Medication Adherence and Medicare Expenditure Among Beneficiaries With Heart Failure

Modest increases in adherence to medication regimens among Medicare patients with heart failure were associated with lower Medicare spending in 3 major drug classes.
Published Online: September 20, 2012
Ruth Lopert, MD, FAFPHM; J. Samantha Shoemaker, PhD; Amy Davidoff, PhD; Thomas Shaffer, MHS; Abdulla M. Abdulhalim, BSPharm; Jennifer Lloyd, MA; and Bruce Stuart, PhD
Objectives: To (1) measure utilization of and adherence to heart failure medications and (2) assess whether better adherence is associated with lower Medicare spending.

Study Design: Pooled cross-sectional design using six 3-year cohorts of Medicare beneficiaries with congestive heart failure (CHF) from 1997 through 2005 (N = 2204).

Methods: Adherence to treatment was measured using average daily pill counts. Bivariate and multivariate methods were used to examine the relationship between medication adherence and Medicare spending. Multivariate analyses included extensive variables to control for confounding, including healthy adherer bias.

Results: Approximately 58% of the cohort were taking an angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB), 72% a diuretic, 37% a beta-blocker, and 34% a cardiac glycoside. Unadjusted results showed that a 10% increase in average daily pill count for ACE inhibitors or ARBs, beta-blockers, diuretics, or cardiac glycosides was associated with reductions in Medicare spending of $508 (not significant [NS]), $608 (NS), $250 (NS), and $1244 (P <.05), respectively. Estimated adjusted marginal effects of a 10% increase in daily pill counts for beta-blockers and cardiac glycosides were reductions in cumulative 3-year Medicare spending of $510 to $561 and $750 to $923, respectively (P <.05).

Conclusions: Higher levels of medication adherence among Medicare beneficiaries with CHF were associated with lower cumulative Medicare spending over 3 years, with savings generally exceeding the costs of the drugs in question.

(Am J Manag Care. 2012;18(9):556-563)
Hospitalization of the elderly with congestive heart failure (CHF) results in a significant cost burden. Prior research has shown that improved medication adherence can reduce medical expenditures among elderly patients.

  • Modest increases in adherence to CHF medication regimens were associated with lower Medicare spending in 3 major drug classes, with savings exceeding the costs of the drugs.

  • The evidence suggests there may also be a degree of undertreatment in the Medicare population with CHF.

  • Policy makers should explore cost-effective methods for improving adherence to evidence-based treatment guidelines by prescribers and to prescribed medication regimens by patients.
Cardiovascular disease (CVD) remains the most costly cause of morbidity and mortality in the United States today,1,2 with expenditures estimated at $448.5 billion in 2008—twothirds attributable to direct patient care.3 Of the estimated 81 million adults with CVD, nearly 6 million have some degree of congestive heart failure (CHF), with an incidence approaching 10 per 1000 persons after age 65 years.2 Recent estimates suggest it is the single most common reason for hospitalization in the elderly,4 costing Medicare in excess of $10.0 billion in 2007,5 but it is thought that hospitalization could be substantially reduced with better outpatient care, including adequate drug therapy.6

There is also growing interest in the impact of medication adherence on health outcomes, and its consequent effects on healthcare utilization and expenditure. Despite its importance for clinical and coverage decision-making, the effect of medication adherence on healthcare expenditures has been studied in only a limited range of conditions, particularly diabetes,7-17 with very little focus on CHF despite the significant burden of disease and benefits of evidence-based treatment. Moreover, claims of cost offsets from improved adherence are often controversial, reflecting methodologic concerns about limited generalizability, short time frames, and particularly the impact of survivor bias and reverse causality. The latter implies that an inverse relationship between higher drug adherence and lower medical costs could reflect either the effect of the drug or that healthier people are more adherent to treatment, an effect known as “healthy adherer” bias.18

This study of the relationship between adherence to CHF medication and Medicare expenditure was designed to address a gap in the literature concerning cost offsets associated with CHF treatment, while also addressing methodologic limitations of prior studies. Our study cohort was selected from a nationally representative sample of the Medicare population. Users of each of 4 major drug groups for treatment of CHF were followed for up to 3 years, with explicit consideration of censoring by death or another cause. We applied an empirical specification of the relationship between medication adherence and spending on traditional Medicare services, including controls indicative of healthy lifestyle. The study protocol was approved by the University of Maryland Baltimore Institutional Review Board.


Data and Study Sample

We used the Medicare Current Beneficiary Survey (MCBS), a nationally representative rotating-panel survey of Medicare beneficiaries conducted by the Centers for Medicare & Medicaid Services. Approximately 4500 beneficiaries are selected for induction into the survey each year and are followed for up to 3 additional years using computer-assisted in-person interviews. The survey captures demographic and socioeconomic characteristics, health behaviors, and health and functional status. Self-reported information on healthcare utilization and costs of all health services is collected for years 2 through 4. The MCBS survey responses are linked to Medicare administrative data and include all matching Part A and B claims as well as prescription drug utilization and spending. Respondents are asked to maintain records of prescriptions (eg, insurance slips, used medication containers) during the year. The MCBS interviewers record all prescription fills reported during each 4-month recall period, noting the drug name, unit dosage, and quantity dispensed, although the number of days of treatment supplied is not captured.

Drawing on the MCBS from 1997 to 2005, we identified beneficiaries with a CHF diagnosis in the induction year using a claims-based algorithm developed by the Centers for Medicare & Medicaid Services for the Chronic Condition Warehouse.19 Individuals were followed for up to 3 years until they completed their MCBS tenure or were lost to follow-up, admitted to a long-term care facility for a long-term stay, or died. Six 3-year, nonoverlapping cohorts were thus created. The sample was restricted to community-dwelling beneficiaries at the time of the initial survey, and also excluded individuals who were in capitated Medicare health plans at any time during their tenure in the MCBS because they lacked the Part A and B claims data necessary for computing Medicare costs. The final sample consisted of 2204 beneficiaries with a diagnosis of heart failure at baseline.

We then identified individuals who reported filling prescriptions during their first year in the MCBS following their fall induction survey in 4 broad pharmacologic groups that are among the recommended therapies for CHF.20-24 These groups were angiotensin-converting enzyme (ACE) inhibitors or angiotensin II receptor blockers (ARBs), betablockers with approved indications in heart failure, diuretics, and cardiac glycosides. The drug user cohorts were not mutually exclusive.


Drugs within each class were identified through clinical review, using drug names reported on the MCBS. Drug adherence was based on the number of tablets dispensed. In cases where the counts of pills were missing, we imputed them by averaging intact pill counts over all years the individual was observed to use the specific drug(s) in question.

We measured adherence using average daily pill counts over the 36-month period of observation. This measure was generated by dividing the total pill count within each drug class by the number of days over which the individual was observed. This metric was independent of the number of pills dispensed at one time, which may have been 60 or even 90 days’ supply for some medications. In addition, using the number of days observed as the denominator allowed us to consider disruptions in drug regimens (eg, hospitalization, admission to a long-term care facility, death) that also affect expenditures. The dependent variable was total Medicare Part A and B spending measured over the same period. All dollar values were converted to constant 2006 dollars using the Consumer Price Index for urban consumers.25

Our multivariate models included an extensive set of covariates to control for confounding due to health status, predilection for prescription drug use, access to care, and healthy adherer bias. Health status measures were captured from 3 sources: (1) self-reported measures (a standard 5-item scale of health status from excellent to poor, body mass index computed from self-reported height and weight, and limitations in activities of daily living); (2) Medicare administrative data (current and former recipients of Social Security Disability Insurance); and (3) select claims-based comorbidities common in the elderly, including hypertension, ischemic heart disease, diabetes, chronic obstructive pulmonary disease, chronic renal disease, hyperlipidemia, and osteoarthritis, plus an index of coexisting conditions. Demographic factors potentially influencing both prescription use and outcomes included age, sex, race, marital status, and education. Access to care was captured by data on income and the possession of supplemental medical or drug coverage. Dummy variables were used to capture censoring (loss to follow-up, admission to a long-term care facility, death), as well as year of induction into the MCBS (a proxy for temporal trends in treatment).

We controlled for healthy adherer bias by including additional measures of prescription drug utilization for each of the 4 drug classes in each drug-specific equation. This approach enabled us to estimate the effect of an incremental increase in adherence with each medication conditional upon utilization of other medications in the regimen. Except for the drug utilization and censoring variables, all covariates were measured during the baseline year.

Statistical Analysis

Regressions for each of the drug groups were used to estimate the relationship between medication adherence and 3-year Medicare spending. Models were estimated using a generalized linear model with a gamma distribution and log link to approximate the right skewed distribution of Medicare costs.

Three sets of 4 models were estimated. The models within sets were estimated by restriction to users of the 4 distinct drug groups. The first set modeled the bivariate relationship between Medicare spending and average daily pill counts within the specified drug group without control variables.

The other 2 sets of models included an extensive set of covariates (Table 1) as well as additional drug measures to address confounding due to unobserved healthy adherer effects. The drug measures in the second set included daily pill counts for the specified drug group in addition to dichotomous variables indicating use of the other 3 groups of drugs. The third set of models included measures of daily pill counts for all 4 drug groups as well as indicators of use of each.

We contend that by including measures of utilization for several drugs in the same equation, any shared variance due to a healthy adherer effect is removed. However, this does give rise to larger standard errors and introduces the possibility of underestimating the true impact of drugs with similar effects on health outcomes due to multicollinearity between the drug measures.

To facilitate interpretation, all model coefficients were converted to marginal effects, holding all covariates at their sample means. All models were estimated using Stata 11 (Statacorp, College Station, Texas).


Descriptive Findings

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