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The American Journal of Managed Care July 2009
Adherence to Osteoporosis Medications After Patient and Physician Brief Education: Post Hoc Analysis of a Randomized Controlled Trial
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Measuring Concurrent Adherence to Multiple Related Medications
Niteesh K. Choudhry, MD, PhD; William H. Shrank, MD, MSHS; Raisa L. Levin, MS; Joy L. Lee, BA; Saira A. Jan, MS, PharmD; M. Alan Brookhart, PhD; and Daniel H. Solomon, MD, MPH
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Becky A. Briesacher, PhD; Susan E. Andrade, ScD; Hassan Fouayzi, MS; and K. Arnold Chan, MD
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Brandon T. Suehs, PharmD; and Tawny L. Bettinger, PharmD, BCPP
Impact of Workplace Health Services on Adherence to Chronic Medications
Bruce W. Sherman, MD; Sharon Glave Frazee, PhD; Raymond J. Fabius, MD, CPE; Rochelle A. Broome, MD; James R. Manfred, RPh; and Jeffery C. Davis, MBA

Measuring Concurrent Adherence to Multiple Related Medications

Niteesh K. Choudhry, MD, PhD; William H. Shrank, MD, MSHS; Raisa L. Levin, MS; Joy L. Lee, BA; Saira A. Jan, MS, PharmD; M. Alan Brookhart, PhD; and Daniel H. Solomon, MD, MPH

This study compares the performance of several definitions of concurrent adherence to related medications.

Objectives: To propose standardized methods for measuring concurrent adherence to multiple related medications and to apply these definitions to a cohort of patients with diabetes mellitus.


Study Design: Retrospective cohort study of 7567 subjects with diabetes prescribed 2 or more classes of oral hypoglycemic agents in 2005.


Methods: For each medication class, adherence for each patient was estimated using prescription-based and interval-based measures of proportion of days covered (PDC) from cohort entry until December 31, 2006. Concurrent adherence was calculated by applying these 2 measures in the following 3 ways: (1) the mean of each patient’s average PDC, (2) the proportion of days during which patients had at least 1 of their medications available to them, and (3) the proportion of patients with a PDC of at least 80% for all medication classes. Because patients taking multiple related medications have distinct patterns of use, the analysis was repeated after classifying patients into mutually exclusive groups.


Results: Concurrent medication adherence ranged from 35% to 95% depending on the definition applied. Interval-based measures provide lower estimates than prescription-based techniques. Definitions that require the use of at least 1 drug class categorize virtually all patients as adherent. Requiring patients to have a PDC of at least 80% for each of their drugs results in only 30% to 40% of patients being defined as adherent. The variability in adherence is greatest for patients whose treatment regimen changed the most during follow-up.


Conclusions: The variability in adherence estimates derived from different definitions may substantially impact qualitative conclusions about concurrent adherence to related medications. Because the measures we propose have different underlying assumptions, the choice of technique should depend on why adherence is being evaluated.

(Am J Manag Care. 2009;15(7):457-464)

Variability in adherence estimates derived from different definitions of concurrent adherence
to related medications may lead to very different qualitative conclusions about adherence in a
given population.


  • Therefore, it is important to establish how concurrent adherence to multiple related medications should be calculated.
  • This study found that concurrent medication adherence varied widely depending on the definitions applied because of the assumptions on which they are based.
  • The choice of adherence measurement technique should depend on the reason why adherence is being measured.


dherence (also called compliance) to prescribed medications is a key dimension of healthcare quality. Nonadherence is associated with poor health outcomes1 and with substantial economic cost2,3 and threatens the gains in quality that have been made by appropriate pharmacotherapy over the past several decades.4 Efforts to accurately measure and improve adherence have received increasing attention from patients, physicians, payers, and other healthcare stakeholders. Moreover, the National Committee for Quality Assurance has recently included adherence among the measures by which it evaluates the quality of care provided by healthcare plans.5


Adherence may be assessed in various ways, including surveying patients, directly observing medication taking, measuring drug or metabolite blood levels, and using electronic medication monitors.3 Pharmacy refill claims, which are highly correlated with these measures,6 provide an objective way to measure adherence for large populations of patients. Such data are frequently used by health services researchers and pharmacy benefit plan managers to calculate adherence by estimating the amount of medication available to a patient over a given interval.7


These measures were developed and have been most widely used to evaluate adherence to individual medications or medication classes. For example, Benner et al8 assessed statin adherence by measuring the proportion of days covered (PDC) for patients newly prescribed 1 of these agents. However, it is common for patients with chronic conditions (such as hypertension, coronary artery disease, or congestive heart failure) to switch between classes of medications or to receive multiple medications to treat a single disease. Such behavior is clinically sensible and formalized in practice guidelines but is inadequately captured by measuring adherence for a single agent or medication class.

Studies that have measured concurrent adherence to multiple medication classes have used various techniques,9-11 and unlike adherence to single medications or single medication classes,6,12 there are no published definitions or guidelines about how to structure these measurements. Understanding the variability in adherence estimates obtained from different measures will assist in selecting appropriate definitions. In this article, we propose standardized methods for measuring concurrent adherence, apply them to a cohort of patients with diabetes mellitus receiving treatment with oral hypoglycemic agents, and compare their performances.




Study Cohort

We assembled a cohort of patients who received pharmacy benefits through Horizon Blue Cross Blue Shield of New Jersey and who were prescribed an oral hypoglycemic agent between January 1, 2005, and December 31, 2005. We considered only those patients who had 1 or more inpatient or outpatient claims with a diagnosis of diabetes (International Classification of Diseases, Ninth Revision, Clinical Modification code 250.x) and were prescribed 2 or more classes of oral hypoglycemic medications during this period. While all oral hypoglycemics may be considered members of 1 therapeutic class, we defined classes on a mechanistic basis (ie, sulfonylurea, metformin hydrochloride, glitazones, acarbose, and meglitinides), as many patients take agents belonging to these different classes concurrently. Furthermore, this strategy allows for the generalization of our methods to patients using noninterchangeable medication classes (eg, statins, angiotensin-converting enzyme inhibitors, antiplatelets, and b-blockers after myocardial infarction).


We excluded patients who lost eligibility or did not fill any prescriptions or use any medical services in 2006. We defined an index date for each oral hypoglycemic class prescribed to each patient as the first prescription date for any member of the class during the accrual period.


We combined filled prescription data for patients in our cohort with complete paid claims data and eligibility files to create a relational database consisting of data for all filled prescriptions, procedures, inpatient and outpatient physician encounters, hospitalizations, and deaths for the patients in our cohort. Prescription information in the claims data included drug name, dosage, date dispensed, quantity dispensed, and days supplied. All traceable person-specific identifying factors were transformed into anonymous coded study numbers. The institutional review board of Brigham and Women’s Hospital approved the study.

Measures of Adherence

To measure adherence, we first created a supply diary for each patient-day by stringing together consecutive fills of each medication class being studied based on dispensing dates and reported days’ supply. All drugs dispensed within a therapeutic class (eg, glyburide and glipizide in the sulfonylurea class) were considered interchangeable. When a dispensing occurred before the previous dispensing should have run out, utilization of the new medication was assumed to begin the day after the end of the old dispensing. If a patient accumulated more than 180 days’ supply on a given day, the accumulated supply was truncated at 180 days.


We estimated adherence by calculating the PDC for each drug class prescribed to each patient from the index date to the end of our assessment period (December 31, 2006) using 2 different methods that differ in how the denominator of the adherence measure is calculated (Figure 1). We defined prescription-based adherence based on medication possession for all drugs within a class during the time between 2 prescriptions; that is, the number of days of medication supplied between the first and last prescriptions in a given period (numerator) was divided by the number of days between these 2 prescriptions plus the accumulated days supplied from the last prescription (denominator). In contrast, we defined interval-based adherence based on medication possession during the interval from the index date to December 31, 2006. In this way, the number of days of medication supplied throughout the period (numerator) was divided by the number of days in it (denominator).


Adherence to Multiple Medications Concurrently

We estimated simultaneous adherence to multiple medication classes for each patient by using the prescription-based adherence estimated for each class in 3 distinct ways (Figure 2). First, we calculated an average of the prescription-based PDC for each patient and then generated a group mean of these averages for the entire cohort. For example, for patients taking 2 classes of oral hypoglycemics, the PDC for each was calculated and then averaged, and this average was used to calculate the mean group PDC. Second, we calculated the number of days during which patients had at least 1 of their prescribed medications available to them beginning from the date they filled their first prescription for any oral hypoglycemic (ie, their earliest index date) until their latest prescription date for any of the medications they were using. For example, for a patient being treated with metformin and glyburide, the numerator of the adherence measure was the number of days during which he or she had either metformin or glyburide available. Third, we estimated the proportion of patients who had a prescription-based PDC of at least 80% for each medication they were using. For example, for patients treated with glyburide and metformin, each patient was considered adherent if his or her prescription-based PDC for each was at least 80%.


We repeated this process using interval-based adherence estimates. In this way, we determined adherence using 6 different methods (ie, 3 different techniques for each of the prescription-based and interval-based methods).


Complex Patterns of Medication Use

Patients taking multiple related medications may have many distinct patterns of medication-taking behavior, and measures of concurrent adherence may differ more substantially for some patients. Therefore, we classified patients into mutually exclusive groups and then applied our 6 adherence definitions to each group individually.


We created medication-taking groups by first generating clinically plausible scenarios of how patients may use multiple medications. For example, patients may initiate therapy with 1 medication and then may be started on a second agent at some point later for them to meet their therapeutic goals. We applied our schema to a subsample of 500 patients from our cohort and then created additional categories for patients who could not be categorized into 1 of our prespecified groups. We used 8 distinct medication-taking patterns generated in this way to categorize our entire cohort based on the definitions given in Table 1 and then applied our 6 different adherence definitions to each group. In all cases, follow-up began on the earliest prescription date for any oral hypoglycemic prescribed in 2005.


Sensitivity Analysis

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