Examining Medication Adherence in Older Women ...

Journal of Women & Aging, 22:157–170, 2010 Copyright © Taylor & Francis Group, LLC ISSN: 0895-2841 print/1540-7322 online DOI: 10.1080/08952841.2010.495547

Examining Medication Adherence in Older Women with Coronary Heart Disease DONNA WEST School of Pharmacy, The University of Mississippi, University, MS

LEANNE LEFLER College of Nursing, University of Arkansas for Medical Sciences, Little Rock, AR

Downloaded By: [West, Donna] At: 20:38 19 July 2010

AMY FRANKS College of Pharmacy, University of Arkansas for Medical Sciences, Little Rock, AR

The purposes of this study were to examine medication adherence in older women with coronary heart disease and to identify barriers and facilitators of medication adherence. Methods: The study used a semistructured interview guide and established measures to examine medication taking 3 months after hospital discharge. Results: Thirty-two women completed the study: 65.6% were adherent to medications, but others were less adherent and self-modified their therapy. Over half (52.1%) suffered side effects, 71.9% had experienced psychological barriers, and all had economic barriers. Facilitators included a pillbox system (85%) and discharge medication counseling (90%). Conclusion: Tailored interventions to improve adherence in older women are needed. KEYWORDS medication medication management

adherence,

tailored

interventions,

Coronary heart disease (CHD) is the leading cause of death among older women in the U.S., accounting for almost one in three deaths, and costing $142 billion annually (American Heart Association [AHA], 2009). For women with CHD, the risk of myocardial infarction (MI), stroke, heart failure, or other serious complications is substantial (AHA, 2009). Numerous Address correspondence to Donna West, Associate Professor, School of Pharmacy, The University of Mississippi, Faser Hall, Room 225, University, MS 38677. E-mail: dswest@ olemiss.edu 157


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clinical trials have demonstrated the efficacy of cardiovascular (CV) medications in reducing morbidity and mortality in CHD patients (ALLHAT, 2002; Chobanian et al., 2003; Expert Panel on Detection and Treatment of High Blood Cholesterol in Adults, 2001; LIPID, 1998). Nonadherence to CV medications is associated with poor prognosis, rehospitalizations, and mortality (Col, Fanale, & Kronholm, 1990; Gallager, Viscoli, & Horwitz, 1993; Jackevicius, Li, & Tu, 2008; Sokol, McGuigan, Verbrugge, & Epstein, 2005; Wei et al., 2002). The rate of nonadherence to medication approaches 50% (Avorn et al., 1998), and women are 18–43% more likely to be nonadherent or discontinue taking their medications compared to men (Eagle et al., 2004; Ellis et al., 2004; Schultz, O’Donnell, McDonough, Sasane, & Meyer, 2005). Additionally, women more often have lower socioeconomic status and less health insurance than men (Agency for Healthcare Research and Quality, 2009). Economic issues are thought to contribute to nonadherence; yet free medication programs have not resolved the problem (Jackevicius, Mamdani, & Tu, 2002). Clearly, we must have a better understanding of the problem of medication nonadherence in women, if CHD morbidity and mortality rates are to improve. Previous studies have attempted to address this multifactorial problem from a single perspective, though in reality the issues are interrelated and complex. Additionally, medication persistence has often been examined from a single-drug-class perspective, but women with CHD are attempting to manage a multiple-medication regimen. Therefore, this study comprehensively explored older women’s medication-taking experiences and barriers and facilitators to medication adherence after hospital discharge. The study focused on adherence following hospital discharge because this may be a particularly challenging time for women (Chapman et al., 2005; Ellis et al., 2004; Ho et al., 2006). We adapted Murray and colleagues’ framework for studying medication adherence in the elderly (Murray et al., 2004) to explore how older women’s medication-taking experience was related to their adherence. Their framework is based on a health-care utilization model with predisposing, enabling, and need variables. We have focused on the enabling variables related to income, insurance, support, and medication information provided by health professionals and the need variable related to response to therapy. The response to therapy consists of the therapeutic and biophysiological response to therapy and the psychosocial issues related to medication use. It has been suggested that patients will take, discontinue, or alter therapy to minimize the side effects and psychosocial and economic barriers and to maximize the positive therapeutic response. Patients are in a trade-off between biophysiological, psychosocial, and economic barriers and the positive therapeutic effects of taking medications (Murawski & Bentley, 2001).

Medication Adherence in Older Women

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METHODS


Design and Participants We used a computer-assisted semistructured telephone interview (CATI) technique that included open narratives for additional information to elicit a holistic description of older women’s medication-taking experience. The study was approved by the Institutional Review Board (IRB) of a large university medical center and was conducted in compliance with the IRB’s requirements. Older women with a diagnosis of CHD (ICD/9 410-414, 429.2) who were admitted to the university hospital were recruited for the study. Women were eligible if they were at least 62 years old at the time of enrollment, had access to a working telephone, were medically stable, and were able to read a prescription label, understand the nature of the study, and provide informed consent. Women were excluded if they had a diagnosis of dementia or could not speak English. Potential participants were identified by an attending clinician using discharge ICD/9 codes via the hospital computer system. After the patient gave permission to be contacted by the research team, a member of the research team contacted the medical team to ensure the patient was stable and then approached the patient for informed consent.

Data Collection A medical abstraction form developed by the authors was used to collect comorbid conditions, admitting and discharge diagnoses, CHD-related diagnostic tests and treatments, new and ongoing medications at discharge, and CHD risk factors. Data were collected from the participant’s medical record and scanned for direct entry into REMARK© optical mark recognition software (2007 Gravic, Inc., Malvern, PA), which easily interfaces with programs for data analysis. At 3 months (+/− 2 weeks) postdischarge, a trained research assistant conducted a telephone interview with each participant using a computerassisted telephone interview (CATI) guide system. The semistructured interview was designed to elicit the medication-taking experiences of older women with CHD. We chose 3 months postdischarge because the greatest decline in medication persistence is usually seen within the first few months of therapy, making this an important time period to study (Benner et al., 2002; Chapman et al., 2005; Eagle et al., 2004; Ho et al., 2006). The instrument contained multiple-choice, short-answer, and open narrative response items and combined portions of several well-known instruments to address the multifactorial nature of medication adherence for older women with CHD. The interview took approximately 1 hour to complete, and upon completion, study participants were mailed a $30 gift card in appreciation of their time.

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The patient characteristics and enabling resources examined included race, income, education level, family and social support, and medical and prescription insurance information. Additionally, questions about Medicare Part D were included, since there is little empirical data on patients’ perceptions of Medicare Part D as an enabling resource. One item from the National Health Interview Survey was included to measure self-rated health status from poor to excellent on a 5-point scale. This question has been tested on 1129 subjects with chronic disease and has a test–retest reliability coefficient of 0.92 (Lorig et al., 1996). Several questions pertained to the participant’s perceived need for medication information. Specifically, participants were asked whether they had received discharge counseling, whether they had requested additional medication information from health-care providers, and whether they needed additional medication information at the time of the interview. MEDICATION

ADHERENCE

Medication adherence was measured using the general adherence scale of the Medical Outcomes Study. This consists of five items using a 6-point Likert-type scale (1–6); internal consistency reliability approaches 0.80, and 2-year stability is r = 0.41 (Hays et al., 1994; Sherbourne, Hays, Ordway, DiMatteo, & Kravitz, 1992). The scale is scored by averaging together responses to the five items after reverse scoring two items. We considered participants with scores 1–4 to be nonadherent. A score of 5–6 indicated that the participant was adherent, taking her medications most of the time. This is similar to other studies allowing people who take medications 80% of the time or more to be considered adherent (Chapman et al., 2005; Ellis et al., 2004). RESPONSE

TO THERAPY AND BARRIERS

The regimen screen from the Brief Medication Questionnaire, developed by Svarstad and colleagues (Redman, 2003; Svarstad, Chewning, Sleath, & Claesson, 1999), was used to obtain a list of the medications the participant was taking at 3 months postdischarge. The regimen screen asks open-ended questions about medications taken, how they are taken (e.g., skipped doses, times per day), therapeutic response, and side effects experienced. The survey also includes questions about the beneficial effects of therapy. If a participant’s medication list did not match the discharge medication list, the research assistant inquired about each medication not discussed and recorded the participant’s responses. Barriers were categorized as biophysiological, economic, or psychosocial barriers. Biophysiological barriers are associated with side effects,


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and economic barriers are associated with the cost of taking medications. Psychosocial barriers included scheduling barriers, psychological barriers, and knowledge of and understanding barriers, as shown in Table 3. A list of potential barriers to medication adherence was developed based on factors from the literature that influence a person’s pharmaceutical-related quality of life (Catz, Kelly, Bogart, Benotsch, & McAuliffe, 2000; Murawski & Bentley, 2001; Simpson, Johnson, Farris, & Tsuyuki, 2002). Participants were asked whether they had encountered each of the 26 barriers to taking their CV medication during the past 3 months. A list of facilitators to medication adherence was also included, including medication discharge counseling, use of pillboxes or other scheduling devices, family and friend support, and prescription assistance programs. Additionally, open-ended questions and probing questions were included. Two questions pertaining to participants’ perspectives on participating in a telephone interview were also included to determine the feasibility of future CATI studies.

Data Analysis To conduct the quantitative analyses, SPSS 15.0 (Chicago, IL) was used. Frequencies and descriptive statistics were calculated for demographic variables, self-rated health, medication adherence, and perceived facilitators and barriers. For hypothesis-generation, we conducted chi-square analyses to evaluate relationships between medication adherence (the adherent group vs. the nonadherent group) and perceived barriers, including physiological, psychosocial, and economic barriers. We evaluated the relationship between adherence and perceived information need. An alpha level of 0.20 was used to indicate significance for the hypothesis-generating analyses because the investigators were most interested in trends identified by this pilot study to provide insight into significant barriers for future study. Content analysis of narrative data was performed as described by Morse and Field (1995), then participants’ narrative data were compared using constant comparison technique described by Speziale and Carpenter (2003). Themes were formed from the data on facilitators and barriers (psychological, side effects, etc.) and explicated in data tables, using quotes from participants.

RESULTS Participant Characteristics Thirty-seven patients agreed to participate, and 32 completed data collection. Three patients died after consent and 3 months postdischarge, and 2 withdrew from the study citing illness as the reason. Of the 32 completing the study, 59.4% were Caucasian, 75% were widowed or single, 21.9% earned less than $10,000 annually, and 84.4% earned less than $30,000.


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CHD was the primary diagnosis for all women in the study. All but one had a diagnosis of CHD prior to the most recent hospitalization. Nine (28.1%) of the participants had a coronary stent and 3 received a stent during this hospitalization. Seventeen (52.1%) had undergone coronary artery bypass surgery, with 4 undergoing surgery during this hospitalization. The mean number of risk factors for CHD documented in participants’ medical records was 4.34 (± 2.5): 81.3% with hypertension; 65.6%, diabetes; 59.4%, congestive heart failure; 56.3%, pulmonary disease; 46.9%, hyperlipidemia; 31.3%, thyroid disease; 31.3%, chronic indigestion; 28.1%, arthritis; 28.1%, renal disease; and 25%, cancer. No significant differences in baseline characteristics or other study variables were found between Caucasians and other races. The mean number of medications the women were taking at discharge was 10.72 (± 3.14) (6 to 19 medications with a range of 13), and the mean number of CV medications was 5.97 (± 1.56; 3 to 9 medications; range 6). The most common medications taken included angiotensin receptor blockers or angiotensin converting enzyme inhibitors, beta-blockers, diuretics, cholesterol-lowering medications, gastrointestinal medications, diabetes medications, and anxiety medications. Participants were also taking other medications, such as antiplatelets, electrolyte replacements, anticoagulants, nitrates, calcium channel blockers, thyroid medications, sleep medications, antidepressants, pain relievers, and hormones. At 3 months postdischarge, 25 (78.1%) of the participants indicated that their health was fair or poor. They were taking 9.88 (SD = 2.76; range from 5 to 17) medications on average, including 5.19 (SD = 2.01; range from 2 to 9) for CV problems. Nineteen (59.4%) had at least one change in their medications in the 3 months following discharge. Of these 19, 52.6% had experienced at least one switch from one drug to another within the same therapeutic class, and 63.2% had at least one medication discontinued.

Enabling Variables—Facilitators to Adherence When asked whether they had received medication information, 28 (87.6%) said medication counseling was provided by nurses and/or physicians at hospital discharge and by the pharmacist at their pharmacy. Eleven (34.4%) had called a physician, nurse, or pharmacist to ask questions about their medications since being discharged. Half wished they had access to a physician, nurse, or pharmacist to call about their drug therapy now (n = 16; 50.0%) and would have liked a 15-minute consultation about their drug therapy at some time after discharge (n = 17; 53.1%). When asked about receiving a consultation about their drug therapy, the majority (n = 12; 70.6%) indicated that they would prefer it via telephone. Participants listed several facilitators that helped improve medication adherence. Twenty-six (81.3%) had a pillbox system to help them, and 11


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(34.4%) had a family member or friend helping them daily with their medications. One participant said “My daughter put the pills into the pillbox and divided them up into Sunday through Saturday, and morning, noon, and evening.” Other participants commented on the importance of taking them at the “same time each day to help me remember to take my medications.” Prescription insurance and prescription assistance plans were perceived as facilitators by participants. When queried about strategies used to decrease costs associated with medications, participants included the use of generics, $4.00 prescription plans, manufacturer assistance programs, and free samples. Eighteen (56%) of the participants had Medicare Part D prescription medication coverage. Of these 18, 83% said their experience had been good or excellent with Medicare Part D, and 66.7% said it had lowered the cost of obtaining medications. Three said they had to switch medications because of their Medicare Part D plan formulary.

Response to Therapy and Barriers to Adherence When asked about their medications, almost all (n = 30, 93.8%) indicated that the medications made them feel better. However, 18 (56.3%) thought that they were taking at least one drug that was not beneficial. Participant statements on the perceived benefits of their medications are shown in Table 1. Participants were also asked if they had ever experienced side effects for their drugs. Over half (52.1%) said they experienced a side effect; however, often they did not know which drug was causing the side effects. Statements from the participants about side effects are given in Table 2. Participants noted a range from 2 to 13 barriers, with a mean of 7.1 barriers. Table 3 provides a list of these barriers. The number of barriers experienced was negatively correlated with medication adherence scores (r = −.350; p = .05). Most of the participants experienced side effects, but only 5 (15.6%) indicated that the side effects were bothersome and that they had altered their medication because of side effects. Psychosocial barriers included scheduling, psychological, and knowledge barriers. Knowledge and understanding barriers were the most common, with 26 (81.3%) indicating some type of knowledge barrier. Participants said that it was difficult to remember the names of CV medications, and it was easy to get confused when medications or doses were changed. Psychological barriers were also common, with 23 (71.9%) agreeing that they worried or were concerned about taking their medications. Fourteen (43.8%) also indicated some scheduling-type barrier; the most common was carrying CV medications outside the home. Another barrier was cost. All of the participants had difficulty affording their medications. Most (n = 23; 71.9%) indicated that they had had to give something up to buy their medications, and approximately a fifth indicated that they had not gotten needed medications filled because of cost.

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TABLE 1 Sample Statements From Participants About the Benefits of Their Cardiovascular Medications “I can breathe better. I don’t have the tightness in my chest.” “Yeah, it makes me feel better. Cause my heart feels like it stays really tired, like it is run down and I can hardly breathe. When it is time to take my heart medicine it lightens up on me. I get really, really tired.” “I breathe better and my heart doesn’t feel like it is racing. The medication keeps my heart calmed down.” “I don’t have the tightness in my chest like I used to. I am able to breathe better. I have not had to use my oxygen in about 2 weeks.”


“I know I have to have them to make my heart going right. Maybe they do make me feel better. They keep telling me to take the Plavix to keep the blood flowing. I guess it makes me feel better cause it’s necessary to live.”

TABLE 2 Sample Statements From Participants About the Side Effects of Cardiovascular Medications “Whatever side effect I have I don’t know which ones causes them. Because I take them [medications] all in the morning.” “I have headaches and stay dizzy all the time. The doctor doesn’t seem to understand what I mean. They look at you like I am crazy or something.” “There is not a way to answer this. There are so many [side effects] I don’t know. I just don’t know which one is interacting with another one.” “I am not sure. I’m having side effects from something but I’m not sure. I have had this before but I don’t remember which med I quit taking that causes it.” “Just frequent trips to the bathroom. I don’t mind if I am home but if you are in the car and can’t get there.”

Medication Adherence Participants’ mean adherence score was 5.18 (SD = 0.93); 65.6% scored 5 or greater and thus were categorized as adherent to medication therapy. Using chi-square we compared women who were adherent to those not adherent to identify barriers that were significant in determining medication adherence. The relationship between physiological barriers and medication adherence was statistically significant (p = .019). That is, participants who indicated that side effects were bothersome were less adherent to their medication therapy. There was no significant relationship between psychosocial barriers and adherence, in part because psychosocial barriers were common in both the adherent and nonadherent groups. Chi-square analysis was also used to determine whether perceived information needs were related to medication adherence. Participants who were less adherent were more likely to want to discuss medication issues


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TABLE 3 Barriers to Medication Adherence Yes N (%) Physiological Barriers (Side Effects)

Side effects of heart medications (meds) bother you Side effects of heart meds make you feel worse Altered how take heart meds because of how they make you feel

4 (13%)

Difficult to remember to take heart meds all of the time Spend lots of time planning to take heart meds Changed ways to take heart meds to make it easier Have to do something special to carry heart meds Medication containers difficult to carry with you

2 (6.3%)

1 (3%) 1 (3%)

Psychosocial Barriers


Scheduling Barriers

Psychological Barriers

Knowledge Barriers

Cost Barriers

Worry about what heart meds may do to you Worry about taking several heart meds together Worry that heart meds are controlling you Taking heart meds every day frustrates you Worry when dose of heart med changes Worry about missing a dose or late dose Taking meds reminds you of heart disease Trouble remembering names of heart meds Trouble remembering what heart meds are used for Get confused when pills looks alike Get confused when different Dr./nurse changes meds Instructions on taking heart meds too hard to understand Getting heart meds filled difficult Hard to pay for heart meds Need heart meds but do not get due to cost Changed way take heart meds because it costs too much Have to give something up because of cost of meds Trouble getting refills for heart meds

1 (3%) 2 (6.3%) 12 (37.5%) 0 (0%) 7 (21.9%) 15 (46.9%) 7 (21.9%) 8 (25%) 10 (31.3%) 1 (3.1%) 19 (59.4%) 18 (56.3%) 10 (31.3%) 12 (37.5%) 14 (43.8%) 2 (6.3%) 4 (12.5%) 10 (31.3%) 7 (21.9%) 1 (3.1%) 23 (71.9%) 1 (3.1%)

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with a health-care professional (p = .009) and to want more information about medications (p = .05). However, participants who were adherent were as likely as those who were nonadherent to want to receive a 15-minute medication consultation.


DISCUSSION The study sample consisted of older women with low incomes and in poor health, a vulnerable population for medication-adherence issues. Approximately one third reported that they were not adherent to their CV medications and altered how they took their medications, suggesting that improvements are needed in medication adherence of older women with CHD. Other researchers have found similar nonadherence among women with CHD (Eagle et al., 2004; Ellis et al., 2004; Schultz et al., 2005). Because CV disease is primarily a condition of the elderly and is costly, medical professionals need to provide resources to enable older women to take medications as directed (Jessup et al., 2009). Response to therapy in this sample included the positive and negative effects of the medications. The participants experienced multiple, yet different, medication barriers. Almost all had experienced a side effect of a drug; as it became increasingly bothersome, the participants adhered to their medications less. Thus, providers should monitor for side effects and communicate with patients to identify side effects, provide counseling, and find alternative strategies if indicated. Many of these participants also experienced psychosocial barriers, which could be overcome through counseling and education. Interestingly, multiple medication barriers were experienced by most participants, both adherent and not-adherent women, and it was difficult to distinguish between the groups because the barriers were so prevalent. Participants in this study were taking multiple medications that often had changed in therapeutic class or dosage during the 3-month period postdischarge. Their medication regimens were not static and required the participants to continually relearn their medication regimens. Because of the number of medications and changes to medications during the first 3 months postdischarge, it is not surprising that participants experienced knowledge barriers and medication nonadherence. Similarly, Ho and colleagues (2006) found that medication therapy discontinuation was common after discharge and concluded that there is a need for an intervention to help patients transition from the hospital to the outpatient setting. Our results also provide evidence that a postdischarge intervention is warranted. Smith and colleagues (2008) and Daughtery and colleagues (2008) recently showed that continued contact with MI patients after discharge can improve medication adherence.



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Information from health-care providers is a potential enabler of medication adherence, and the participants in this study indicated a need for more information or for consultation with a health-care professional. Lee, Grace, and Taylor (2006) found that a pharmacy care program, which involved a pharmacist-provided education component and follow-up, improved medication adherence and most importantly, clinical outcomes. The participants in our study would also have benefited from a consultation with a pharmacist or nurse to review their medication regimens to ensure appropriateness, safety, and cost-effectiveness. Within Medicare Part D, there are provisions to offer medication therapy management (MTM) services for patients with complex and costly medication regimes (Centers for Medicare and Medicaid Services, 2008). MTM services encompass a broad range of professional clinical activities, including monitoring response to therapy and performing an annual comprehensive medication review. MTM is usually provided to patients who are taking multiple medications and have high prescription drug costs; however, these activities are not regularly scheduled, and the best timing and sequence of MTM services for these patients have not been established. Our study provides evidence that 1–3 months after hospital discharge is an appropriate time for pharmacists or nurse practitioners to provide MTM services. An education intervention during postdischarge may help older women transition to the outpatient setting and improve their medication use. This study found that patients experience multiple yet different medication barriers. Thus, an intervention to improve adherence will need to be tailored to the patient’s medication-taking experience and should be age, culture, gender, and medication specific. Similarly, KrouselWood, Hyre, Muntner, and Morisky (2005) report that because multiple individualized factors potentially affect patient adherence, no single intervention has emerged as superior. These researchers recommend tailoring an intervention to overcome patient-specific barriers to medication adherence. The small sample limits the generalizability of these findings. There may also be a hidden selection bias or a Hawthorne effect with participants being more adherent to their medications because of study enrollment. Since CHD is a chronic condition, a longer period of study would also be preferred. Finally, the instrument’s validity and reliability need further evaluation. Other factors, including health literacy, cognitive impairment, and other personal characteristics, may also influence medication adherence but were not measured in this study. Despite these limitations, the results of this study help us better understand the barriers to adherence among older women with CHD. Medication adherence is more likely to occur if health-care professionals choose strategies to address the identified barriers (Shaw et al., 2005). The next step is to

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develop and test tailored strategies for health-care professionals to use when such barriers are identified.

ACKNOWLEDGMENTS The investigators acknowledge Teresa West, RN, who recruited and interviewed patients in this study, and Dr. Jean McSweeney, who provided much guidance. The study was funded by the Tailored Bio-behavioral Intervention Research Center, which is funded by a grant (P20-211G1-11444-04-03) from the National Institute of Nursing Research.


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