Scores on a new medication adherence scale maintained a strong graded association with antihypertensive drug pharmacy fill adherence among community-dwelling seniors in a managed care organization.
Objective: To evaluate the association and concordance of the new 8-item self-report Morisky Medication Adherence Scale (MMAS) with pharmacy fill data in a sample of community-dwelling seniors with hypertension.
Study Design: Cross-sectional study.
Methods: Pharmacy records for antihypertensive medications were abstracted for 87 managed care adult patients with hypertension 65 years and older who completed a survey that included the MMAS. Continuous single-interval medication availability (CSA), medication possession ratio (MPR), and continuous multiple-interval medication gaps (CMG) were calculated using pharmacy data. The MMAS adherence was categorized as high, medium, and low (MMAS scores of 8, 6 to <8, and <6, respectively); pharmacy fill nonpersistence was defined as less than 0.8 for CSA and MPR and as greater than 0.2 for CMG.
Results: Overall, 58%, 33%, and 9% of participants had high, medium, and low medication adherence, respectively, by the MMAS. After adjustment for demographics and in comparison to high adherers on the MMAS, patients with low MMAS adherence were 6.89 (95% confidence interval [CI], 2.48-19.10) times more likely to have nonpersistent pharmacy fill adherence by CSA and were 5.22 (95% CI, 1.88-14.50) times more likely to have nonpersistent pharmacy fill adherence by MPR. Concordance between the MMAS and CSA, MPR, and CMG was 75% or higher.
The MMAS is significantly associated with antihypertensive drug pharmacy refill adherence. Although further validation of the MMAS is needed, it may be useful in identifying low medication adherers in clinical settings.(Am J Manag Care. 2009;15(1):59-66)
Compared with pharmacy fill, the self-report Morisky Medication Adherence Scale (MMAS) performed well in identifying patients with low adherence to antihypertensive medication regimens.
The MMAS tool is simple and economical to use in routine outpatient settings and may provide clinicians and administrators with important information to guide treatment decisions for patients with hypertension.
Patient surveys were conducted from December 2002 to March 2003 using a standardized data collection instrument. The survey data (including sociodemographic data and medication adherence) were entered into a Microsoft Access database (Microsoft Corporation, Redmond, WA) and were transferred to SAS version 9.1.3 (SAS Institute, Inc, Cary, NC) for analysis; quality check revealed less than 1% data entry error. All patient identification information was collected and maintained according to Health Insurance Portability and Accountability Act of 1996 regulations and health plan privacy rules.
The managed care organization’s data warehouse system was the source of the pharmacy fill data for the present study. The data warehouse (an Oracle relational database [Oracle Corporation, Redwood Shores, CA]) was populated with historic claims data, patient roster data, diagnosis and procedural codes, and code descriptions. Data were extracted by informatics analysts using the Oracle Discoverer tool and were transported into SAS version 9.1.3. The pharmacy data were abstracted on 87 patients and included 42 different antihypertensive medications with 1578 fills captured in the study period.
Pharmacy fill data were extracted for the 2002 calendar year and included a listing of all antihypertensive prescriptions filled, the date filled, generic and brand names of the drugs, and number of pills dispensed. The following 3 measures of adherence were calculated: continuous single-interval medication availability (CSA), medication possession ratio (MPR), and continuous multiple-interval medication gaps (CMG).17,18 The CSA was calculated by dividing the days’ supply obtained at a pharmacy fill by the number of days before the next pharmacy fill for that same medication. The MPR was calculated as the sum of the days’ supply obtained between the first pharmacy fill and the last fill (the supply obtained in the last fill was excluded), divided by the total number of days in this period. The CMG was calculated by dividing the total number of days without medications (ie, treatment gaps) between the first and last pharmacy fills by the number of days in this period. A graphical example of how CSA, MPR, and CMG were calculated is provided in eAppendix B, available at www.ajmc.com.
For every participant, CSA was calculated for each pharmacy fill interval, and MPR and CMG were calculated by class of antihypertensive medication being taken. Values greater than 1 were truncated at the maximum value of 1 for CSA and MPR.19 Given that self-reported adherence reflects adherence to participants’ antihypertensive medication regimen, 1 CSA was assigned to each participant based on the mean of all CSAs calculated from all of his or her antihypertensive drug pharmacy fill intervals. One MPR and 1 CMG were assigned to each participant. For participants filling more than 1 class of antihypertensive medication, MPR and CMG were calculated for each class and then averaged across all classes to assign a single MPR and CMG to each participant. Given that a cut point of 0.8 has been previously used to define adequate medication adherence using pharmacy data,19-22 pharmacy fill nonpersistence was defined as less than 0.8 for CSA and MPR and as greater than 0.2 for CMG. The continuous single-interval gap statistic is the inverse of CSA and is not presented.
Of 87 patients included in the study, the mean age was 76 years, 31% were men, 48% were black, 47% had graduated from high school, 43% were married, 43% smoked cigarettes, and the mean number of antihypertensive medications being taken was 2.2 (range, 1-4 medications). The mean (SD) MMAS score was 7.4 (0.9). Demographic characteristics of the study participants by MMAS category are given in Table 1. A significantly higher percentage of black versus white patients were low adherers by the MMAS. There were no other significant differences in patient demographics across MMAS categories. By self-report using the MMAS, the distribution of participants’ adherence to prescribed medication use in this study population was 58% high adherence, 33% medium adherence, and 9% low adherence. The distributions of the pharmacy fill adherence measures are shown in the Figure. The median CSA, MPR, and CMG were 0.91, 0.91, and 0.12, respectively (Table 2). However, 23% and 25% of patients had nonpersistent CSA and MPR, respectively, and 35% had nonpersistent CMG.
Association of the MMAS With Nonpersistence as Determined by Pharmacy Fill
Hypertension, a public health challenge in the United States and worldwide, is a modifiable risk factor for cardiovascular events.23,24 Clinicians require information on antihypertensive medication adherence to draw proper conclusions about the effectiveness of treatment.25 The goal is to have access to a quick, reasonably accurate self-report adherence measure for use in outpatient settings to facilitate clinical decision making. We evaluated the accuracy of a new self-report measure by assessing its association and concordance with pharmacy fill rates. To our knowledge, the concordance between the MMAS and pharmacy fill for antihypertensive medications has not been previously evaluated. In this study, the MMAS maintained a strong, graded, statistically significant association with pharmacy fills. Using CSA, MPR, and CMG for comparison, the MMAS correctly classified at least 75% of patients as being adherent or not. Furthermore, patients classified as low adherers by the MMAS were significantly more likely to be nonpersistent by each measure of pharmacy fill. Therefore, the MMAS may be a practical and valid approach for identifying low adherers to chronic medication regimens in outpatient settings.
Low adherence poses unique challenges for clinicians trying to determine if prescribed treatment is effective. If clinicians are able to accurately identify patients with low adherence, then appropriate and timely interventions can be implemented. Several modifiable factors have been reported to negatively affect adherence to prescribed therapies. These include forgetfulness,11 depression,26 lack of knowledge regarding hypertension and its treatment,27 complexity of medication regimen,28 healthcare system perceptions by the patient,29 sexual dysfunction,30 adverse effects of medication,31 and poor quality of life.32 The MMAS provides information on behaviors associated with low adherence that may be unintentional (eg, forgetfulness) or intentional (eg, not taking medications when one feels worse). Identification of these behaviors can facilitate tailoring of interventions to specific patient issues.33 For example, if a patient is identified as a low adherer by the MMAS and the responses indicate forgetfulness as a major barrier, then the clinician may suggest that the patient use weekly pill boxes and engage a family member or friend to assist with medication reminders. If a patient is identified as a low adherer and responds that she stops taking medications when she feels better or worse, then the clinician can address knowledge barriers and medication adverse effects and educate the patient about the chronic nature of hypertension and the importance of taking medication as prescribed. On the other hand, if patients are found to be high adherers and their blood pressure remains uncontrolled, then the clinician should consider increasing medication dosage or adding a medication to their regimen.2,34 It may be that the use of a simple tool (eg, the MMAS) in the outpatient setting may allow clinicians to eliminate low adherence as a contributing factor to poor blood pressure control.
Although the concordance between self-report and pharmacy fill was good in this study, it was not perfect. Shortcomings of self-report include reliance on recall and social desirability bias, with a tendency to overestimate adherence.10 In addition, pharmacy fill rates may not capture some nuances of medication adherence behavior9 and are not practical to capture in real time among outpatient clinical encounters. It is generally assumed that patients who fill medications also take them unless they have been instructed by their provider otherwise or have adverse effects that limit their medication intake.19 It is possible that patients’ medication adherence varies by drug class. In the present study, 21 patients had 1 or more drug classes with an MPR of less than 0.8 and had an MPR of 0.8 or higher for other drug classes. Even after accounting for this using a generalized estimating equation, a strong and statistically significant association between selfreported medication adherence and MPR and CMG nonpersistence pharmacy fill rates remained present (data not shown). This suggests that averaging the pharmacy measures did not mask any drug class–specific relationships.
Stroupe and colleagues35 reported that more than 20% oversupply (ie, MPR, ≥1.2) is related to a similar risk of future hospitalization as that experienced by patients with an undersupply of medication (ie, MPR, <0.8). In the present study, only 5 patients had a mean MPR of 1.2 or higher, none of whom had low adherence on the MMAS. After excluding these 5 patients, the associations between nonpersistence and self-report medication adherence were similar to those of the original analysis. When we reclassified these patients as nonpersistent, the results were also similar (data not shown).
Study Limitations and Strengths
There are several important clinical implications of our findings. Compared with pharmacy fill, the selfreport MMAS performed well in identifying patients with low adherence to antihypertensive medication use. These patients are likely at greatest risk for uncontrolled blood pressure and subsequent adverse outcomes and could benefit most from tailored interventions to overcome barriers to adherence. Although pharmacy fill data represent an objective assessment of medication adherence, this is impractical for use in clinical settings and does not provide information on reasons for low adherence. The MMAS tool is simple and economical to use in routine outpatient settings and may provide clinicians with important information (ie, barriers to adherence) to guide treatment decisions for patients with hypertension. Scores on the MMAS maintained a strong graded association with antihypertensive drug pharmacy fill adherence in community-dwelling seniors receiving healthcare through a managed care organization. This association suggests that patients’ self-report of adherence behavior is consistent with the rate at which they fill their antihypertensive medications. The present study extends prior work demonstrating the internal reliability and predictive validity of the MMAS with respect to blood pressure control. This 8-item tool is simple and feasible to incorporate into clinical practice and may be useful in identifying patients at risk for medication adherence issues, including low adherence in outpatient settings.
Author Affiliations: From the Center for Health Research (MK-W), the Department of Cardiology-Hypertension Section (RNR), Ochsner Clinic Foundation, New Orleans, LA; the Department of Epidemiology (MK-W, TI), the Department of Family and Community Medicine (MK-W), the Department of Biostatistics (LSW), Tulane University, New Orleans, LA; the Department of Community Health Sciences (DEM), University of California, Los Angeles, Los Angeles, CA; and the Department of Community and Preventive Medicine (PM), Mount Sinai School of Medicine, New York, NY.
Author Disclosure: The authors (MK-W, TI, LSW, RNR, DGM, PM) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Funding Source: The project described was supported in part by grant R01 AG022536 from the National Institute on Aging and by the Ochsner Clinic Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Aging or the National Institutes of Health.
Authorship Information: Concept and design (MK-W, RNR, DEM, PM); acquisition of data (MK-W); analysis and interpretation of data (MK-W, TI, LSW, RNR, PM); drafting of the manuscript (MK-W, LSW, DEM, PM); critical revision of the manuscript for important intellectual content (TI, LSW, PM); statistical analysis (TI, LSW, PM); obtaining funding (MK-W); and supervision (MK-W).
Address correspondence to: Marie Krousel-Wood, MD, MSPH, Center for Health Research, Ochsner Clinic Foundation, 1514 Jefferson Hwy, New Orleans, LA 70121. E-mail: firstname.lastname@example.org.
1. Ong KL, Cheung BM, Man YB, Lau CP, Lam KS. Prevalence, awareness, treatment, and control of hypertension among United States adults 1999-2004. Hypertension. 2007;49(1):69-75.
3. Krousel-Wood MA, Thomas S, Muntner P, Morisky DE. Medication adherence: a key factor in achieving blood pressure control and good clinical outcomes in hypertensive patients. Curr Opin Cardiol. 2004;19(4):357-362.
5. Bond WS, Hussar DA. Detection methods and strategies for improving medication compliance. Am J Hosp Pharm. 1991;48(9):1978-1988.
7. Schiff GD, Fung S, Speroff T, McNutt RA. Decompensated heart failure: symptoms, patterns of onset, and contributing factors. Am J Med. 2003;114(8):625-630.
9. Grymonpre R, Cheang M, Fraser M, Metge C, Sitar DS. Validity of a prescription claims database to estimate medication adherence in older persons. Med Care. 2006;44(5):471-477.
11. Morisky DE, Green W, Levine DM, et al. Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care. 1986;24(1):67-74.
13. Morisky DE, Ang A, Krousel-Wood MA, Ward H. Predictive validity of a medication adherence measure in an outpatient setting. J Clin Hypertens (Greenwich). 2008;10(5):348-354.
15. Garber MC, Nau DP, Erickson SR, Aikens JE, Lawrence JB. The concordance of self-report with other measures of medication adherence: a summary of the literature. Med Care. 2004;42(7):649-652.
17. Steiner JF, Prochazka AV. The assessment of refill adherence using pharmacy records: methods, validity, and applications. J Clin Epidemiol. 1997;50(1):105-116.
19. Sikka R, Xia F, Aubert RE. Estimating medication adherence using administrative claims data. Am J Manag Care. 2005;11(7):449-457.
21. Kopjar B, Sales AE, Pi~neros SL, Sun H, Li YF, Hedeen AN. Adherence
22. Simpson E, Beck C, Richard H, Eisenberg MJ, Pilote L. Drug prescriptions
23. Fields LE, Burt VL, Cutler JA, Hughes J, Roccella EJ, Sorlie P. The burden of adult hypertension in the United States 1999 to 2000: a rising tide. Hypertension. 2004;44(4):398-404.
25. Feinstein AR. Compliance bias and the interpretation of therapeutic trials. In: Haynes RB, Taylor DW, Sackett DL, eds. Compliance in Health Care. Baltimore, MD: Johns Hopkins Press; 1979:309.
27. Egan BH, Lackland DT, Cutler NE. Awareness, knowledge and attitudes of older Americans about high blood pressure. Arch Intern Med. 2003;163(6):681-687.
29. World Health Organization Web site. Hypertension in adherence to long-term therapies: evidence for action. 2003. http://www.connectedhealth.org/programs/medication-adherence/research-materialsâ€”
30. Wassertheil-Smoller S, Blaufox MD, Oberman A, et al. Effect of antihypertensives on sexual function and quality of life: the TAIM study. Ann Intern Med. 1991;114(8):613-620.
32. Krousel-Wood MA, Thomas S, Jannu A, Muntner P, Morisky DE, Re RN. Low adherence to prescribed antihypertension medication and poorer quality of life in elderly hypertensive patients. Poster presented at: American Heart Association 2nd Scientific Conference on Compliance in Healthcare and Research, Washington DC, May 18, 2004.
34. Oâ€™Connor PJ. Overcome clinical inertia to control systolic blood pressure. Arch Intern Med. 2003;163(22):2677-2678.
adherence and health care use among adults with hypertension in an urban health care system. Pharmacotherapy. 2006;26(6):779-789.