance of high Star ratings to health plans33 these changes would likely encourage plans to address early non-adherence among their enrollees. Quality measures focused on adherence should also account for underuse of medications due to not starting or not refilling a prescribed medication.

The new Star adherence metrics place an important and innovative emphasis on holding health plans accountable for appropriate medication adherence. As a response to this mandate, CMS and health plans caring for Medicare enrollees should focus on implementing and disseminating system-level interventions to help patients successfully start medications, as well as encourage ongoing medication persistence in those who have achieved 2 fills or more.2,8,17,34 Research suggests that effective interventions to improve medication starts and ongoing adherence are available. For example, one recent study showed that automated outreach to early nonadherent patients can successfully improve statin starts and refill rates.35 These types of interventions, when cost-effective, have great potential as healthcare systems move toward greater integration and meaningful use of electronic health information.36-38

Limitations

This analysis has a few limitations worth noting: all patients in this study had diabetes. The Medicare Star adherence measures are also be applied to patients with hypertension or hyperlipidemia who do not have diabetes. The level of adherence based on Medicare Star measure specifications in these large, integrated delivery systems was generally high, and these systems also achieve consistently high scores on other Medicare Star metrics39; the level of Medicare Star adherence and early nonadherence to medications may be different in other healthcare settings. In addition, not all health plans currently engaged in reporting to CMS have access to EHR prescription data. However, since KP system characteristics, such as integration and meaningful use of electronic healthcare data, are put forth as models of care by the ACA and other recent legislation,36-38 and health plans will be moving to EHRs based on these requirements, these findings provide a significant benchmark for medication adherence standards moving forward. In addition, we do not have data on why patients mayhave discontinued medications after only 1 fill, and were therefore excluded from the Medicare Star metric.

We were unable to measure medication adherence for a substantial proportion of patients with diabetes because they had no evidence they were placed on a CVD risk factor control medication by their physician (ie, no prescription orders or fills in 2010). As shown in Table 1, 19.6% of diabetes patients had no evidence they were prescribed statins, 26.1% had no evidence they were prescribed ACE inhibitors/ARBs, and 39% showed no evidence they were prescribed oral diabetes medications. Medication adherence metrics would not be appropriate for monitoring quality of care in these patients; however, whether risk factors in these patients were being managed through lifestyle interventions alone, or whether due to age or other comorbidities these medicines were not indicated for CVD risk factor control, is unknown. Future research should focus on developing quality metrics that monitor quality for a wide range of CVD risk factor control efforts in Medicare-aged patients with diabetes that take the needs of older patients with multiple comorbidities into account.

CONCLUSIONS

While higher Star-defined adherence is associated with CVD risk factor control, this new measure excludes a significant number of diabetes patients prescribed cardiometabolic medications that are at high risk for poor CVD outcomes. Healthcare policies that encourage system-level efforts to address the underuse of medications in diabetes patients should focus on decreasing CVD risk for the entire population of Medicare patients, including those presently excluded from the new Star adherence metrics.

Acknowledgments

The authors would like to gratefully acknowledge the contributions of Michael Chase, MD, associate medical director for quality at Kaiser Permanente Colorado; Alan Whippy, MD, medical director of quality and safety, The Permanente Medical Group, Oakland, CA; and Elizabeth A. McGlynn, MD, director of the Kaiser Permanente Center for Effectiveness and Safety Research, Oakland, CA.

Author Affiliations: Division of Research, Kaiser Permanente Northern California (JAS, WD, AJK), Oakland, CA; Institute for Health Research, Kaiser Permanente Colorado (MAR, JFS, GKG), Colorado, Denver; and Center for Health Research, Kaiser Permanente Northwest (GAN), Portland, OR.

Source of Funding: This study was funded by the Kaiser Permanente Center for Effectiveness and Safety Research, Contract no. KR021125. This project was supported by grant number R01HS019859 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. This activity was also supported by the Health Delivery Systems Center for Diabetes Translational Research (NIDDK grant 1P30-DK092924).

Author Disclosures: Dr Nichols has received diabetes research funding from AstraZeneca, Boehringer Ingelheim, Merck, and Novartis. Drs Raebel, Schmittdiel, Steiner, and Karter; Ms Dyer; and Mr Goodrich report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (JAS, MAR, JFS, AJK, GAN); acquisition of data (JAS, WD, MAR, JFS, GKG, GAN); analysis and interpretation of data (JAS, WD, MAR, JFS, GAN); drafting of the manuscript (JAS, AJK); critical revision of the manuscript for important intellectual content (MAR, JFS, AJK, GAN); statistical analysis (JAS, WD); provision of study materials or patients (JFS); obtaining funding (JAS, JFS, AJK); administrative, technical, or logistic support (GAN); and supervision (JAS).

Address correspondence to: Julie A. Schmittdiel, PhD, Division of Research, Kaiser Permanente, 2000 Broadway, Oakland, CA 94612. E-mail: Julie.A.Schmittdiel@kp.org.
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