Barriers to and Facilitators of Medication Adherence
Published Online: October 10, 2013
Sharon J. Rolnick, PhD, MPH; Steve Asche, MA; Pamala A. Pawloski, PharmD; Richard J. Bruzek, PharmD; and Brita Hedblom, BS
Patients’ failure to adhere to recommended therapy regimens results in serious negative medical and financial sequelae.1 Recognizing generally suboptimal adherence rates, several studies have examined barriers to medication taking. Most often these reports are condition specific. Toh and colleagues2 identified complex regimens as problematic for adherence in patients with chronic heart failure. Patients ran out of medication, resulting in adverse medical consequences. Toh and colleagues proposed education, counseling, and reduced dosing frequency to improve adherence. Burley,3 working with transplant patients, offered strategies to improve adherence such as giving simple instructions to patients and family members and reiterating the importance of medications. Solomon and colleagues4 conducted a study to systematically assess medication use for osteoporosis, assessing barriers to treatment, specific untreated patient populations, and proven methods to increase treatment rates. They found less-than-optimal adherence, no consistent predictors of undertreatment, and limited approaches to quality improvement and interventions. Currently, no welldefined mechanism exists to identify or improve nonadherence to medication regimens within or across disease states.
Leadership within a large integrated healthcare system with a well-educated patient population was interested in identifying factors that serve as barriers to and facilitators of adherence among patients taking medications for chronic conditions. Existing rates of adherence were suboptimal, despite pharmacy being a covered benefit for most patients and the existence of readily available pharmacy services. All clinics owned by the medical group have in-clinic pharmacies, and patients are able to order medications 24 hours a day through the mail order service supported by online and call-in options. To better understand adherence, we conducted a 2-part study. In the first phase of the study, we examined adherence across 8 chronic medical conditions. In the second phase, we selected the 2 conditions with the lowest adherence rates, asthma/chronic obstructive pulmonary disease (COPD) (32% adherence) and diabetes (51%), and surveyed adherent and nonadherent patients regarding barriers to and facilitators of adherence. We report the results of the survey component of the study.
Phase 1: Study Population, Data Collection, and Identifying Adherence
Study Population. The study was conducted within a large Midwestern integrated health system serving more than 800,000 patients and included all patients 18 years or older with at least 1 of 8 medical conditions. Patients were identified using the health system’s electronic medical records and administrative databases. The selected conditions represented the most prevalent conditions treated and included those with both low- and high-cost medications. These conditions also included disease states where most care is delivered through either primary or specialty care. The 8 conditions were asthma/COPD, cancer, depression, diabetes, hypercholesterolemia, hypertension, multiple sclerosis, and osteoporosis. Patients were required to have a 12-month (365 + 15 days) record of prescription coverage and a minimum of 2 prescription fills for the medication used to treat 1 of the above-mentioned conditions.
Data Collection. Data on medication fills were obtained from January 2007 through March 2009; however, each individual’s adherence for each medication was tracked for 1 year (+ 15 days) using the most recent prescription fill information. In tracking adherence for 1 year, we allowed a grace period of 15 days, recognizing that some patients are not exact to the day in obtaining medication. We did not want to classify those with small extensions in getting refills as nonadherent. Data were linked on diagnoses for a given individual to medications associated with those diagnoses to ensure that the prescriptions corresponded to the conditions. Prescription fills were required to be at least a 28-day supply to eliminate any that might have been intended for an acute situation. We also required 2 fills and examined fills at the front and back end of the 1-year window to ensurechronicity in usage. We recognized that those who stopped medication after a short duration would be excluded but chose to focus on adherence patterns in patients attempting to take medications chronically. Primary or secondary diagnoses for any of the 8 diseases of interest were identifi ed using International Classification of Diseases, Ninth Revision codes. Prescription order data were obtained using generic product identifi er codes (Master Drug Data Base v2.0, Medi-Span, Indianapolis, Indiana) for 128 medications used to treat the conditions enumerated.
Calculation of Medication Adherence. Adherence was calculated using the medication possession ratio (MPR) and a cut-point of 80% for each medication.5-9 The number of days of study participation was determined by subtracting the first fill date from the last fill date within the 12-month (+ 15 day) study period for each included patient.
Binary indicators of adherence utilized an MPR of 0.80 or higher. If the MPR was lower than 0.80, the patient was considered nonadherent. Medication adherence was calculated individually for each patient for each medication and for each disease of interest. Patients on more than 1 medication for a single disease were evaluated for adherence to each individual medication and deemed nonadherent to their regimen if they did not achieve the 80% MPR for any of the prescribed medications for that condition. From the 8 conditions of interest, we identified the 2 conditions with the lowest adherence rates: diabetes (adherence 51%) and asthma/COPD (32%). These 2 conditions became the focus for the survey of adherence barriers and facilitators.
For both conditions, we calculated adherence using pharmacy claims data that we considered robust. For asthma patients we included medications used on a scheduled basis because we could not track medications used on an as-needed basis. We examined data on patients with diabetes both including and excluding those who take insulin solely for treatment of their diabetes. We also ran analyses on these patients using an adjusted MPR, as has been done by others.10 The percentage of adherent patients did not vary a great deal regardless of approach. In the end, we included all diabetes patients in our study.
Phase 2: The Survey
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