Currently Viewing:
The American Journal of Managed Care June 2015
Impact of the Patient-Centered Medical Home on Veterans' Experience of Care
Ashok Reddy, MD; Anne Canamucio, MS; and Rachel M. Werner, MD, PhD
Higher 30-Day and 60-Day Readmissions Among Patients Who Refuse Post Acute Care Services
Maxim Topaz, PhD, MA, RN; Youjeong Kang, PhD, MPH, RN; Diane E. Holland, PhD, RN; Brenda Ohta, PhD, MSW; Kathy Rickard, MSN, RN; and Kathryn H. Bowles, PhD, RN, FAAN, FACMI
Moving From Healthcare to Health
Bernard J. Tyson
Improving Diabetic Patient Transition to Home Healthcare: Leading Risk Factors for 30-Day Readmission
Hsueh-Fen Chen, PhD; Taiye Popoola, MBBS, MPH; Kavita Radhakrishnan, PhD, RN; Sumihiro Suzuki, PhD; and Sharon Homan, PhD
Quality of Care and Relative Resource Use for Patients With Diabetes
Troy Quast, PhD
How Will Provider-Focused Payment Reform Impact Geographic Variation in Medicare Spending?
David Auerbach, PhD, MS; Ateev Mehrotra, MD, MPH; Peter Hussey, PhD; Peter J. Huckfeldt, PhD; Abby Alpert, PhD; Christopher Lau, PhD; and Victoria Shier, MA
Currently Reading
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
Stimulating Comprehensive Medication Reviews Among Medicare Part D Beneficiaries
William R. Doucette, PhD; Jane F. Pendergast, PhD; Yiran Zhang, MS, BS Pharm; Grant Brown, PhD; Elizabeth A. Chrischilles, PhD; Karen B. Farris, PhD; and Jessica Frank, PharmD
The Role of Nurse Practitioners in Primary Healthcare
John Kralewski, PhD, MHA; Bryan Dowd, PhD, MS; Ann Curoe, MD, MPH; Megan Savage, BS; and Junliang Tong, MS
Provider Behavior and Treatment Intensification in Diabetes Care
Helaine E. Resnick, PhD, MPH; and Michael E. Chernew, PhD

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.


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.


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.


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.


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: 


1. Murphy KM, Topel RH. Estimation and inference in 2-step econometric models. J Business Econ Statistics. 2002;20(1):88-97.
2. Rosenthal MB, Fernandopulle R, Song HR, Landon B. Paying for quality: providers’ incentives for quality improvement. Health Aff (Millwood). 2004;23(2):127-141.
3. Werner RM, Kolstad JT, Stuart EA, Polsky D. The effect of pay-for-performance in hospitals: lessons for quality improvement. Health Aff (Millwood). 2011;30(4):690-698.
4. Viswanathan M, Golin CE, Jones CD, et al. Interventions to improve adherence to self-administered medications for chronic diseases in the United States: a systematic review. Ann Intern Med. 2012;157(11):785-795. Review.
5. Lee JK, Grace KA, Taylor AJ. Effect of a pharmacy care program on medication adherence and persistence, blood pressure, and low-density lipoprotein cholesterol. JAMA. 2006;296(21):2563-2571.
6. Sokol MC, McGuigan KA, Verbrugge RR, Epstein RS. Impact of medication adherence on hospitalization risk and healthcare cost. Med Care. 2005;43(6):521-530.
7. Cutler DM, Everett W. Thinking outside the pillbox—medication adherence as a priority for health care reform. N Engl J Med. 2010;362(17):1553-1555.
8. Stuart B, Loh FE, Roberto P, Miller LM. Increasing Medicare Part D enrollment in medication therapy management could improve health and lower costs. Health Aff (Millwood). 2013;32(7):1212-1220.
9. Offsetting effects of prescription drug use on Medicare’s spending for medical services. Congressional Budget Office website. -11-29-12.pdf. Published November 2012. Accessed June 12, 2013.
10. Roebuck MC, Liberman JN, Gemmill-Toyama M, Brennan TA. Medication adherence leads to lower health care use and costs despite increased drug spending. Health Aff (Millwood). 2011;30(1):91-99.
11. Lee WC, Balu S, Cobden D, Joshi AV, Pashos CL. Medication adherence and the associated health-economic impact among patients with type 2 diabetes mellitus converting to insulin pen therapy: an analysis of third-party managed care claims data. Clin Ther. 2006;28(10):1712-1725; discussion 1710-1711.
12. Ho PM, Bryson CL, Rumsfeld JS. Medication adherence: its importance in cardiovascular outcomes. Circulation. 2009;119(23):3028-3035.
13. Ho PM, Rumsfeld JS, Masoudi FA, et al. Effect of medication nonadherence on hospitalization and mortality among patients with diabetes mellitus. Arch Intern Med. 2006;166(17):1836-1841.
14. Lau DT, Nau DP. Oral antihyperglycemic medication nonadherence and subsequent hospitalization among individuals with type 2 diabetes. Diabetes Care. 2004;27(9):2149-2153.
15. Dall TM, Zhang Y, Chen YJ, Quick WW, Yang WG, Fogli J. The economic burden of diabetes. Health Aff (Millwood). 2010;29(2):297-303.
16. Goldman DP, Joyce GF, Lawless G, Crown WH, Willey V. Benefit design and specialty drug use. Health Aff (Millwood). 2006;25(5):1319-1331.
17. Joyce GF, Escarce JJ, Solomon MD, Goldman DP. Employer drug benefit plans and spending on prescription drugs. JAMA. 2002;288(14):1733-1739.
18. Goldman DP, Joyce GF, Escarce JJ, et al. Pharmacy benefits and the use of drugs by the chronically ill. JAMA. 2004;291(19):2344-2350.
19. Goldman DP, Joyce GF, Karaca-Mandic P. Varying pharmacy benefits with clinical status: the case of cholesterol-lowering therapy. Am J Manag Care. 2006;12(1):21-28.
20. Goldman DP, Jena AB, Lakdawalla DN, Malin JL, Malkin JD, Sun E. The value of specialty oncology drugs. Health Serv Res. 2010;45(1):115-132.
21. Romley J, Goldman D, Eber M, Dastani H, Kim E, Raparla S. Cost-sharing and initiation of disease-modifying therapy for multiple sclerosis. Am J Manag Care. 2012;18(8):460-464.
22. Seabury SA, Goldman DP, Maclean JR, Penrod JR, Lakdawalla DN. Patients value metastatic cancer therapy more highly than is typically shown through traditional estimates. Health Aff (Millwood). 2012;31(4):691-699.
23. Yeaw J, Benner JS, Walt JG, Sian S, Smith DB. Comparing adherence and persistence across 6 chronic medication classes. J Manag Care Pharm. 2009;15(9):728-740.
24. Medicare health & drug plan quality and performance ratings 2013 Part C & Part D technical notes [draft] 2012. CMS website. Updated August 9, 2012. Accessed May 26, 2015.
25. Murphy KM, Topel RH. Estimation and inference in 2-step econometric models. J Bus Econ Stat. 2002;20(1):88-97. doi:10.1198/073500102753410417.
26. Lachaine J, Petrella RJ, Merikle E, Ali F. Choices, persistence and adherence to antihypertensive agents: evidence from RAMQ data. Can J Cardiol. 2008;24(4):269-273.
27. Kronish IM, Woodward M, Sergie Z, Ogedegbe G, Falzon L, Mann DM. Meta-analysis: impact of drug class on adherence to antihypertensives. Circulation. 2011;123(15):1611-1621.
28. Heart failure disease management improves outcomes and reduces costs. 2012. Agency for Healthcare Research and Quality website. Published April 24, 2008. Accessed February 12, 2013.
29. Goldman DP, Joyce GF, Zheng Y. Prescription drug cost sharing: associations with medication and medical utilization and spending and health. JAMA. 2007;298(1):61-69. Review.
30. Volpp KG, Loewenstein G, Troxel AB, et al. A test of financial incentives to improve warfarin adherence. BMC Health Serv Res. 2008;8:272.
31. Chernew ME, Shah M, Wegh A, et al. Impact of decreasing copayments on medication adherence within a disease management environment. Health Aff (Millwood). 2008;27(1):103-112.
32. Chernew ME, Rosen AB, Fendrick AM. Value-based insurance design. Health Aff (Millwood). 2007;26(2):w195-w203.
33. Chernew ME, Juster IA, Shah M, et al. Evidence that value-based insurance can be effective. Health Aff (Millwood). 2010;29(3):530-536.
34. Seabury SA, Gupta C, Philipson TJ, Henkhaus LE, PhRMA Medication Adherence Advisory Council. Understanding and overcoming barriers to medication adherence: a review of research priorities. J Manag Care Spec Pharm. 2014;20(8):775-783.

Copyright AJMC 2006-2020 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
Welcome the the new and improved, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up