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The American Journal of Managed Care March 2015
Evaluation of Care Management Intensity and Bariatric Surgical Weight Loss
Sarit Polsky, MD, MPH; William T. Donahoo, MD; Ella E. Lyons, MS; Kristine L. Funk, MS, RD; Thomas E. Elliott, MD; Rebecca Williams, DrPh, MPH; David Arterburn, MD, MPH; Jennifer D. Portz, PhD, MSW; and Elizabeth Bayliss, MD, MSPH
Potential Savings From Increasing Adherence to Inhaled Corticosteroid Therapy in Medicaid-Enrolled Children
George Rust, MD, MPH, FAAFP, FACPM; Shun Zhang, MD, MPH; Luceta McRoy, PhD; and Maria Pisu, PhD
Innovation in Plain Sight
Karen Ignagni, MBA, President and Chief Executive Officer, America's Health Insurance Plans
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Jean Yoon, PhD, MHS; Chuan-Fen Liu, PhD, MPH; Jeanie Lo, MPH; Gordon Schectman, MD; Richard Stark, MD; Lisa V. Rubenstein, MD, MSPH; and Elizabeth M. Yano, PhD, MSPH
Are Healthcare Quality "Report Cards" Reaching Consumers? Awareness in the Chronically Ill Population
Dennis P. Scanlon, PhD; Yunfeng Shi, PhD; Neeraj Bhandari, MD; and Jon B. Christianson, PhD
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Glen B. Taksler, PhD; and R. Scott Braithwaite, MD, MSc, FACP
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Weiwei Chen, PhD; Teresa M. Waters, PhD; and Cyril F. Chang, PhD
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Jad Hayes, MS, ASA, MAAA; J. Russell Hoverman, MD, PhD; Matthew E. Brow, BA; Dana C. Dilbeck, BA; Diana K. Verrilli, MS; Jody Garey, PharmD; Janet L. Espirito, PharmD; Jorge Cardona, BS; and Roy Beveridge, MD
The Combined Effect of the Electronic Health Record and Hospitalist Care on Length of Stay
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Strategy for a Transparent, Accessible, and Sustainable National Claims Database
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Treatment Patterns, Healthcare Utilization, and Costs of Chronic Opioid Treatment for Non-Cancer Pain in the United States
David M. Kern, MS; Siting Zhou, PhD; Soheil Chavoshi, MS; Ozgur Tunceli, PhD; Mark Sostek, MD; Joseph Singer, MD; and Robert J. LoCasale, PhD
Trends in Mortality Following Hip Fracture in Older Women
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Long-Term Outcomes of Analogue Insulin Compared With NPH for Patients With Type 2 Diabetes Mellitus
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Factors Affecting Medication Adherence Trajectories for Patients With Heart Failure
Deborah Taira Juarez, ScD; Andrew E. Williams, PhD; Chuhe Chen, PhD; Yihe Goh Daida, MS; Sara K. Tanaka, MPH; Connie Mah Trinacty, PhD; and Thomas M. Vogt, MD, MPH

Factors Affecting Medication Adherence Trajectories for Patients With Heart Failure

Deborah Taira Juarez, ScD; Andrew E. Williams, PhD; Chuhe Chen, PhD; Yihe Goh Daida, MS; Sara K. Tanaka, MPH; Connie Mah Trinacty, PhD; and Thomas M. Vogt, MD, MPH
Nonwhite race, smoking, and increasing body mass index were associated with the lowest adherence trajectories for patients with heart failure, with adherence dropping off within the first year.

Objectives: To examine the relationship between patient characteristics and medication adherence trajectories for patients with congestive heart failure (CHF).

Study Design: Historical prospective study.

Methods: We conducted a secondary analysis of data assembled for the Practice Variation and Care Outcomes (PRAVCO) study, which examined patterns of cardiovascular care. We used groupbased trajectory modeling to define medication adherence trajectories, and then modeled factors associated with belonging to a trajectory group during the 6year period from 2005 to 2010 (n = 10,986). We focused on the use of angiotensinconverting enzyme (ACE) inhibitors or angiotensin II receptor blockers (ARBs) for secondary prevention of CHF.

Results: Four trajectory groups were optimal in characterizing adherence level patterns: 1) low adherence group, with an initial average adherence rate of 62% that dropped to between 40% and 50%; 2) increasing adherence group, with an initial average adherence rate of 55% that increased to 90%; 3) decreasing adherence group, with an initial average adherence rate above 90% that decreased to 60%; 4) high adherence group, with an average adherence rate consistently above 90%. Age, region, education, smoking, and race were all significantly associated with the likelihood of belonging to a particular trajectory. Nonwhites were less likely to be in the high adherence group, and smoking was more common in the low adherence group (22%) than in the high group (10%); increasing body mass index and Charlson Comorbidity Index (CCI) scores were also associated with being in the low adherence group.

Conclusions: Population characteristics associated with sustained low adherence might be used to target interventions and improve vulnerable patients’ prospects of heart health.

Am J Manag Care. 2015;21(3):e197-e205

  • Four distinct longitudinal patterns characterize medication adherence for patients with heart failure: 1) low; 2) increasing; 3); decreasing; and 4) high.
  • Nonwhites and patients who smoke or with increasing body mass index are less likely to be in the high adherence group and may need targeted interventions.
  • The factors that explain why nonwhite patients are more likely to follow a clinically inferior adherence trajectory should be examined in future research.

Often caused by coronary artery disease, congestive heart failure (CHF) is a complex clinical syndrome of symptoms and signs that occur when the heart is not pumping efficiently. Currently, more than 5 million people in the United States suffer from CHF, and the prevalence is expected to rise over time as a result of an aging population, improved survival of people with ischemic heart disease, and more effective treatments for CHF.1,2 Clinical guidelines suggest offering angiotensinconverting enzyme (ACE) inhibitors or angiotensin receptor blockers (ARBs) along with betablockers as firstline treatment for patients with CHF.3

Despite widespread knowledge among providers of the efDespite widespread knowledge among providers of the effectiveness of ACE inhibitors/ARBs in patients with CHF, a study by McGlynn and colleagues found that only 64% of patients with CHF were receiving recommended care.4 Other studies have found that even among people who see their providers regularly, 50% are nonadherent to prescribed medications after 6 months.5 This nonadherence results in thousands of premature deaths, preventable hospitalizations, and increased healthcare costs.6,7 Of all medicationrelated hospital admissions, 33% to 69% are due to poor medication adherence, with an approximate cost of $100 billion annually.811 Greater adherence to medications for chronic conditions including hypertension, diabetes, CHF, and hypercholesterolemia has been associated with higher medication costs but net overall reduction in healthcare costs.12,13

We examined data developed from the electronic medical records of 2 large integrated healthcare systems to identify longitudinal medication adherence trajectories that captured the patterns of change in adherence observed in this population over 6 years. Trajectories are simply patterns of change in medication adherence: high adherence sustained over time, for example, or rapid decline following high initial adherence. Two recently published studies have found advantages of groupbased trajectory models (GBTMs) over traditional adherence measures in analyzing patterns of medication adherence. Franklin et al found that GBTMs summarized adherence patterns better than traditional approaches, including average medication possession ratios (MPRs),14 while a study by Li et al demonstrated that GBTMs captured dynamic adherence behavior over time better than percent of covered days, a more commonly used measure.15

Our goal was to determine whether any demographic or behavioral factors were associated with the probability of belonging to a trajectory group that poses a risk to cardiovascular health. After identifying the optimal subset of adherence trajectory groups, we determined the population characteristics that were associated with clinically suboptimal adherence trajectory groups. Such factors could be used to focus adherence promotion interventions and thereby prevent cardiovascular morbidity and mortality.


This historical prospective study involved secondary analysis of data assembled for the Practice Variation and Care Outcomes (PRAVCO) study.16 The PRAVCO study used electronic medical and administrative records over an 11year period in 2 large Kaiser healthcare systems— Hawaii and the Pacific Northwest—covering approximately 750,000 persons in geographically and ethnically diverse populations. PRAVCO examined variation in the secondary prevention and management of cardiovascular disease to determine its relationship to outcomes of care for physicians. The goal of PRAVCO was to develop electronic medical record–based qualityofcare indices for 11 cardiovascular primary care services. It related physicians’ prior index scores to subsequent disease incidence and to care utilization in their patients.

Our study focused on factors affecting adherence to ACE inhibitors or ARBs for secondary prevention of CHF during the 6year period of 2005 to 2010 (n = 10,986). To be included in the study, an enrollee needed to have been hospitalized for a cardiovascular event, have a prior diagnosis of CHF, be at least 21 years old, and have been prescribed an ACE inhibitor or ARB. We excluded data prior to 2005 due to inconsistencies in the reporting of race. In order to have enough data to create medication adherence trajectories, only patients with 3 or more years of relevant adherence data were included. These 3 years were not required to be consecutive.

Factors Affecting Adherence

For the PRAVCO study, race information was abstracted from the electronic medical records. We grouped race into 5 categories: 1) white (n = 7562); 2) black (n = 235); 3) Asian (n = 1599); 4) Pacific Islander (n = 208); and 5) other (n = 1382). We used white as the reference group. We separated Asians from Pacific Islanders because there is increasing evidence that highlights differences in quality of care and health outcomes between the 2 groups.17-19

Our models also included age, gender, smoking status, education level, and region (Pacific Northwest or Hawaii). Enrollment, region, age, and gender information were from health plan administrative data. Education level was obtained through geocoding based on year 2000 census data. Body mass index (BMI) and smoking status were obtained at inperson clinical encounters and retrieved from electronic medical record data sources. BMI was calculated every year using height and weight; if more than 1 measure existed in a year, the latter one was used. BMI was measured as a continuous variable; and any change was calculated as BMI (time period 2) – BMI (time period 1). The Charlson Comorbidity Index (CCI) score was constructed from inpatient International Classification of Diseases, Ninth Revision, Clinical Modification diagnoses that were modified by removal of the cardiovascular component because all patients had cardiovas-cular disease.20

Medication Adherence

Pharmacy dispensing data from participating healthcare organizations were used to calculate an annual MPR21,22 that represented the days in a calendar year that patients possessed ACE inhibitors and ARBs and were not hospitalized, and that were subsequent to a primary hospital discharge diagnosis of CHF. The MPR was computed using the remainder of the nonhospitalized days in the year as the denominator and the number of days covered by the dispensing in the numerator. Days covered by dispensing in a prior calendar year were carried over and contributed to the numerator in the next year. Days within the calendar year were excluded from both the numerator and denominator if they were prior to the first ever known inpatient diagnosis of CHF or if they were spent in the hospital.

Statistical Analysis

We summarized demographic characteristics at baseline and calculated mean medication adherence scores for each racial group over time. For the main analyses, we used a GBTM approach. GBTM23 is an application of finite mixture modeling24,25 that assumes a population is composed of a mixture of distinct groups defined by their longitudinal trajectories. The first stage of model development defines groups, with a group being a set of individuals whose outcomes (medication adherence, in this case) follow approximately the same longitudinal course.

The collection of GBTM trajectory groups approximates a continuous population distribution of trajectories that is of unknown shape. Each group summarizes the trajectories in a distinctive region of this continuous distribution. To determine the best model, we used the Bayesian information criteria26,27 and Akaike information criterion,28 as well as consideration of the strengths and weakness of alternative specifications to select the most parsimonious model by balancing model complexity (number of parameters) versus goodness of fit to the sample data.

The model that best fit our data (depicted in Figure 1) assumes that the population we observed is composed of 4 distinct medication adherence trajectories. In a second stage, we examined how the probability of trajectory group membership depends on individuals’ preexisting sociodemographic characteristics, such as race, or on characteristics that change during the period of time covered by the trajectory such as their health (ie, comorbid illness) and health risk characteristics (ie, BMI).

Model parameters representing these characteristics quantify the risk and protective factors associated with membership in a trajectory group. We estimated trajectory groups in a model assuming a normal distribution in the first step.

Then we examined the effect of participant characteristics on trajectories in an extension of the basic model in the second step.

The interpretation of the model parameter estimates for individual characteristics depends on whether they are fixed or timevarying. Estimates for fixed characteristics (eg, race) represent the probability of trajectory group membership given a characteristic relative to a reference group. The probability estimates of trajectory group membership vary depending on that fixed characteristic. For timevarying characteristics, they estimate the change in adherence associated with a unit of increase (eg, the change in adherence among individuals within a trajectory group when their CCI score increases from 0 to 1). Hence, our approach yields 3 kinds of results: the shapes of the trajectories, the proportion of the population that is best characterized by each adherence trajectory group, and the probability of belonging to each trajectory group given personlevel attributes.

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