Int J Clin Pharm (2014) 36:1048–1058 DOI 10.1007/s11096-014-9993-y
RESEARCH ARTICLE
Impact of community pharmacist intervention discussing patients’ beliefs to improve medication adherence Gina Gujral • Karl Winckel • Lisa M. Nissen W. Neil Cottrell
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Received: 1 November 2013 / Accepted: 30 July 2014 / Published online: 19 August 2014 Koninklijke Nederlandse Maatschappij ter bevordering der Pharmacie 2014
Abstract Background Adherence to evidence based medicines in patients who have experienced a myocardial infarction remains low. Individual’s beliefs towards their medicines are a strong predictor of adherence and may influence other factors that impact on adherence. Objective To investigate if community pharmacists discussing patients’ beliefs about their medicines improved medication adherence at 12 months post myocardial infarction. Setting This study included 200 patients discharged from a public teaching hospital in Queensland, Australia, following a myocardial infarction. Patients were randomised into intervention (n = 100) and control groups (n = 100) and followed for 12 months. Method All patients were interviewed between 5 to 6 weeks, at 6 and 12 months post discharge by the researcher using the repertory grid technique. This technique was used to elicit the patient’s individualised beliefs about their medicines for their myocardial infarction. In the intervention group, patients’ beliefs about their medicines were communicated by the researcher to their community pharmacist. The pharmacist used this information to tailor their discussion with the patient about their medication beliefs at designated time points (3 and 6 months post discharge). The control group
was provided with usual care. Main outcome measure The difference in non-adherence measured using a medication possession ratio between the intervention and control groups at 12 months post myocardial infarction. Results There were 137 patients remaining in the study (intervention group n = 72, control group n = 65) at 12 months. In the intervention group 29 % (n = 20) of patients were nonadherent compared to 25 % (n = 16) of patients in control group. Conclusion Discussing patients’ beliefs about their medicines for their myocardial infarction did not improve medication adherence. Further research on patients beliefs should focus on targeting non-adherent patients whose reasons for their non-adherence is driven by their medication beliefs. Keywords Adherence Australia Beliefs Community pharmacist intervention Myocardial infarction Repertory grid technique
Impact of findings on clinical practice •
G. Gujral (&) K. Winckel L. M. Nissen W. N. Cottrell School of Pharmacy, The University of Queensland, Brisbane, QLD, Australia e-mail: [email protected] K. Winckel Department of Pharmacy, Princess Alexandra Hospital, Brisbane, QLD, Australia L. M. Nissen School of Clinical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
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The repertory grid interview can be utilised to identify individual beliefs patients have towards their medicines prescribed for their myocardial infarction. The beliefs identified from the repertory grid interview have potential to be used by community pharmacists to help tailor their discussions with patients about their medicines. To increase the likelihood of success, adherence interventions should be targeted to non-adherent patients and tailored to the patient’s reason for nonadherence. Community pharmacists are ideally placed to play a leading role.
Int J Clin Pharm (2014) 36:1048–1058
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Up to 25 % of patients may be non-adherent to their medications 12 months after experiencing a myocardial infarction.
Introduction In people who have experienced a myocardial infarction (MI), adherence to the medicines recommended in evidence based guidelines [1–4], reduces morbidity and mortality [5, 6]. Despite this, adherence rates fall by 18 % at 1 month [7], 22 % at 6 months [8], and 50 % at 12 months [9]. Adherence is a complex health behaviour impacted by a number of factors. The World Health Organization (WHO) has divided these into five dimensions; the healthcare team and system, patient, therapy, condition, social and economic [10]. The extent to which each factor impacts on adherence is widely debated [11, 12]. Individuals’ beliefs are strong predictors of health behaviour including medication adherence [13, 14]. A belief is an attitude an individual has towards a proposition and the more they consider the proposition to be true, the more likely the belief will drive their behaviour [15]. Therefore, the beliefs an individual holds towards their medicines may influence; the way they interact with the healthcare team and system, their perception of the efficacy or side effects of treatment, perception of the longevity or short term nature of their condition and; their balance of the cost versus benefit of the therapy. Medication beliefs have been most commonly expressed in terms of necessity and concern domains through the use of the Beliefs about Medicines Questionnaire (BMQ) Specific. Patients with high necessity beliefs and low concern beliefs are more likely to be adherent to treatment whereas patients that have higher concern beliefs compared to necessity beliefs are more likely to be non-adherent [9, 16–18]. However these belief domains are not easily structured into interventions to improve medication adherence [14]. The belief domains in the BMQ Specific identify the patient’s necessity/concern beliefs but do not identify individual propositions and therefore beliefs that may lie outside these domains. This also holds true for other validated questionnaires determining patients beliefs about medicines [19–21] which have yet to be operationalised to improve medication adherence. A method that has been utilised in heart failure and angina to determine medication and treatment beliefs is the repertory grid technique [14, 15, 19]. The repertory grid technique is grounded in personal construct theory and was first utilised as a psychology tool in personality theory in 1955 by George Kelly [22]. Since then it has been utilised in a variety of medical and non-medical fields [18–22]. The
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repertory grid technique allows individuals to generate personal constructs that reflect propositions about a topic of interest (e.g. the individual’s medicines). The topic of interest is then rated on the proposition to develop the individual’s attitude towards the proposition and their beliefs towards the topic of interest. The beliefs generated are therefore personalised to the individual, with potential to fall outside the necessity and concern framework and overlap with the domains identified by the WHO. It is suggested that interventions targeted to adherence should be tailored and dynamic, that is individualised and change according to changes in the individual [10]. The repertory grid technique offers a novel method to obtain beliefs towards medicines in individuals who have experienced a MI and utilise individual beliefs to tailor an intervention to support medication adherence in this population.
Aim of the study The aim of this study was to investigate if community pharmacists discussing patients’ beliefs about their MI medicines improved medication adherence at 12 months.
Ethics approval Ethics approval for the study was obtained from the Human Research Ethics Committee at the Princess Alexandra Hospital and at The University of Queensland (Reference Number: 2009001447).
Methods We conducted a randomised controlled trial enrolling 200 patients diagnosed with a MI, from October 2009 to August 2010, in a large public teaching hospital in Brisbane, Australia. The primary outcome was medication nonadherence at 12 months. Secondary outcomes included medication non-adherence at 6 months and changes in adherence and medication beliefs between 6 to 12 months. Study population The study population was a convenience sample of patients admitted to the coronary care unit, cardiology ward or general medical wards with a documented diagnosis of STelevated MI or Non-ST-elevated MI. Informed consent was obtained from all participants. Participation in the study required patients to nominate and attend one community pharmacy for the study period.
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Study design Patients were randomly assigned into the pharmacy intervention or control group using block randomisation [23]. Patient’s baseline demographic details, medical history and medications on discharge were recorded. After discharge, all patients were followed for 12 months and participated in three interviews with the researcher (GG) (Fig. 1): face to face between 5 and 6 weeks; by telephone at 6 and 12 months. Medication beliefs were elicited at all three interviews using the repertory grid technique and the BMQ Specific at the 6 and 12 month interviews. Medication
Fig. 1 Outline of patient journey. The diagram outlines the patient journey through the 12 month study
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beliefs from the repertory grid technique were communicated to the community pharmacist in the intervention group who used this information to tailor their discussion with the patient at 3 and 6 months. Study tools The repertory grid technique was utilised to identify medication beliefs in all study patients. Patients were provided their medicines prescribed for their MI in groups of three and asked to compare and contrast between them to generate constructs reflecting their propositions about
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Fig. 2 Using the repertory grid interview to elicit medication beliefs. The diagram details the process of the repertory grid interview, from the patient generating constructs to using the constructs to elicit medication beliefs. Patients are shown their medicines in groups of three and asked to compare and contrast between them to generate constructs. After they have generated their constructs, the patient then rates each construct for each of their medicines, as the example with the angiotensin receptor blocker, irbesartan, is shown. By rating their constructs, the patient is eliciting their beliefs. *Generated from the two medicines that are similar; #generated from the one medicine that is different
their medicines (Fig. 2). The propositions consisted of two opposing poles of the patient’s attitude towards their medicines. This was repeated for different combinations of the patients’ medicines. A Likert scale was placed between the two poles and the patient asked to rate their medicines on each proposition to elicit their medication beliefs (Fig. 2). The use of the repertory gird technique to elicit medication beliefs is described in more detail by Cottrell et al. and Percival et al. [24, 25]. The BMQ Specific was used to monitor changes in necessity and concern beliefs between the intervention and control groups. Participants were required to rate on a five point Likert scale the degree to which they agree or disagree to the five statements of necessity and five statements of concern. Participating pharmacies Participating community pharmacists were provided with an explanation of the study by the researcher and informed if their patient was allocated to the intervention or control
group. The researcher (GG) met with each pharmacist that had patients in the intervention group and explained how the pharmacist should conduct the interviews integrating the patients’ beliefs from the repertory grid interview. The pharmacists were provided with a folder detailing the interview schedules. Pharmacists with patients in the intervention group were compensated with $75 per patient whereas pharmacists with patients in the control group were compensated with $25 per patient at the end of the study. Study intervention In the intervention group the community pharmacist reviewed the patient monthly when they collected their prescriptions, to assess if they were getting their MI medicines dispensed and whether they were experiencing any problems with their MI medicines. At 3 and 6 months, the pharmacist had a longer discussion with the patient tailored to their medication beliefs provided by the researcher from the repertory grid interview (Fig. 3a, b). The medication
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(a)
(b) Fig. 3 a and b Example of an intervention patient’s completed repertory grid and interpretation of patient’s beliefs for the community pharmacist. The community pharmacist was provided the information above and asked to discuss the patient’s medication beliefs using the patient’s words and address any concern beliefs the
patient may have. The pharmacist was provided with an example of how to discuss the information with the patient. ACE-I angiotensin converting enzyme inhibitor, ARB angiotensin receptor blocker, GTN glyceryl trinitrate, MI myocardial infarction
beliefs for the patient in Fig. 3a, suggested that they believed clopidogrel affected blood pressure and the statin thinned the blood. The pharmacist tailored their discussion with the patient to modify these beliefs as shown in Fig. 3a.
In Fig. 3b, the pharmacist focussed their discussion around the patient’s beliefs toward their aspirin and clopidogrel. The figures highlight the individualised nature of the beliefs elicited during the repertory grid interview and how
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Table 1 Baseline patient characteristics Intervention n = 100
Control n = 100
Total n = 200 (%)
Age yrs mean [SD]
58.4 [11.3]
60.4 [11.0]
59.4 [11.2]
Males
77
80
157 (78.5)
Currently employeda
46
51
97 (48.5)
Smoker
38
33
71 (35.5)
STEMI
40
34
74 (37)
Non-STEMI
60
66
126 (63)
20
38
58 (29)
Reason for admission
Past medical history Ischaemic heart diseasea CABG
a
6
18
24 (12)
Previous stenta Heart failure
6 19
21 31
27 (13.5) 50 (25)
Hypertension
57
54
111 (55.5)
Diabetes mellitus
26
30
56 (28)
Stroke/TIA
5
4
9 (4.5)
AF
3
10
13 (6.5)
Depression/anxiety
12
9
21 (10.5)
ACE-I/ARB
84
72
156 (78.4)
Beta blocker
90
89
179 (89.9)
Lipid lowering agent
99
98
197 (99)
Aspirin
98
97
195 (98)
Clopidogrel
82
77
159 (79.9)
GTN
82
75
157 (78.9)
Discharge medication
ACE angiotensin converting enzyme, ARB angiotensin receptor blocker, AF atrial fibrillation, CABG coronary artery bypass graft, GTN glyceryl trinitrate, Non-STEMI non-ST elevated myocardial infarction, STEMI ST elevated myocardial infarction, TIA transient ischemic attack Values are mean ± SD or n (%) a
Statistically significant p B 0.05
this information was communicated by the researcher to the patient’s community pharmacist and tailored to the subsequent discussion with the patient. Study control Patients in the control group did not have their medication beliefs communicated to their community pharmacist by the researcher. The community pharmacists for patients in the control group were asked to provide the patient with usual care when they collected their prescription medications. Medication adherence Medication adherence was measured in two ways. A medication possession ratio (MPR) was determined from prescriptions filled by the patient over the study period for the lipid lowering agent and ACE-I/ARB or beta-blocker (if they were not prescribed an ACE-I/ARB). A self-
reported measure the Medication Adherence Report Scale (MARS) was also utilised [26, 27]. The MPR was calculated by dividing the cumulative number of days of supply by the total number of days in the measuring interval (1 year), multiplied by 100 % [28]. Patients with a MPR of C80 % were categorised as adherent [28–30]; this has been shown to provide a reasonable balance between sensitivity (that is, the ability to correctly identify adherence) and specificity (ability to correctly identify nonadherence) [31]. To ensure the accuracy of the MPR, medication dispensing data was collected from; the patients’ elected community pharmacy, other community pharmacies where patients had prescriptions for their MI medicines dispensed and public hospital databases. Statistical analysis A sample size of 100 patients per group gave the study an 80 % power to detect a difference of 30 % non-adherence
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Data presented above for 132 patients; at 12 months there were 69 out of 72 patients in the intervention group and 64 out of 65 patients in the control group prescribed a lipid lowering agent. One patient had incomplete data and could not be included in the above analysis
-0.35 (medium) -0.56 (large) -0.47 (medium to large) -0.07 (small) 0 -0.19 (small) r value (effect size)
-0.17 (small)
-0.07 (small)
0.248
1 0
0.066 0.014
0 1
0.724 1.000
0 2
0.854
7
0.082
5
0.121
Ties
p value
1
5 4
0 3
10 6
7 1
1 2
13
21 Positive ranks
23
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Negative ranks
17
2
15 (5 and 16) 5.5 (1 and 10) 9 (5.5 and 13.5) 8 (5 and 13) 4.5 (2.25 and 4.75) 8 (4 and 12.5) 8 (4.5 and 10.5) Median BMQ differential 12 months (25th and 75th quartiles)
5 (2.75 and 9.75)
2 (-0.5 and 6.75) 6 (2 and 11.5) 6.5 (5.5 and 13.25) 4.5 (-0.75 and 7.75) 8 (2.5 and 11) 8 (3 and 10) Median BMQ differential 6 months (25th and 75th quartiles)
6.5 (-1.75 and 10.25)
Group 4: intervention (n = 4) Group 3: control (n = 13) Group 3: intervention (n = 14) Group 2: control (n = 2) Group 2: intervention (n = 6) Group 1: control (n = 41) Group 1: intervention (n = 45)
Table 2 Changes in medication beliefs from 6 to 12 months post MI
11 (1 and 16)
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Group 4: control (n = 7)
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in the control group versus 15 % in the intervention group. This was based on a significance level of 5 %. A 30 % rate of non-adherence in the control group was based on an average rate of medication non-adherence in studies 1 year post MI [9, 32–35]. Patients were categorised as non-adherent based on the MPR of the lipid lowering drug. There was a significant correlation between the MPR for the lipid lowering drug and ACE-I/ARB or beta-blocker at 6 and 12 months (r = 0.630, p \ 0.001 and r = 0.698, p \ 0.001, respectively). The correlation between the MPR and MARS was significant at 6 and 12 months (r = 0.338, p \ 0.001 and r = 0.481, p \ 0.001, respectively). A slightly higher proportion of patients were still prescribed lipid lowering drugs at 12 months (69 out of 72 in the intervention group and 64 out of 65 in the control group) compared to the ACE-I/ARB or beta blocker (69 out of 72 in the intervention group and 63 out of 65 in the control group). The frequency of non-adherent patients between the intervention and control groups was analysed using Chisquare to assess the primary and first secondary outcomes. For the secondary outcome of changes in adherence and medication beliefs between 6 and 12 months, the sample population was arranged into four groups based on changes in patients’ medication adherence during the study; Group 1 adherent at 6 and 12 months, Group 2 non-adherent at 6 and 12 months, Group 3 adherent at 6 months and nonadherent at 12 months, Group 4 non-adherent at 6 months and adherent at 12 months. Medication beliefs were measured using a BMQ Differential (necessity score–concerns score) and changes in beliefs analysed by comparing the median BMQ differential at 6 and 12 months using the Wilcoxon signed-rank test. Comparisons of demographic data were made between the intervention and control groups using parametric (age) and non-parametric tests (frequency data). The statistical analysis was conducted using IBM SPSS Statistics version 22. A two sided p value of B0.05 was considered to be statistically significant.
Results A total of 640 participants were identified in hospital as potential candidates, of which 200 were enrolled into the study (Fig. 4); 100 were randomised to the control group and 100 to the intervention group. Baseline characteristics of patients in the control group revealed a significantly higher incidence of ischaemic heart disease (p = 0.005), coronary artery bypass graft (p = 0.009), and coronary stents (p = 0.002) and these patients were more likely to be employed (p = 0.031) (Table 1). At 12 months, there were 137 patients remaining in the study; 72 in intervention group
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Fig. 4 Patient flow diagram at 12 months. The diagram details the number of patients from recruitment to analysis; TSI Torres Strait Islander
and 65 in the control group (Fig. 4). Patients in the control group were significantly more likely to have attended a cardiac rehabilitation program (63 %) compared to the intervention group (46 %) by 12 months (p = 0.043). In the primary outcome, there was no significant difference between the number of patients categorised as nonadherent in the intervention compared to the control group [n = 20 (29 %) vs. n = 16 (25 %), p = 0.605, respectively] as measured by the MPR of the lipid lowering agent (\80 %). For the secondary outcome, there was no significant difference in the number of patients categorised as non-adherent between the intervention and control groups at 6 months [n = 11 (15 %) vs. n = 10 (15 %), p = 0.932, respectively]. Changes in medication beliefs measured using the median BMQ differential are shown in Table 2. There was a significant increase in the BMQ differential in control
group patient’s categorised as adherent at 6 months and non-adherent at 12 months (group 3) and a non-significant increase observed in intervention patients who were nonadherent at 6 months and adherent at 12 months (group 4). There were no significant changes in the BMQ differential scores in the remaining groups.
Discussion In a randomised controlled trial, community pharmacists discussing medication beliefs with patients did not improve medication adherence at 6 or 12 months after a myocardial infarction. A small number of patients’ adherent at 6 months and non-adherent at 12 months had a significant increase in their necessity beliefs, reflected in their BMQ differential score. Patients whose medication adherence did
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not change over the 12 month study period did not have a change in beliefs. In our study we introduced a tailored intervention whereby medication beliefs for individual patients were identified and if incongruous these were discussed with the patient by their pharmacist at two time points. In previous studies, interventions to improve medication adherence delivered on a number of occasions e.g. monthly pharmacist contact or telephone follow up have improved adherence [36, 37]. In these studies adherence data was used in an ‘adherence feedback’ loop to the healthcare professional delivering the intervention. In our intervention however, although pharmacists were provided the patients’ medication beliefs we did not provide adherence information to the pharmacists. The lack of impact on adherence in our study reflects similar findings in other studies in cardiovascular disease where the intervention was delivered to all patients irrespective of their adherence or reasons for nonadherence. In a meta-analysis by Cutrona et al. [36], such ‘broad’ interventions had an overall small or limited effect size on adherence. However, interventions targeted to patients who were non-adherent and tailored to their reasons for non-adherence have improved medication adherence [36, 38]. Although our intervention was ‘tailored’ to the patient’s individual medication beliefs, we did not identify their reasons for non-adherence and thus our intervention was not tailored in this regard. We used a novel method to explore patient’s beliefs towards their medicines, the repertory grid technique. This approach has been utilised previously in patients with heart failure [24, 25] and angina [39] to generate beliefs towards medicines and treatment. Questions that may arise when reviewing some of the individual constructs generated by patients with this technique is that; does it allow beliefs to be generated and is there an overlap with knowledge? Beliefs are assumed truths and can be considered declarative statements that say something about an individual’s world that can be true or false [15]. This reflects the constructs and the rating of these in the repertory grid technique. An example in Fig. 3a illustrates this: the patient had a belief that clopidogrel was for blood pressure. Changing this belief is important because if they felt this to be true, it may mean they decide to make a decision to stop their clopidogrel if they become dizzy. There is undoubtedly an overlap between beliefs generated by the repertory grid technique and knowledge. This should be no surprise as one common analysis of knowledge is that it is a ‘‘justified true belief’’. So for an individual to be said to have knowledge three things must hold: the proposition is true; the individual believes the proposition is true; and the individual is justified in believing the proposition is true. Therefore only in some instances would the beliefs generated by the repertory grid technique be considered
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‘justified true beliefs’. The strength of the repertory grid technique is that it allows patients to generate beliefs that are of importance to them and are not based on researcher generated constructs. The repertory grid technique also provides opportunity to identify individual beliefs that may fall outside the necessity-concerns framework which would also explain why there was limited change in the BMQ differential observed in our study. There are limitations to our study which also explain why it was not successful in improving medication adherence in this population. The intervention, although identifying beliefs and tailored in the sense of identifying individual beliefs, was not tailored to those patients whose beliefs were driving their non-adherence. Further we targeted a sample population who had experienced a MI, whereas targeting non-adherent patients in this population may have produced a different result. Finally the control group were followed up every month by the same community pharmacy and more of this group had attended a cardiac rehabilitation program. Cardiac rehabilitation programs are reported to improve adherence [35] and continual follow-up by the same community pharmacy may have changed the patients behaviour compared to if they had filled their prescriptions at different community pharmacies over the 12 month period.
Conclusion An intervention tailored to identifying and discussing individual patient’s beliefs was not successful in improving medication adherence at 12 months in patients who had experienced a MI. Future studies to address patients’ beliefs and medication adherence should target nonadherent patients whose reasons for their non-adherence is driven by their medication beliefs. Acknowledgments We would like to thank all study participants and community pharmacists involved in this research for giving up their time to take part in the study. Funding This work was supported by the Pharmacy Board of Queensland Research Grants Program 2008, Brisbane, Queensland. The Pharmacy Board had no input in the research design, methodology or results. The ideas expressed in this manuscript are those of the authors and are not intended to represent the position of the Board or members of the Board. Conflicts of interest
There are no conflicts of interest.
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RESEARCH ARTICLE
Impact of community pharmacist intervention discussing patients’ beliefs to improve medication adherence Gina Gujral • Karl Winckel • Lisa M. Nissen W. Neil Cottrell
•
Received: 1 November 2013 / Accepted: 30 July 2014 / Published online: 19 August 2014 Koninklijke Nederlandse Maatschappij ter bevordering der Pharmacie 2014
Abstract Background Adherence to evidence based medicines in patients who have experienced a myocardial infarction remains low. Individual’s beliefs towards their medicines are a strong predictor of adherence and may influence other factors that impact on adherence. Objective To investigate if community pharmacists discussing patients’ beliefs about their medicines improved medication adherence at 12 months post myocardial infarction. Setting This study included 200 patients discharged from a public teaching hospital in Queensland, Australia, following a myocardial infarction. Patients were randomised into intervention (n = 100) and control groups (n = 100) and followed for 12 months. Method All patients were interviewed between 5 to 6 weeks, at 6 and 12 months post discharge by the researcher using the repertory grid technique. This technique was used to elicit the patient’s individualised beliefs about their medicines for their myocardial infarction. In the intervention group, patients’ beliefs about their medicines were communicated by the researcher to their community pharmacist. The pharmacist used this information to tailor their discussion with the patient about their medication beliefs at designated time points (3 and 6 months post discharge). The control group
was provided with usual care. Main outcome measure The difference in non-adherence measured using a medication possession ratio between the intervention and control groups at 12 months post myocardial infarction. Results There were 137 patients remaining in the study (intervention group n = 72, control group n = 65) at 12 months. In the intervention group 29 % (n = 20) of patients were nonadherent compared to 25 % (n = 16) of patients in control group. Conclusion Discussing patients’ beliefs about their medicines for their myocardial infarction did not improve medication adherence. Further research on patients beliefs should focus on targeting non-adherent patients whose reasons for their non-adherence is driven by their medication beliefs. Keywords Adherence Australia Beliefs Community pharmacist intervention Myocardial infarction Repertory grid technique
Impact of findings on clinical practice •
G. Gujral (&) K. Winckel L. M. Nissen W. N. Cottrell School of Pharmacy, The University of Queensland, Brisbane, QLD, Australia e-mail: [email protected] K. Winckel Department of Pharmacy, Princess Alexandra Hospital, Brisbane, QLD, Australia L. M. Nissen School of Clinical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
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The repertory grid interview can be utilised to identify individual beliefs patients have towards their medicines prescribed for their myocardial infarction. The beliefs identified from the repertory grid interview have potential to be used by community pharmacists to help tailor their discussions with patients about their medicines. To increase the likelihood of success, adherence interventions should be targeted to non-adherent patients and tailored to the patient’s reason for nonadherence. Community pharmacists are ideally placed to play a leading role.
Int J Clin Pharm (2014) 36:1048–1058
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Up to 25 % of patients may be non-adherent to their medications 12 months after experiencing a myocardial infarction.
Introduction In people who have experienced a myocardial infarction (MI), adherence to the medicines recommended in evidence based guidelines [1–4], reduces morbidity and mortality [5, 6]. Despite this, adherence rates fall by 18 % at 1 month [7], 22 % at 6 months [8], and 50 % at 12 months [9]. Adherence is a complex health behaviour impacted by a number of factors. The World Health Organization (WHO) has divided these into five dimensions; the healthcare team and system, patient, therapy, condition, social and economic [10]. The extent to which each factor impacts on adherence is widely debated [11, 12]. Individuals’ beliefs are strong predictors of health behaviour including medication adherence [13, 14]. A belief is an attitude an individual has towards a proposition and the more they consider the proposition to be true, the more likely the belief will drive their behaviour [15]. Therefore, the beliefs an individual holds towards their medicines may influence; the way they interact with the healthcare team and system, their perception of the efficacy or side effects of treatment, perception of the longevity or short term nature of their condition and; their balance of the cost versus benefit of the therapy. Medication beliefs have been most commonly expressed in terms of necessity and concern domains through the use of the Beliefs about Medicines Questionnaire (BMQ) Specific. Patients with high necessity beliefs and low concern beliefs are more likely to be adherent to treatment whereas patients that have higher concern beliefs compared to necessity beliefs are more likely to be non-adherent [9, 16–18]. However these belief domains are not easily structured into interventions to improve medication adherence [14]. The belief domains in the BMQ Specific identify the patient’s necessity/concern beliefs but do not identify individual propositions and therefore beliefs that may lie outside these domains. This also holds true for other validated questionnaires determining patients beliefs about medicines [19–21] which have yet to be operationalised to improve medication adherence. A method that has been utilised in heart failure and angina to determine medication and treatment beliefs is the repertory grid technique [14, 15, 19]. The repertory grid technique is grounded in personal construct theory and was first utilised as a psychology tool in personality theory in 1955 by George Kelly [22]. Since then it has been utilised in a variety of medical and non-medical fields [18–22]. The
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repertory grid technique allows individuals to generate personal constructs that reflect propositions about a topic of interest (e.g. the individual’s medicines). The topic of interest is then rated on the proposition to develop the individual’s attitude towards the proposition and their beliefs towards the topic of interest. The beliefs generated are therefore personalised to the individual, with potential to fall outside the necessity and concern framework and overlap with the domains identified by the WHO. It is suggested that interventions targeted to adherence should be tailored and dynamic, that is individualised and change according to changes in the individual [10]. The repertory grid technique offers a novel method to obtain beliefs towards medicines in individuals who have experienced a MI and utilise individual beliefs to tailor an intervention to support medication adherence in this population.
Aim of the study The aim of this study was to investigate if community pharmacists discussing patients’ beliefs about their MI medicines improved medication adherence at 12 months.
Ethics approval Ethics approval for the study was obtained from the Human Research Ethics Committee at the Princess Alexandra Hospital and at The University of Queensland (Reference Number: 2009001447).
Methods We conducted a randomised controlled trial enrolling 200 patients diagnosed with a MI, from October 2009 to August 2010, in a large public teaching hospital in Brisbane, Australia. The primary outcome was medication nonadherence at 12 months. Secondary outcomes included medication non-adherence at 6 months and changes in adherence and medication beliefs between 6 to 12 months. Study population The study population was a convenience sample of patients admitted to the coronary care unit, cardiology ward or general medical wards with a documented diagnosis of STelevated MI or Non-ST-elevated MI. Informed consent was obtained from all participants. Participation in the study required patients to nominate and attend one community pharmacy for the study period.
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Study design Patients were randomly assigned into the pharmacy intervention or control group using block randomisation [23]. Patient’s baseline demographic details, medical history and medications on discharge were recorded. After discharge, all patients were followed for 12 months and participated in three interviews with the researcher (GG) (Fig. 1): face to face between 5 and 6 weeks; by telephone at 6 and 12 months. Medication beliefs were elicited at all three interviews using the repertory grid technique and the BMQ Specific at the 6 and 12 month interviews. Medication
Fig. 1 Outline of patient journey. The diagram outlines the patient journey through the 12 month study
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beliefs from the repertory grid technique were communicated to the community pharmacist in the intervention group who used this information to tailor their discussion with the patient at 3 and 6 months. Study tools The repertory grid technique was utilised to identify medication beliefs in all study patients. Patients were provided their medicines prescribed for their MI in groups of three and asked to compare and contrast between them to generate constructs reflecting their propositions about
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Fig. 2 Using the repertory grid interview to elicit medication beliefs. The diagram details the process of the repertory grid interview, from the patient generating constructs to using the constructs to elicit medication beliefs. Patients are shown their medicines in groups of three and asked to compare and contrast between them to generate constructs. After they have generated their constructs, the patient then rates each construct for each of their medicines, as the example with the angiotensin receptor blocker, irbesartan, is shown. By rating their constructs, the patient is eliciting their beliefs. *Generated from the two medicines that are similar; #generated from the one medicine that is different
their medicines (Fig. 2). The propositions consisted of two opposing poles of the patient’s attitude towards their medicines. This was repeated for different combinations of the patients’ medicines. A Likert scale was placed between the two poles and the patient asked to rate their medicines on each proposition to elicit their medication beliefs (Fig. 2). The use of the repertory gird technique to elicit medication beliefs is described in more detail by Cottrell et al. and Percival et al. [24, 25]. The BMQ Specific was used to monitor changes in necessity and concern beliefs between the intervention and control groups. Participants were required to rate on a five point Likert scale the degree to which they agree or disagree to the five statements of necessity and five statements of concern. Participating pharmacies Participating community pharmacists were provided with an explanation of the study by the researcher and informed if their patient was allocated to the intervention or control
group. The researcher (GG) met with each pharmacist that had patients in the intervention group and explained how the pharmacist should conduct the interviews integrating the patients’ beliefs from the repertory grid interview. The pharmacists were provided with a folder detailing the interview schedules. Pharmacists with patients in the intervention group were compensated with $75 per patient whereas pharmacists with patients in the control group were compensated with $25 per patient at the end of the study. Study intervention In the intervention group the community pharmacist reviewed the patient monthly when they collected their prescriptions, to assess if they were getting their MI medicines dispensed and whether they were experiencing any problems with their MI medicines. At 3 and 6 months, the pharmacist had a longer discussion with the patient tailored to their medication beliefs provided by the researcher from the repertory grid interview (Fig. 3a, b). The medication
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(a)
(b) Fig. 3 a and b Example of an intervention patient’s completed repertory grid and interpretation of patient’s beliefs for the community pharmacist. The community pharmacist was provided the information above and asked to discuss the patient’s medication beliefs using the patient’s words and address any concern beliefs the
patient may have. The pharmacist was provided with an example of how to discuss the information with the patient. ACE-I angiotensin converting enzyme inhibitor, ARB angiotensin receptor blocker, GTN glyceryl trinitrate, MI myocardial infarction
beliefs for the patient in Fig. 3a, suggested that they believed clopidogrel affected blood pressure and the statin thinned the blood. The pharmacist tailored their discussion with the patient to modify these beliefs as shown in Fig. 3a.
In Fig. 3b, the pharmacist focussed their discussion around the patient’s beliefs toward their aspirin and clopidogrel. The figures highlight the individualised nature of the beliefs elicited during the repertory grid interview and how
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Table 1 Baseline patient characteristics Intervention n = 100
Control n = 100
Total n = 200 (%)
Age yrs mean [SD]
58.4 [11.3]
60.4 [11.0]
59.4 [11.2]
Males
77
80
157 (78.5)
Currently employeda
46
51
97 (48.5)
Smoker
38
33
71 (35.5)
STEMI
40
34
74 (37)
Non-STEMI
60
66
126 (63)
20
38
58 (29)
Reason for admission
Past medical history Ischaemic heart diseasea CABG
a
6
18
24 (12)
Previous stenta Heart failure
6 19
21 31
27 (13.5) 50 (25)
Hypertension
57
54
111 (55.5)
Diabetes mellitus
26
30
56 (28)
Stroke/TIA
5
4
9 (4.5)
AF
3
10
13 (6.5)
Depression/anxiety
12
9
21 (10.5)
ACE-I/ARB
84
72
156 (78.4)
Beta blocker
90
89
179 (89.9)
Lipid lowering agent
99
98
197 (99)
Aspirin
98
97
195 (98)
Clopidogrel
82
77
159 (79.9)
GTN
82
75
157 (78.9)
Discharge medication
ACE angiotensin converting enzyme, ARB angiotensin receptor blocker, AF atrial fibrillation, CABG coronary artery bypass graft, GTN glyceryl trinitrate, Non-STEMI non-ST elevated myocardial infarction, STEMI ST elevated myocardial infarction, TIA transient ischemic attack Values are mean ± SD or n (%) a
Statistically significant p B 0.05
this information was communicated by the researcher to the patient’s community pharmacist and tailored to the subsequent discussion with the patient. Study control Patients in the control group did not have their medication beliefs communicated to their community pharmacist by the researcher. The community pharmacists for patients in the control group were asked to provide the patient with usual care when they collected their prescription medications. Medication adherence Medication adherence was measured in two ways. A medication possession ratio (MPR) was determined from prescriptions filled by the patient over the study period for the lipid lowering agent and ACE-I/ARB or beta-blocker (if they were not prescribed an ACE-I/ARB). A self-
reported measure the Medication Adherence Report Scale (MARS) was also utilised [26, 27]. The MPR was calculated by dividing the cumulative number of days of supply by the total number of days in the measuring interval (1 year), multiplied by 100 % [28]. Patients with a MPR of C80 % were categorised as adherent [28–30]; this has been shown to provide a reasonable balance between sensitivity (that is, the ability to correctly identify adherence) and specificity (ability to correctly identify nonadherence) [31]. To ensure the accuracy of the MPR, medication dispensing data was collected from; the patients’ elected community pharmacy, other community pharmacies where patients had prescriptions for their MI medicines dispensed and public hospital databases. Statistical analysis A sample size of 100 patients per group gave the study an 80 % power to detect a difference of 30 % non-adherence
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Data presented above for 132 patients; at 12 months there were 69 out of 72 patients in the intervention group and 64 out of 65 patients in the control group prescribed a lipid lowering agent. One patient had incomplete data and could not be included in the above analysis
-0.35 (medium) -0.56 (large) -0.47 (medium to large) -0.07 (small) 0 -0.19 (small) r value (effect size)
-0.17 (small)
-0.07 (small)
0.248
1 0
0.066 0.014
0 1
0.724 1.000
0 2
0.854
7
0.082
5
0.121
Ties
p value
1
5 4
0 3
10 6
7 1
1 2
13
21 Positive ranks
23
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Negative ranks
17
2
15 (5 and 16) 5.5 (1 and 10) 9 (5.5 and 13.5) 8 (5 and 13) 4.5 (2.25 and 4.75) 8 (4 and 12.5) 8 (4.5 and 10.5) Median BMQ differential 12 months (25th and 75th quartiles)
5 (2.75 and 9.75)
2 (-0.5 and 6.75) 6 (2 and 11.5) 6.5 (5.5 and 13.25) 4.5 (-0.75 and 7.75) 8 (2.5 and 11) 8 (3 and 10) Median BMQ differential 6 months (25th and 75th quartiles)
6.5 (-1.75 and 10.25)
Group 4: intervention (n = 4) Group 3: control (n = 13) Group 3: intervention (n = 14) Group 2: control (n = 2) Group 2: intervention (n = 6) Group 1: control (n = 41) Group 1: intervention (n = 45)
Table 2 Changes in medication beliefs from 6 to 12 months post MI
11 (1 and 16)
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Group 4: control (n = 7)
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in the control group versus 15 % in the intervention group. This was based on a significance level of 5 %. A 30 % rate of non-adherence in the control group was based on an average rate of medication non-adherence in studies 1 year post MI [9, 32–35]. Patients were categorised as non-adherent based on the MPR of the lipid lowering drug. There was a significant correlation between the MPR for the lipid lowering drug and ACE-I/ARB or beta-blocker at 6 and 12 months (r = 0.630, p \ 0.001 and r = 0.698, p \ 0.001, respectively). The correlation between the MPR and MARS was significant at 6 and 12 months (r = 0.338, p \ 0.001 and r = 0.481, p \ 0.001, respectively). A slightly higher proportion of patients were still prescribed lipid lowering drugs at 12 months (69 out of 72 in the intervention group and 64 out of 65 in the control group) compared to the ACE-I/ARB or beta blocker (69 out of 72 in the intervention group and 63 out of 65 in the control group). The frequency of non-adherent patients between the intervention and control groups was analysed using Chisquare to assess the primary and first secondary outcomes. For the secondary outcome of changes in adherence and medication beliefs between 6 and 12 months, the sample population was arranged into four groups based on changes in patients’ medication adherence during the study; Group 1 adherent at 6 and 12 months, Group 2 non-adherent at 6 and 12 months, Group 3 adherent at 6 months and nonadherent at 12 months, Group 4 non-adherent at 6 months and adherent at 12 months. Medication beliefs were measured using a BMQ Differential (necessity score–concerns score) and changes in beliefs analysed by comparing the median BMQ differential at 6 and 12 months using the Wilcoxon signed-rank test. Comparisons of demographic data were made between the intervention and control groups using parametric (age) and non-parametric tests (frequency data). The statistical analysis was conducted using IBM SPSS Statistics version 22. A two sided p value of B0.05 was considered to be statistically significant.
Results A total of 640 participants were identified in hospital as potential candidates, of which 200 were enrolled into the study (Fig. 4); 100 were randomised to the control group and 100 to the intervention group. Baseline characteristics of patients in the control group revealed a significantly higher incidence of ischaemic heart disease (p = 0.005), coronary artery bypass graft (p = 0.009), and coronary stents (p = 0.002) and these patients were more likely to be employed (p = 0.031) (Table 1). At 12 months, there were 137 patients remaining in the study; 72 in intervention group
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Fig. 4 Patient flow diagram at 12 months. The diagram details the number of patients from recruitment to analysis; TSI Torres Strait Islander
and 65 in the control group (Fig. 4). Patients in the control group were significantly more likely to have attended a cardiac rehabilitation program (63 %) compared to the intervention group (46 %) by 12 months (p = 0.043). In the primary outcome, there was no significant difference between the number of patients categorised as nonadherent in the intervention compared to the control group [n = 20 (29 %) vs. n = 16 (25 %), p = 0.605, respectively] as measured by the MPR of the lipid lowering agent (\80 %). For the secondary outcome, there was no significant difference in the number of patients categorised as non-adherent between the intervention and control groups at 6 months [n = 11 (15 %) vs. n = 10 (15 %), p = 0.932, respectively]. Changes in medication beliefs measured using the median BMQ differential are shown in Table 2. There was a significant increase in the BMQ differential in control
group patient’s categorised as adherent at 6 months and non-adherent at 12 months (group 3) and a non-significant increase observed in intervention patients who were nonadherent at 6 months and adherent at 12 months (group 4). There were no significant changes in the BMQ differential scores in the remaining groups.
Discussion In a randomised controlled trial, community pharmacists discussing medication beliefs with patients did not improve medication adherence at 6 or 12 months after a myocardial infarction. A small number of patients’ adherent at 6 months and non-adherent at 12 months had a significant increase in their necessity beliefs, reflected in their BMQ differential score. Patients whose medication adherence did
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not change over the 12 month study period did not have a change in beliefs. In our study we introduced a tailored intervention whereby medication beliefs for individual patients were identified and if incongruous these were discussed with the patient by their pharmacist at two time points. In previous studies, interventions to improve medication adherence delivered on a number of occasions e.g. monthly pharmacist contact or telephone follow up have improved adherence [36, 37]. In these studies adherence data was used in an ‘adherence feedback’ loop to the healthcare professional delivering the intervention. In our intervention however, although pharmacists were provided the patients’ medication beliefs we did not provide adherence information to the pharmacists. The lack of impact on adherence in our study reflects similar findings in other studies in cardiovascular disease where the intervention was delivered to all patients irrespective of their adherence or reasons for nonadherence. In a meta-analysis by Cutrona et al. [36], such ‘broad’ interventions had an overall small or limited effect size on adherence. However, interventions targeted to patients who were non-adherent and tailored to their reasons for non-adherence have improved medication adherence [36, 38]. Although our intervention was ‘tailored’ to the patient’s individual medication beliefs, we did not identify their reasons for non-adherence and thus our intervention was not tailored in this regard. We used a novel method to explore patient’s beliefs towards their medicines, the repertory grid technique. This approach has been utilised previously in patients with heart failure [24, 25] and angina [39] to generate beliefs towards medicines and treatment. Questions that may arise when reviewing some of the individual constructs generated by patients with this technique is that; does it allow beliefs to be generated and is there an overlap with knowledge? Beliefs are assumed truths and can be considered declarative statements that say something about an individual’s world that can be true or false [15]. This reflects the constructs and the rating of these in the repertory grid technique. An example in Fig. 3a illustrates this: the patient had a belief that clopidogrel was for blood pressure. Changing this belief is important because if they felt this to be true, it may mean they decide to make a decision to stop their clopidogrel if they become dizzy. There is undoubtedly an overlap between beliefs generated by the repertory grid technique and knowledge. This should be no surprise as one common analysis of knowledge is that it is a ‘‘justified true belief’’. So for an individual to be said to have knowledge three things must hold: the proposition is true; the individual believes the proposition is true; and the individual is justified in believing the proposition is true. Therefore only in some instances would the beliefs generated by the repertory grid technique be considered
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‘justified true beliefs’. The strength of the repertory grid technique is that it allows patients to generate beliefs that are of importance to them and are not based on researcher generated constructs. The repertory grid technique also provides opportunity to identify individual beliefs that may fall outside the necessity-concerns framework which would also explain why there was limited change in the BMQ differential observed in our study. There are limitations to our study which also explain why it was not successful in improving medication adherence in this population. The intervention, although identifying beliefs and tailored in the sense of identifying individual beliefs, was not tailored to those patients whose beliefs were driving their non-adherence. Further we targeted a sample population who had experienced a MI, whereas targeting non-adherent patients in this population may have produced a different result. Finally the control group were followed up every month by the same community pharmacy and more of this group had attended a cardiac rehabilitation program. Cardiac rehabilitation programs are reported to improve adherence [35] and continual follow-up by the same community pharmacy may have changed the patients behaviour compared to if they had filled their prescriptions at different community pharmacies over the 12 month period.
Conclusion An intervention tailored to identifying and discussing individual patient’s beliefs was not successful in improving medication adherence at 12 months in patients who had experienced a MI. Future studies to address patients’ beliefs and medication adherence should target nonadherent patients whose reasons for their non-adherence is driven by their medication beliefs. Acknowledgments We would like to thank all study participants and community pharmacists involved in this research for giving up their time to take part in the study. Funding This work was supported by the Pharmacy Board of Queensland Research Grants Program 2008, Brisbane, Queensland. The Pharmacy Board had no input in the research design, methodology or results. The ideas expressed in this manuscript are those of the authors and are not intended to represent the position of the Board or members of the Board. Conflicts of interest
There are no conflicts of interest.
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