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The American Journal of Managed Care April 2015
Clinical Provider Perceptions of Proactive Medication Discontinuation
Amy Linsky, MD, MSc; Steven R. Simon, MD, MPH; Thomas B. Marcello, BA; and Barbara Bokhour, PhD
Optimizing the Use of Telephone Nursing Advice for Upper Respiratory Infection Symptoms
Rosalind Harper, PhD, RN; Tanya Temkin, MPH; and Reena Bhargava, MD
Redefining and Reaffirming Managed Care for the 21st Century
David Blumenthal, MD, MPP; and David Squires, MA
Managing Specialty Care in an Era of Heightened Accountability: Emphasizing Quality and Accelerating Savings
John W. Peabody, MD, PhD, DTM&H; Xiaoyan Huang, MD; Riti Shimkhada, PhD; and Meredith Rosenthal, PhD
Antibiotic Prescribing for Respiratory Infections at Retail Clinics, Physician Practices, and Emergency Departments
Ateev Mehrotra, MD, MPH; Courtney A. Gidengil, MD, MPH; Claude M. Setodji, PhD; Rachel M. Burns, MPH; and Jeffrey A. Linder, MD, MPH
Persistent High Utilization in a Privately Insured Population
Wenke Hwang, PhD; Michelle LaClair, MPH; Fabian Camacho, MS; and Harold Paz, MD, MS
Self-Efficacy in Insurance Decision Making Among Older Adults
Kathleen Kan, MD; Andrew J. Barnes, PhD; Yaniv Hanoch, PhD; and Alex D. Federman, MD, MPH
Limited Effects of Care Management for High Utilizers on Total Healthcare Costs
Brent C. Williams, MD, MPH
Observation Encounters and Subsequent Nursing Facility Stays
Anita A. Vashi, MD, MPH, MHS; Susannah G. Cafardi, MSW, LCSW, MPH; Christopher A. Powers, PharmD; Joseph S. Ross, MD, MHS; and William H. Shrank, MD, MSHS
Elderly Veterans With Dual Eligibility for VA and Medicare Services: Where Do They Obtain a Colonoscopy?
Ashish Malhotra, MD, MS; Mary Vaughan-Sarrazin, PhD; and Gary E. Rosenthal, MD
Costs of Venous Thromboembolism Associated With Hospitalization for Medical Illness
Kevin P. Cohoon, DO, MSc; Cynthia L. Leibson, PhD; Jeanine E. Ransom, BA; Aneel A. Ashrani, MD, MS; Tanya M. Petterson, MS; Kirsten Hall Long, PhD; Kent R. Bailey, PhD; and John A. Heit, MD
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Binary Measures for Associating Medication Adherence and Healthcare Spending
Pamela N. Roberto, MPP; and Eberechukwu Onukwugha, PhD

Binary Measures for Associating Medication Adherence and Healthcare Spending

Pamela N. Roberto, MPP; and Eberechukwu Onukwugha, PhD
The use of an 80% threshold or other binary cut point may be insufficient for characterizing the relationship between medication adherence and Medicare spending.
Take-Away Points
Patients are commonly considered to be adherent when they have medication on hand at least 80% of the time. We find evidence that a binary adherence measure
may be insufficient to characterize the association between adherent behavior and Medicare spending due to nonlinearities in the adherence/spending relationship among Part D enrollees with diabetes.
  • There is considerable heterogeneity in Medicare spending for beneficiaries above the conventional 80% adherence threshold.
  • Part A spending is highest among beneficiaries at the top end of the adherence distribution.
  • A better understanding of the adherence/spending relationship would assist stakeholders in targeting patients for adherence interventions.
Prescription medicines are an important component in the treatment of diabetes and diabetes-related complications. When taken as directed, those medicines play a key role in slowing disease progression, preventing hospitalizations, and reducing premature mortality. Despite considerable evidence for the efficacy of medications in managing diabetes and other chronic diseases, it is estimated that patients never fill 20% to 30% of newly written prescriptions, and more than 50% of medications are not taken as prescribed.1,2 Improving adherence is critically important for reducing avoidable hospitalizations and preventing the use of costly acute care, but gaps persist in our knowledge about which patients should be targeted for intervention. In particular, we lack data about the specific patient populations in whom increasing adherence could have the greatest impact to improve outcomes and lower costs.3,4

A substantial body of research demonstrates that adherence to medications used to prevent or manage diabetes and diabetes-related complications is associated with improved health outcomes and lower healthcare spending.5-13 To classify patients as adherent, researchers have typically used a cutoff of 80% for measurements of the medication possession ratio or proportion of days covered (PDC).12,14-16 This approach makes strong assumptions about the nature of the relationship between adherence and spending. These assumptions are typically treated as maintained rather than testable assumptions, and it remains unclear whether the functional form of this relationship is best characterized by a binary measure of adherence. A better understanding of the relationship between adherence and healthcare spending would help to inform stakeholders’ expectations about the magnitude of spending reductions that are achievable through improvements in adherence. Additionally, it would assist payers and providers in identifying and targeting patients who are most likely to benefit from intervention.


Data and Sample Selection

Our study used survey and claims data from the 2007 to 2010 Medicare Current Beneficiary Survey (MCBS), which is a nationally representative sample of Medicare beneficiaries. We limited our analysis to community-dwelling beneficiaries with diabetes who filled Part D–covered prescriptions for angiotensin-converting enzyme (ACE) inhibitors or angiotensin receptor blockers (ARBs), and were continuously enrolled in Medicare Parts A and B and a stand-alone Part D prescription drug plan for a full calendar year between 2007 and 2010. We classified beneficiaries as having diabetes if they self-reported having ever been given a diagnosis of diabetes by a physician. ACE inhibitors/ARBs were chosen for this analysis because they have been shown to be effective for the prevention of vascular complications in patients with diabetes and are recommended for virtually all elderly diabetics.17 Beneficiaries who received drug coverage through capitated Medicare Advantage plans were excluded because these plans do not generate the Parts A and B claims needed to evaluate medical spending.


Our dependent variable was total Medicare Parts A and B expenditures, adjusted to 2010 dollars using the all urban consumers component of the Consumer Price Index (CPI-U). To calculate adherence to ACE inhibitor/ARB therapy, we used the Part D prescription event data to determine the PDC, measured as a continuous variable ranging from 0 to 1. PDC is the adherence metric recommended by the Pharmacy Quality Alliance and is defined as the number of days a full supply of medication is on hand each month divided by the number of days in the month.18 Days spent in the hospital or a skilled nursing facility were excluded from both the numerator and de nominator, as medications were presumably administered from facility supplies during inpatient stays.

Covariates in our regression analyses included sociodemographic characteristics, measures of health status, and controls for healthy adherer bias. Sociodemographic characteristics included age, gender, race and ethnicity, US Census region, and whether or not the beneficiary received the low-income subsidy. Health status was measured by: 1) a count of self-reported comorbid chronic conditions, including hypertension, hyperlipidemia, asthma/emphysema/chronic obstructive pulmonary disease, congestive heart failure, heart disease, osteoarthritis, and depression; 2) self-reported diabetes-related complications related to the eyes, feet, or kidneys; and 3) an indicator for whether the beneficiary originally qualified for Medicare on the basis of a disability.

Variables to control for potential healthy adherer bias included self-reported health status, the number of limitations in activities of daily living, any use of statins or oral antidiabetic medications, any insulin use, education level, marital status, and diabetes management behaviors including checking one’s own blood sugar, checking for sores on feet, and having attended a diabetes management class. We hypothesized that beneficiaries who had more years of education, had spousal support, and took steps to actively manage their diabetes would be more likely to be engaged in their healthcare. If engaged beneficiaries were more likely to have both higher adherence and better overall health outcomes, this healthy adherer effect could confound the relationship between adherence and spending, thereby overestimating the impact of medication adherence.5,19-22

Analytic Approach

To characterize the bivariate association between level of medication adherence and Medicare Parts A and B spending, we stratified the sample into quintiles based on the proportion of days covered. Due to the panel design of the MCBS, beneficiaries could contribute multiple years of data to our pooled sample. This design violates a key assumption of many standard statistical analytic approaches—namely that the error terms across observations are independent and identically distributed. To account for correlation in the error terms, as well as the nonnormal and heavily right-skewed distribution of Medicare spending, we used cluster-robust generalized linear models with a gamma distribution and log link to estimate the relationship between adherence and total Medicare Parts A and B spending.23 Robust standard errors were computed using an exchangeable correlation matrix.24 Since virtually all beneficiaries in the sample (97%) had positive spending, we replaced the small share of observations with zero spending with a value of $1. To more precisely investigate the functional form of the relationship between adherence and spending, we also performed multivariate regression using Part A spending and Part B spending as dependent variables in separate regression models.

We investigated nonlinearity in the relationship between adherence and spending using polynomial transformations of the continuous adherence measure. Tests for statistical significance on models including more than 1 polynomial function (eg, squared and cubed values) are subject to type II error due to the high correlation between the transformed variables. Orthogonal polynomials reduce the possibility of type II error by creating independent nonlinear functions of a given continuous variable in order to ensure that each transformed variable is evaluated individually without risk of unfounded correlation with other transformed variables. We used Hermite polynomials—part of the family of classical orthogonal polynomials—to develop nonlinear transformations of the PDC variable for inclusion in the regression models.25 All analyses were conducted using SAS 9.2 (SAS Institute, Cary, North Carolina). The study was approved by the Institutional Review Board of the University of Maryland - Baltimore.


Application of our inclusion criteria resulted in a sample of 1881 ACE inhibitor/ARB users, 61.1% of whom met the conventional 80% threshold for adherence. The distribution of PDC was left-skewed, with an average of 77.2%, a median of 85.8%, and an interquartile range of 68.5% to 92.3%. After stratifying the sample by quintile of PDC, each quintile contained 376 observations, except for the middle quintile, which contained 377 observations.

The unadjusted bivariate association between adherence and Medicare Parts A and B spending clearly suggests a nonlinear relationship (Figure). For total Parts A and B spending, increased adherence was associated with consistently lower spending across quintiles 1 through 4, reaching a turning point between quintiles 4 and 5. In quintile 5—which represents the most highly adherent beneficiaries—average total spending was considerably higher than for the rest of the sample, except for the least adherent beneficiaries in quintile 1. Although beneficiaries in quintiles 4 and 5 both meet the conventional definition of high adherence, we observed large spending differences between these groups. For instance, the average total spending for beneficiaries in quintile 4 was $9277 versus $15,501 for those in quintile 5. Part A spending was also highest overall for quintile 5 ($8489)—more than double the amount for quintiles 3 ($4237) and 4 ($3485). We observed less overall variation in Part B spending across adherence quintiles, with no consistent pattern in the bivariate relationship between adherence and spending.

The Table summarizes the statistical significance and direction of the relationship between medication adherence and Medicare Parts A and B spending, adjusting for sociodemographic characteristics, health status, and healthy adherer effects. Our interest is in the functional form of the relationship, and therefore, we focus here on the significance of the coefficients, rather than their magnitudes, and report the full regression results in an online eAppendix (available at The statistical significance of the second- and third-order Hermite polynomials provides evidence for nonlinearities in the relationship between medication adherence and spending, but suggests a more complex nonlinear relationship than is reflected in the Figure. Specifically, the statistical significance of the third order term suggests that the relationship is characterized by multiple inflection points. The results presented in the Table indicate that this relationship between adherence and Parts A and B spending appears to be entirely driven by Part A, as we did not observe a statistically significant association between adherence and Part B spending.


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