Using Passive Measures to Improve Patient Medication Adherence

January 1, 2008
George N. Varghese, PharmD
George N. Varghese, PharmD

Thomas C. Shank, PharmD
Thomas C. Shank, PharmD

Catherine A. Sauri, MD
Catherine A. Sauri, MD

Adherence is a complex behavioral process strongly influenced by environmental factors. Six posters designed to improve medication adherence were displayed in a medical clinic, with each poster displayed for 1 month. These posters were seen by clinic patients but, as passive measures, required no additional time on the part of clinicians. Medication adherence to antidepressant therapy was assessed for two 18-month periods. Days of therapy and median gap (the number of days a patient goes without medication before filling the next prescription) were similar between the periods. Medication possession ratio (MPR) was increased in the intervention period (0.974 vs 0.994 days). During the 6-month period that the adherence posters were displayed, persistence decreased by only 10% (versus 22% for the nonintervention period). Use of passive measures may improve patient medication adherence. In this prospective study, both the MPR and persistence were improved. (Drug Benefit Trends. 2008:20:17-24)

Adherence is a complex behavioral process strongly influenced by environmental factors. Six posters designed to improve medication adherence were displayed in a medical clinic, with each poster displayed for 1 month. These posters were seen by clinic patients but, as passive measures, required no additional time on the part of clinicians. Medication adherence to antidepressant therapy was assessed for two 18-month periods. Days of therapy and median gap (the number of days a patient goes without medication before filling the next prescription) were similar between the periods. Medication possession ratio (MPR) was increased in the intervention period (0.974 vs 0.994 days). During the 6-month period that the adherence posters were displayed, persistence decreased by only 10% (versus 22% for the nonintervention period). Use of passive measures may improve patient medication adherence. In this prospective study, both the MPR and persistence were improved. (Drug Benefit Trends. 2008;20:17-24)

When they are enrolled in clinical trials designed to evaluate drug efficacy, patients often receive extra attention from researchers about the need to closely follow or adhere to the drug therapy regimen in order to provide an accurate assessment of a drug's effects. This added attention is generally missing from routine clinical care settings where higher rates of medication nonadherence may lead to the underperformance of medical therapy effectiveness.

Lack of adherence by patients for whom antidepressant medications are prescribed is common. Hansen and Kessing1 thoroughly reviewed the issue of patient adherence to antidepressant therapies. They found that many patients with depression do not follow their prescribed treatment regimens, with rates of adherence generally lower than those with other types of therapies. In randomized trials of acute therapy for depression, 20% to 40% of patients discontinue therapy before 6 months. In epidemiological trials, more than 50% of patients stop therapy before 6 months.1

High rates of medication nonadherence with antidepressant therapies are linked to worse outcomes.2 In a study assessing medication adherence in privately insured patients who had received a diagnosis of depression, 51% were adherent for the 16-week acute treatment phase; of these, just 42% remained adherent through 33 weeks of therapy.3

A variety of interventions used to improve adherence in chronic conditions, including depression, have been studied. Examples of interventions include the use of personal trainers,4 electronic monitoring,5 peer-driven interventions,6 and behavioral therapy.7 Other studies document the effectiveness of various interventions.8-10

A common thread of most interventions is the need for clinicians to take additional time with patients. Many clinicians, already practicing under significant time constraints, may not feel that they have any additional time to devote to improving patient adherence. This study used passive measures to improve the rate of medication adherence that required no additional action or time on the part of the clinician.

What Is Adherence?
The term "adherence" connotes an active process on the part of patients and is preferred in place of the older term "compliance," which implies no active participation. The meaning of the term "adherence" has changed over time. In 1979, Haynes11 defined adherence as "the extent to which a person's behavior-taking medication, following a diet, executing lifestyle changes-follows medical advice."

A more recent definition of medication adherence by Balkrishnan12 highlights the active participation of the patient by including the level of participation achieved in a medication regimen once a patient has agreed to the regimen. Patients' behavioral response to medical recommendations, including medication taking, is a complex process that is strongly influenced by the environments in which patients live, in which health care providers practice, and in which health care systems deliver care.13,14

The rationale for enhancing adherence is rooted in the premise that patients will get well or stay well if they follow appropriate therapeutic recommendations made by health care providers and health care organizations. This assumes that patients have adequate knowledge, motivation, skills, and resources to follow through with these recommendations. However, this assumption is most likely false.

Poor medication adherence has been known about and described in medical literature for decades. During this time, many educational, motivational, and psychosocial approaches have been described and promulgated. Despite improvements seen in specific patient populations, lack of medication adherence continues to be a major obstacle to maximizing patient health. In a 2003 report, the World Health Organization noted that the rate of adherence to long-term therapy for chronic illnesses in developed countries averages only about 50% and is much worse in developing countries.15

Magnitude and Implications of Nonadherence
The effectiveness of various preventive measures and treatments for persons with cardiovascular disease, stroke, and/or associated risk factors has been demonstrated. Yet many patients do not follow the advice or therapies recommended by their health care providers. The result is that nonadherence with medical therapies adversely affects patients and society.

One study found a discrepancy rate of 76% between medications prescribed and medications (prescription and nonprescription) actually taken.16 In another study, as many as 40% of seniors did not adhere to their medication regimens.17

A review of nearly 600 studies found general nonadherence rates to average 25%, ranging from 12% to 35% depending on the disease.18 This nonadherence rate was estimated to result in 112 million unnecessary visits to health care providers and $300 billion per year in excess health care spending.18

Patients may end up in the hospital; 22% of hospitalizations have been attributed to patient nonadherence to medical therapies.19 In a study of California Medicaid patients, the risk of hospitalization was significantly correlated with the extent of medication nonadherence.20

To further meet its goals for improved risk reduction, secondary prevention, and patient outcomes, the American Heart Association convened an expert panel on adherence. The charge to the panel was to (1) evaluate existing models and research related to adherence, (2) determine whether sufficient data exist to make specific recommendations about adherence, and (3) make recommendations for future research to enhance adherence.

The panel reviewed the literature and concluded that the data are sufficient to support recommendations for improving patient outcomes by addressing issues of adherence. 21 In its report, the panel made recommendations not only for patients but also for health care providers and organizations. The problem for practicing clinicians, according to findings of the report, is that they lack additional time to devote to new adherence initiatives.21

Results of several recent studies suggest that the extent of adherence during the first month of treatment is a powerful predictor of long-term adherence, and many studies show a decrease in adherence rates over time.22-24

Some studies have shown improvement in adherence rates over time using various interventions25-27; however, not all interventions are successful. In one study using telephone interventions, medication adherence did not improve even with regular telephone follow-up by a nurse.28 In our current practice setting, time spent with patients is often limited and there are too few nurses and pharmacists to participate in telephone reminder programs.

Materials and Methods
Six sets of posters were used to promote patient adherence to medication therapies (Figure 1). These posters were changed on the first workday of every month for 6 months. Large posters (approximately 2 × 3 ft) were placed on easels in all 3 clinic patient waiting areas. Smaller posters (approximately 8.5 × 11 in) were framed and hung in each examination room. 

Figure 1. Three of the 6 posters designed to improve medication adherence that were displayed at the Brooke Army Medical Center Family Medicine Service Clinic.


This cohort study, approved by the medical center's Institutional Review Board, evaluated the effects of the posters on the medication-taking behavior of patients for whom antidepressant medication was prescribed. Adherence to an antidepressant therapy regimen was compared between two 18-month periods. The first interval was from July 1, 2002, through November 30, 2003, and the second period was from March 1, 2003, to August 31, 2004.

The length of these intervals was chosen to provide sufficient time for more accurate assessment of changes in adherence measures. In addition, 18 months was a limitation imposed by the software used. For each period, a cohort of patients who were given antidepressant medications was identified. Not all of these patients were therapy-naive at the beginning of the study period. Because some studies have suggested that early medication adherence is critical to long-term adherence, we wanted to identify only those patients who were starting a therapy during each period.

Adults aged 18 to 89 years who were patients of the Family Medicine Service Clinic and who were taking any antidepressant medication were identified using the medical center's database and clinical computer system. These patients' prescription drug information was extracted from the Pharmacy Data Transaction Service computer system, a centralized repository that captures data on all prescriptions filled for Department of Defense (DoD) beneficiaries. (Data from this system allow the DoD to maintain a complete medication profile for every patient.)

Patient prescription data were extracted from the medical center computer system for analysis by another medical center department not connected to the research. Before the analysis, the patient data were de-identified using de-identification software provided by Pfizer, Inc. The software, De-ID, replaced patient social security numbers with sequential numbers beginning with the number 1; removed all patient names, addresses, and phone numbers; and offset all dates by a user-selected number of days (between 1 and 99).

The number of offset days used during the de-identification process was not shared with the investigators but had the effect of maintaining the relationships among all the dates in the database. The result of this process was that prescriptions for any given patient could be identified as belonging to the same patient, but no patients could be identified and no project participants had access to any HIPAA-protected health information.

The de-identified data were imported into the Standardized Therapy Adherence Research Tool (START), a software package developed by Pfizer for analyzing adherence measures. This method for measurement of adherence using electronic databases has been previously described.29

START is designed to work with Microsoft Access–compatible databases to apply a series of data filters to generate consistent and reliable medication adherence measures. Adherence results can be analyzed by individual medication or by therapeutic class. We chose to analyze the data by therapeutic class in order to examine changes in adherence to antidepressant therapy in general over time.

This study evaluated 4 common measures of medication adherence: average days of therapy, persistence, median gap, and medication possession ratio (MPR) (Table 1). These measures can be used to describe medication adherence for individual patients and patient groups.

Data Analysis
START was used to identify patients who began therapy during a defined period before any analyses of adherence measures. Therapy-naive patients were identified by the use of front-end and back-end washout periods. When a front-end washout period is specified, the START wizard examines the claims database and eliminates all patient records from the transformed database in which there is an initial instance of drug or therapeutic drug use during the washout period.

Consequently, all remaining patient records represent new prescription starts, and this eliminates the possibility that some prescriptions appearing early in the database represent therapies started before the designated period. Similarly, use of a back-end washout period identifies and eliminates patients from the study who have prescriptions appearing near the end of the database. Three-month front-end and back-end washout periods were used. Failure to make these adjustments would understate the duration of therapies.

Average length of therapy is a fairly straightforward calculation. Length of therapy takes into account all prescriptions received in aggregate by all patients for any given therapy (or therapy grouping) and determines how many days of therapy on average they received.

Length of therapy is generated directly from the days supply field. The population-specific length of therapy is a weighted average (based on the number of patients).

Persistence is a measure that indicates on a month-to-month basis what percentage of a population started on a specific therapy (or therapy grouping) continues on that therapy. Persistence is a dichotomous variable that each patient receives as either a yes or no outcome for a specific time interval (monthly). Persistence calculation is based on the days supply field or quantity dispensed field. In START, the population- specific persistence calculations are weighted averages (based on the number of patients).

Median gap is not an easily explained measure. If a patient is given a prescription for a 30-day supply on the first day of a given month, it would be expected that the patient would return to the pharmacy for the next 30-day supply on or around the first day of the following month. Gap days are defined as how many days, after the anticipated refill date, before the patient picks up the next month's medication supply. For example, if the patient did not return to the pharmacy until the 15th of the following month, this would be recorded as 15 gap days in therapy. This tracking would be continued until the end of the study period when the median gap could be calculated. A small median gap would indicate a more adherent patient than would a large median gap.

MPR indicates how much medication a patient obtained over a given period of time relative to how much medication the patient should have received. The MPR analysis is calculated by first tallying how many days' supply of medication the patient should have received between the first and last prescription. The number of days of therapy is then divided by the optimum number of therapy days for each patient.

MiniTab version 14.20.0, Minitab Inc, State College, Pa, was used for statistical analyses. Descriptive statistics were computed and displayed for baseline demographics. Means and standard deviations were used to summarize continuous measures. Comparisons of the groups were planned for age and sex using appropriate parametric or nonparametric tests based on the type of data.

MPR at a number of days was used as a composite measure of adherence. For example, MPR 180 is the number of days of therapy that a patient has available for the first 180 days of therapy. MPR at 180 days will be low if a patient has short persistence, large median gaps, or a low MPR. An MPR at 180 days will be more accurate if the back-end washout period is at least 180 days.


Patient demographics. In the baseline period, 1948 patients received 9759 prescriptions for antidepressant medications compared with 2329 patients and 11,631 prescriptions in the intervention group (Table 2).

Age of participants was examined for normality of distribution using the Anderson-Darling test. Because age was not normally distributed in either group (P < .005), the groups were compared using the Mann-Whitney test. The 2 groups were found to have similar age (median of 52.6 years in the baseline group and median of 52.2 years in the intervention group) and sex (20.0% men in the baseline group and 20.4% men in the intervention group; chi-square, P = .76). In both groups, male patients were significantly older than female patients (Kruskal-Wallis, P < .001).

Adherence findings. Study findings are detailed in Table 3. Length of therapy was calculated by determining the number of days between the first day of new therapy and the final day of the last prescribed therapy. The average days of therapy were identical for both groups: 162 days.

Ideally, patients who started with a specific long-term therapy would tolerate it well and continue to follow it regularly as prescribed. This would cause the persistence rate to approach 100%. However, because many persons prematurely stop taking their medications, the persistence rate tends to decline over time for many chronic medications. During the first 3 months of the intervention period (before the posters were displayed), the persistence rate declined more than it did in the baseline period (Figure 2). Beginning when the posters were displayed, the persistence rate in the intervention period declined by only 10% compared with 22% for the baseline group.


Figure 2. The decline seen in medication persistence rates over time slowed during the intervention period when the posters encouraging adherence to patients' medication regimens were on display.


Gap days are the median number of days that patients in the identified therapeutic group are without therapy between prescriptions refills. In this study, both groups had similar median gaps (9.84 days vs 9.88 days). The MPR rates were also similar: 0.974 for the baseline group and 0.994 for the intervention group.


This cohort study included all adult patients starting on antidepressants. There was no attempt to exclude patients by using filters or selection criteria. The periods for the study were overlapping but structured so as not to have any patients in common. The application of the front-end washout period for each patient group ensured that only treatment-naive patients were included in the analysis.

Our data suggest that the use of passive measures, such as posters, may improve patient adherence to their medication regimens. This was demonstrated by the arrested decline in the persistence rate during the intervention period and the increase in the MPR. No significant difference was seen between the 2 periods in mean days of therapy or median gap.

On the basis of an analysis of the data for this study and subsequent data, it was found that both the mean days of therapy and possession ratios are higher in this study population compared with those in other published data. This may be a result of the way that the computer systems record prescriptions. The data used for this analysis are not pharmacy claims data per se but are derived from a pharmacy database record. The DoD computer system records any modification of a prescription as a new duplicate record, and as a result, may artificially elevate some measures of adherence even though attempts were made to eliminate duplicate records. In using DoD data, persistence and median gap may be the best measures of adherence.

Although the analysis presented in this study was for antidepressant therapy, the use of passive measures, such as posters, could affect medication-taking behaviors for patients receiving long-term therapies. The messages delivered on the posters were general in nature, not directed at depression or depression therapies. Further studies are required to document the effectiveness of such interventions using passive measures.

Limitations.The limitations of this analysis are common to other analyses that use retrospective data. Patients in this study were not randomized to receive treatment, and clinicians were not blinded to which patients received treatment. The electronic databases used in this study did not contain data on the severity of depression. This analysis assumed that patients actually took all the medications that were prescribed and dispensed. Some patients may have been given antidepressants for conditions other than depression (eg, generalized anxiety disorder, obsessive-compulsive disorder, migraine, panic attack, and menstrual pain).

Use of pharmacy data is limited in that routine analyses cannot distinguish patients who are nonadherent from those who stop taking medication because they died or their physician discontinued the therapy. In this analysis, patients were not eliminated from the study on the basis of how many (or how few) prescriptions they filled during the study period. It is unlikely, however, that these limitations would be the cause of significant differences between the 2 study groups as both groups of patients were drawn from the same population, both groups were treated by the same clinicians, and the data were analyzed in the same way.

Study strengths. The DoD system captures all prescriptions for DoD beneficiaries filled at military facilities, retail pharmacies, and the DoD mail order pharmacy. Use of a 3-month front-end washout period helped refine the data set to ensure that only treatment-naive patients were included in the analyses. Use of a back-end washout period helped strengthen the analyses by not including patients who would have continued therapy beyond the study period. A final strength of this study is that the groups were likely to be similar because they were treated in the same clinic by the same clinicians for overlapping periods.

Department of Defense Disclaimer
The opinions or assertions contained herein are the private views of the authors and are not to be construed as official or reflecting the views of the Department of Defense or United States Government.



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