The brief medication questionnaire: A tool for screening patient adherence and barriers to adherence

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Abstract

Self-report tools for monitoring adherence can be useful in identifying patients who need assistance with their medications, assessing patient concerns, and evaluating new programs. The aim of this study is to test the validity of the Brief Medication Questionnaire (BMQ), a new self-report tool for screening adherence and barriers to adherence. The tool includes a 5-item Regimen Screen that asks patients how they took each medication in the past week, a 2-item Belief Screen that asks about drug effects and bothersome features, and a 2-item Recall Screen about potential difficulties remembering. Validity was assessed in 20 patients using the Medication Events Monitoring System (MEMS). Results varied by type of non-adherence, with the Regimen and Belief Screens having 80–100% sensitivity for “repeat” non-adherence and the Recall Screen having 90% sensitivity for “sporadic” non-adherence. The BMQ appears more sensitive than existing tools and may be useful in identifying and diagnosing adherence problems.

Introduction

Patient non-compliance or lack of adherence with drug regimens continues to be a major problem in virtually all medical specialties, patient populations, and health settings [1]. Studies show that approximately 25% of all prescribed doses are omitted by patients [2]and that this non-adherence is a significant factor in cardiovascular morbidity and mortality, rejection of transplanted kidneys, leukemia relapse, vision loss in glaucoma, and other indicators of treatment failure 3, 4, 5, 6, 7. Poor adherence also has been implicated in unnecessary and costly procedures and hospitalization in asthma and other conditions [8]. Researchers have identified many determinants of non-adherence 2, 9, specific ways in which communication between professionals and patients contributes to non-adherence 9, 10, 11, and effective interventions 9, 12. Unfortunately, no single intervention is totally effective for all patients and it is not yet possible to predict which individual or subgroup actually needs a given intervention. Patients also are reluctant to admit non-adherence unless clinicians make specific efforts to monitor the degree of adherence on a regular basis 10, 11. Practitioners therefore need accurate, practical, and clinically relevant tools for “screening” or detecting adherence problems 13, 14, 15, 16. Accurate information about adherence also is useful in “targeting” interventions more effectively and efficiently [17], studying professional–patient relationships [18], interpreting drug effects 4, 19, and measuring the outcomes of patient education and disease management programs [20].

Researchers have tested the accuracy of several methods of detecting non-adherence. Unfortunately, no single method is adequate [16]. One of the earliest methods of measuring non-adherence involved physician estimates. Findings revealed that this method was no more accurate than chance alone, leading researchers to abandon it 13, 21. Later studies showed that pill counts provide useful information if done in patients' homes and the purpose of this assessment is not emphasized beforehand [14]. However, home visits are not always feasible and patients often combine medication from several bottles into a single container, making it difficult to interpret pill counts [22]. Researchers also have criticized clinic-based pill counts, because many patients do not bring containers back to the clinic [23]and those that are returned seriously overestimate adherence when compared to more objective methods 19, 24.

Pharmacy refill records and drug claims provide relatively objective, unobtrusive, and inexpensive estimates of adherence in large populations over extended periods of time 25, 26, 27, 28. However, these methods only provide a gross measure of adherence and cannot be used for short-term regimens [20]. Researchers also have used laboratory tests, blood pressure readings, and other physiological measures for detecting non-adherence 13, 29, 30; however, these methods are not always available or feasible. Another concern is that these techniques only reflect drug-taking in the day or two before the test [31]. This is a serious drawback, because patients often increase drug intake a few days before coming to the clinic, giving an erroneous impression of adherence [19].

The most innovative and sophisticated method of measuring adherence is the Medication Events Monitoring System ([MEMS®], Aprex Corporation, Fremont, CA). The MEMS involves dispensing each patient's medication in a bottle that contains a microprocessor in the cap. The microprocessor records the date and time of each bottle opening, with each opening counted as a presumptive dose. There is no assurance that patients actually consume their medication, but they would have to open and close the bottle at prescribed intervals on a daily basis to create a false pattern of adherence. Studies have demonstrated that MEMS is more accurate than other available methods and is therefore considered the “gold standard” of adherence measurement 16, 19. Despite these advantages, this tool has not yet been applied widely due to its cost and other practical issues that limit its use in large studies and routine clinical practice [20].

Self-report measures (face-to-face or telephone interviews, questionnaires, diaries) provide a practical and flexible method of assessing adherence and a unique opportunity to identify patient concerns. While self-reported adherence has been linked to clinical outcomes [22], there are serious concerns about the accuracy of these measures due to poor “sensitivity” or ability to detect true non-adherence 18, 32. In fact seven of eight published studies examining the validity of self-report adherence measures show a sensitivity level below 60% (Table 1). This means that existing tools incorrectly classify at least 40% of patients with true non-adherence. Stewart [18]attempted to improve this situation by developing a more specific and comprehensive set of questions. While her approach yielded excellent results in patients with new prescriptions (85.5% sensitivity), it failed to detect most cases of non-adherence in patients with refills (40% sensitivity).

There are several explanations for the low sensitivity of existing self-report measures. First, it is possible that no single tool can detect all types of non-adherence and that multiple tools are needed. For example, Park and Lipman [33]found that their instrument was more sensitive in detecting major (repeat) dosage errors than minor (sporadic) dosage errors. Unfortunately, most studies do not distinguish repeat and sporadic non-adherence and simply lump them together when calculating sensitivity.

Second, survey researchers have developed a number of time-proven techniques for minimizing the different types of reporting errors that are known to occur when asking people to report the frequency of behaviors that are potentially embarrassing, threatening, or difficult to report accurately for other reasons [34]. Unfortunately, these survey techniques have not been applied rigorously to the design of self-report tools for measuring adherence. For example, survey methodologists generally recommend asking about behavior during a specific time period and using a shorter recall period [34]. However, existing tools for measuring adherence often use long recall periods or an unspecified time period 15, 29. Another limitation of existing tools is that they include questions that might be worded more carefully to reduce memory errors, the level of threat or embarrassment experienced by patients who want to make a favorable impression, and other sources of response error. Examples in the published literature include overly broad questions that ask about multiple drugs in the same item [14], ambiguously worded questions that ask how “regularly” a drug was used [15], narrowly worded questions which mention only one or two types of nonadherent behavior such as forgetting doses or stopping a medication [22], leading questions which prompt or remind patients about what their doctor expects them to do 13, 15, and questions which suggest that non-adherence is “careless” behavior [22]. Whether different types of questions and alternative wording yield more accurate self reported adherence is an issue that clearly deserves further study.

The purpose of this study is to test the validity of a new self-report instrument for measuring and monitoring adherence and barriers to adherence from the patient's perspective. The instrument, referred to as the Brief Medication Questionnaire (BMQ), builds on existing theory and research about patient adherence and survey methodology. Our aims were to develop an instrument that is brief and easy to use, has good sensitivity or ability to detect different types of non-adherence, and has the potential for self-administration by patients with multiple drugs. In this paper, we test the BMQ's ability to detect repeat and sporadic non-adherence using a 5-item Regimen Screen that asks patients how they took each of their medications in the past week, a 2-item Belief Screen about drug efficacy and bothersome features, and a 2-item Recall Screen about remembering difficulties.

Section snippets

Patient recruitment and data collection

The Medication Event Monitoring System ([MEMS®], Aprex Corporation, Fremont, CA) was used to evaluate the sensitivity of a 2-page Brief Medication Questionnaire (BMQ) that we developed for measuring adherence in patients who were prescribed enalapril and captopril (angiotensin-converting enzyme [ACE] inhibitors). Patients were recruited in three pharmacies operated by a Midwestern health maintenance organization (HMO). Patients were eligible if they resided in a non-institutional setting, did

Data analysis

To assess the effects of MEMS assignment, we compare rates of dose omission for patients in the MEMS and non-MEMS groups using pill count data and the t-test. We then test the BMQ's sensitivity or ability to detect true non-adherence, as measured by MEMS. Standard epidemiologic methods 18, 35are used to define and calculate “sensitivity”, “specificity”, “positive predictive value”, and overall “accuracy” of the Regimen, Belief, and Recall Screens (Table 2). The Fisher's exact test is used to

Patient characteristics

Of 43 patients who completed the study, 21 had a standard vial and 22 had a MEMS container (Table 3). Sixty percent of study participants were male and 95% were white. Patient age ranged from 30 to 74 years (mean=52.6); education ranged from 8 to 20 years (mean=13.8); and number of scheduled medications ranged from 3 to 9 (mean=4.5). Patients had been taking their ACE inhibitor for an average of 25.4 months (range, 1 to 96). Twenty one patients were prescribed captopril and 22 were prescribed

Discussion

Our results are consistent with previous studies which show that patients generally under report their non-adherence. However, it is clear that the sensitivity and overall accuracy of self-report measures can be improved by employing established principles of survey methodology. By applying these principles more rigorously, we obtained a Regimen Screen with a sensitivity level of 80%, a positive predictive value and specificity level of 100%, and an overall accuracy of 95%. These results

Practice implications

Clinical studies are needed to evaluate the BMQ's utility in various medical and pharmacy practice settings. However, we believe it can add important dimensions to adherence monitoring and enhance communication between patients and their care givers. First, it provides a clinically relevant and flexible method of screening non-adherence in patients with diverse drugs and drug regimens. When and how often it is administered depends on the patient population and how the information will be used.

Acknowledgements

This research was supported in part by a grant from the National Corporation of Swedish Pharmacies. We thank all patients, pharmacists, and clinic staff who participated in the study and our colleagues and students who provided helpful suggestions. We especially thank Ingegard Agernas, Larry Boh, and Cynthia Raehl.

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