Discussion
In this large cohort of patients with high-risk diabetes across four geographically diverse counties of the Southeastern USA, the agreement between self-reported medication adherence and directly observed counts of pills, insulin pens, and medication bottles was only fair. Several possible conclusions may be drawn.
First, patients' perceptions of their medication-taking behavior may be inaccurate, confounded by the myriad of medications for which dosing instructions vary with life events, such as food intake, activity, or time of day. In this study, for example, 61% (n=261) of patients reported consistently taking medications as prescribed. Yet, of those, 31 people were actually considered non-adherent based on direct observation. Conversely, 40% (n=169) of patients reported poor adherence. Yet, of those, 110 people were doing better than they thought and were assessed as adherent by an NP. For many, this misperception of good versus poor adherence may simply be due to the high proportion of medications that are prescribed to be taken ‘as needed’ or in sliding scale doses. In previously reported studies, discrepancies in self-reported adherence have been attributed to the complexity of the medication regimen20 ,21 and the inherent difficulty in recognizing what adherence is.
Another interpretation of the results may be that in high-risk patients with T2DM, the MMAS may lack sensitivity and may be a poor indicator of actual medication use. Previous work with the MMAS in other populations has shown a strong correlation between self-reported adherence and actual adherence. In hypertension, for example, the MMAS has demonstrated a strong correlation between self-reported adherence and subsequent blood pressure control.7 ,12 Though early studies support the MMAS as being sensitive to actual changes in medication adherence, more recent studies have disputed these findings.11 ,22 ,23
In diabetes, numerous studies have evaluated self-reported medication adherence using the MMAS, some showing a positive relationship between adherence and HbA1c.24 ,25 In many of these studies, however, participant demographics differed widely from those in SEDI, with fewer comorbid illnesses, higher rates of insurance coverage, more frequent single-dose regimens, and higher levels of education and health literacy.16 In SEDI, factors that classified patients as high risk included recent hospitalizations, substance use, tobacco use, and multiple comorbidities—including coronary artery disease, hypertension, heart failure, or chronic kidney disease—all of which require complex medication regimens. As a result, patients may report that they are ‘getting enough medications’ daily, skewing self-reported results26 and suggesting that improvement in diagnostic measures is needed, particularly in illnesses with multiple comorbidities.
A second finding of this study was low discrimination. The c-statistics for both measures were similar, 0.63 for self-reported MMAS and 0.61 for direct observation. Though higher adherence scores were significantly associated with lower HbA1c values for both measures, neither was able to discriminate well between lower and higher levels of serum glucose control as indicated by HbA1c. Similar findings have been previously reported.11 ,16 One possibility is that other factors, beyond medication usage, are driving A1c in this high-risk population.
Regardless of the underlying reasons for lack of agreement between self-report and observed counts, every effort must be made to discover where, in this high-risk population, the breakdown occurs. Despite only fair agreement between self-report and observed medication counts, using the MMAS as a screening tool could improve identification of those highest risk patients in need of follow-up. The MMAS offers a quick survey that can be used in inpatient and outpatient as well as individual and group settings, and is useful for identifying those who need further evaluation and support for medication management. The individual reasons for non-adherence were not included as part of the analysis, as our focus was to evaluate and objectively report the agreement between patient-reported adherence and the observed assessment of a healthcare provider who was visualizing and counting bottles, vials, and pills. Though reasons for medication-taking behavior are critical in an intervention, this analysis was designed to provide evidence for the use of preintervention baseline data for determining the value of patients' self-reported medication adherence. Using these results substantiates the value of a baseline adherence assessment, and opens a forum for patients and providers to then earnestly consider reasons for various medication-taking behaviors and take these into account as part of the subsequent plan of care.
Limitations to this study include the following: a relatively long intake visit and placement of patient-reported outcome surveys, including the MMAS, at the end of the visit when patients may have been fatigued could affect results; patients took the self-reported survey while the healthcare member was present, which may have imposed an inadvertent Hawthorne effect; and a relatively high proportion of the cohort was unable to self-administer the survey due to blindness or very low health literacy. Though NPs read the survey for patients in the latter category, the results may have been skewed.