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Medication Adherence and Measures of Health Plan Quality
Seth A. Seabury, PhD; Darius N. Lakdawalla, PhD; J. Samantha Dougherty, PhD; Jeff Sullivan, MS; and Dana P. Goldman, PhD
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Medication Adherence and Measures of Health Plan Quality

Seth A. Seabury, PhD; Darius N. Lakdawalla, PhD; J. Samantha Dougherty, PhD; Jeff Sullivan, MS; and Dana P. Goldman, PhD
This study examines the association between plan-level measures of health outcomes and medication adherence to assess the viability of adherence as a measure of plan performance.
In addition to directly assessing the relationship between quality measures for adherence and health outcomes, we also explored the implications for healthcare spending on patients with diabetes and CHF. Accordingly, we compared adjusted average annual non-drug medical expenditures (defined as the sum of inpatient and outpatient expenditures) by diabetes and CHF patients according to plan adherence (Figure). Results demonstrated that better adherence is associated with substantially lower average spending per year among both the diabetes and CHF samples. For example, average annual expenditures for CHF patients in plans that had low average adherence were approximately $31,500, compared with $20,407 for patients in high adherence plans (P <.001). Similarly, annual expenditures for patients with diabetes in low adherence plans were $8784, compared with $6766 in high adherence plans (P <.001).

We used the relative differences in predicted expenditure values to simulate potential cost savings associated with improving performance among plans with low adherence. Our estimates suggest that moving all low adherence plans to the moderate category would, on average, reduce aggregate spending among patients with diabetes by 1.6% and among patients with CHF by 6.3%. Similarly, moving all the low and moderate adherence plans to high adherence would reduce spending among diabetes patients by 14.1% and among CHF patients by 13.7%. Note that because there is overlap between the diabetes and CHF samples, some of the improvement in diabetes patients could be due to improved treatment of CHF and vice versa. Thus, these differences in spending are not additive in terms of the implications for total spending at the plan level.

DISCUSSION

We used a large database of private sector health claims to estimate the association between quality metrics of medication adherence and health outcomes at the health plan level for patients with diabetes and CHF. Our results demonstrated that there was significant systematic variation in medication adherence across plans. Moreover, we found a consistently positive correlation between high plan-level adherence and good health outcomes. Patients with CHF or with diabetes in health plans with low and moderate levels of adherence are significantly more likely to experience disease-related complications than patients in plans with high adherence. These findings suggest that medication adherence can provide a useful marker for good health plan performance.

The positive association we find between adherence and outcomes at the plan level could be due to a constellation of factors, including the positive health benefits of adherence itself, along with other quality-improving strategies that may promote both good adherence and good outcomes. For example, plans that reward and retain physicians that promote good medication adherence might end up exhibiting both good adherence and good outcomes. Nevertheless, our results support the use of adherence metrics as a potentially important way to separate better-performing plans from their peers.

Moreover, if the estimated associations were causal in nature, our findings would demonstrate the possible value of improving plan-level adherence as a potentially significant source of cost savings. To understand the magnitude of potential savings, consider that excess medical costs associated with diabetes and CHF were estimated to be $116 billion and $25 billion, respectively, in 2007 (the most recent year available).15,28 Applying our estimated association between cost reductions and improved adherence nationwide, plans with low adherence metrics could save $2.1 billion annually on patients with diabetes and $1.9 billion annually on patients with CHF by improving the adherence of their enrollees to a moderate level in 2012 dollars. Similarly, plans could save $19.3 billion annually on patients with diabetes and $4.1 billion annually on patients with CHF if all plans achieved high adherence. These figures are rough estimates, as our findings are based only on the commercial sector while the burden estimates are for the entire population. There also could also be overlap in savings between the 2 disease states, so the total savings are not additive across the 2 populations. Nevertheless, the potential for savings is significant.

Implications

Our findings have several policy implications. For example, providing incentives for plans to improve adherence by tying measures of medication adherence to reimbursement could be an effective lever to improve the overall quality of care while reducing unnecessary expenditures. This could be done by expanding the use of performance measures based on medication adherence in public schemes, such as Medicare Part D or the health insurance exchanges. Under current Medicare policy, Part D plans are required to report on quality measures for use of and adherence to medications used to treat diabetes, hypertension, and high cholesterol, and use them to evaluate Part D plan performance in publicly reported Star ratings. Our findings suggest this could provide significant value for health plans and patients, along with providing support for consideration of a more comprehensive set of measures.

However, this discussion rests on the important assumption that plans have the ability to drive medication adherence. Our study demonstrates that plans differ substantially in terms of the medication adherence of their beneficiaries, and those plans with patients who have better adherence also have better outcomes. But we do not demonstrate why some plans have better adherence than others, or to what extent plans can actively influence enrollee medication-taking behavior. There are many reasons why medication adherence might systematically differ across plans, and which could provide potential levers for plans to improve. For example, formulary design and cost sharing can both influence patient use of medicines and other medical services, as well as adherence.11,13,23-25,29-33 In addition, synchronizing multiple medication refills to take place on a single monthly pickup date, improving care coordination, adopting health information technology, and providing plan incentives are promising interventions, though questions about effectiveness, scalability, and generalizability persist.34 It will be important to provide health plans with a strong evidence base for cost-effective interventions, as simply tying reimbursement to adherence will not necessarily improve outcomes if plans or providers lack the knowledge or ability to change patient behavior.

Limitations

Our study had several limitations. While our data are national and cover a large and diverse set of patients, they do not comprise a nationally representative random sample. This lack of geographic representativeness could compromise the generalizability of our findings; however, these same data have been shown to provide accurate and generalizable measures of patient behavior and spending levels in numerous past studies.16-19 We were also limited in the range of quality measures we could evaluate given data availability, and further work should extend these analyses using quality measures that require additional data elements unavailable in medical claims (eg, laboratory and/or chart records). Our analysis was based on claims data and lacked more direct measures of disease severity, such as glycated hemoglobin levels for diabetic patients or ejection fraction for CHF patients. Also, while we defined good adherence as above 80% PDC to make it consistent with quality measures, further study could evaluate whether the relationship between adherence and outcomes varies across other levels of adherence.

As we have noted throughout, a potentially important limitation of this study is that we do not estimate a causal relationship between plan-level adherence and health outcomes. There is a clear association between adherence and plan outcomes, but understanding whether that is driven causally by the effects of adhering to a medication regimen or by unobserved heterogeneity among patients or plans is crucial for interpreting the study findings. More work is needed to understand the causal mechanisms that drive plan differences in medication adherence and the implications for patient outcomes.

CONCLUSIONS

Despite these limitations, our study provides a promising first step toward demonstrating that adherence is a promising marker for good performance in a health plan. The constellation of activities that high adherence plans undertake to improve quality seems to produce gains in health outcomes for patients. Gaining more insight into these specific activities emerges as a critical question for research that can help inform policy development.

Author Affiliations: University of Southern California (SAS, DNL, DPG), Los Angeles; Pharmaceuticals Research and Manufacturers of America (JSD), Washington, DC; Precision Health Economics (JS), Boston, MA.

Source of Funding: Financial support for this research was provided by Pharmaceutical Research and Manufacturers of America (PhRMA).

Author Disclosures: Mr Sullivan is an employee of Precision Health Economics (PHE), which provides consulting services to life science firms. Dr Lakdawalla is the chief strategy officer and owns equity in PHE, Dr Goldman is a partner at PHE, and Dr Seabury is a consultant for PHE. Dr Dougherty is an employee of Pharmaceutical Research and Manufacturers of America, which sponsored the study.

Authorship Information: Concept and design (DNL, JSD, JS, DPG, SAS); acquisition of data (DPG); analysis and interpretation of data (DNL, JSD, JS, SAS); drafting of the manuscript (DNL, JSD, SAS); critical revision of the manuscript for important intellectual content (DNL, JSD, SAS); statistical analysis (DNL, JS, SAS); obtaining funding (DPG); administrative, technical, or logistic support (DNL, JS); and supervision (DNL, DPG, SAS).

Address correspondence to: Seth A. Seabury, PhD, University of Southern California, 3335 South Figueroa St, Unit A, Los Angeles, CA 90089-7273. E-mail: seabury@usc.edu. 

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