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Abstract

Background:

The aim of this study was to investigate the proportion of patients who have suboptimal adherence to oral anticoagulant (OAC), identify the predictors of adherence, and determine whether patient-related factors vary across adherence levels in Australia.

Methods:

Respondents were recruited for an online survey using Facebook. Survey instruments included the Morisky Medication Adherence Scale, the Anticoagulation Knowledge Tool, the Perception of Anticoagulant Treatment Questionnaires, and a modified Cancer Information Overload scale. Predictors of medication adherence were identified using ordinal regression analysis.

Results:

Of the 386 responses eligible for analysis, only 54.9% reported a high level of adherence. Participants aged 65 years or younger were less likely to have high adherence compared to older participants (odds ratio [OR], 0.54; 95% confidence interval [CI], 0.33-0.88; P = .013), while females were more likely to be highly adherent compared to males (OR, 1.69; 95% CI, 1.08-2.64; P = .023). The analyses showed that age, gender, treatment satisfaction, information overload, concerns about making mistake when taking OACs, and cost of medication were significant predictors of adherence.

Conclusion:

Self-reported suboptimal adherence to OAC is common among patients with atrial fibrillation. A focus on supporting people who are at higher risk of suboptimal adherence is needed to maximize the benefit of OAC therapy in this population.

Keywords

adherence oral anticoagulant atrial fibrillation heart disease

Introduction

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice and is responsible for 20% to 30% of all strokes.¹ Oral anticoagulant (OAC) therapy is highly effective for stroke prevention in patients with AF.² Evidence from clinical trials demonstrates that both warfarin³ and the direct oral anticoagulants (DOACs)⁴ reduce the risk of stroke by 64% to 70%.⁵ However, optimal adherence is essential to ensure both efficacy and safety with OAC therapy.⁶ Studies have reported that more than 40% of patients with AF suboptimally adhere to OAC therapy.^7,8 Patients who adhere to OAC therapy are less likely to have stroke or major bleeding compared to those who do not.^9,10 As a result, experts have recently recommended a structured follow-up program for all anticoagulated patients with AF to improve adherence and persistence to OAC therapy.¹¹

Various studies have investigated adherence issues surrounding warfarin usage.^7,9 Warfarin use is challenging in clinical practice due to the requirement for routine blood monitoring and numerous food and drug interactions¹²; these factors often impose lifestyle changes on the patient.^12,13 They can lead to suboptimal adherence, instability of anticoagulation control, and discontinuation of therapy.¹³ Consequently, poor adherence to warfarin therapy has been associated with increases in the rate of both bleeding and embolic events.⁹ Although international normalized ratio monitoring can be useful in identifying warfarin patients with suboptimal adherence, identifying DOAC patients with suboptimal adherence is more challenging in the absence of a similar test.¹⁴ Additionally, compared to warfarin, it has been suggested that even a short period of medication suboptimal adherence to DOACs could result in a catastrophic loss of clinical effect.¹²

Patient-related factors such as knowledge,¹⁵ health information overload,¹⁶ treatment convenience, and satisfaction¹⁷ have been investigated in patients with cardiovascular diseases. However, little is known about variation in patient-related factors with regard to adherence in patients with AF. Consequently, we aimed to (1) estimate the proportion of patients with suboptimal adherence to OAC and identify predictors of adherence and (2) determine whether patient-related factors vary across levels of adherence in patients with AF.

Methods

This study was a secondary analysis of an Australian OAC knowledge survey in patients with AF, in which respondents were recruited online (via LimeSurvey) using Facebook.¹⁸ The survey requested sociodemographic characteristics including age, gender, level of education, annual income, and type of employment. The survey also requested the type of OAC respondents were taking and the duration of therapy.

Survey Instruments and Scoring

Survey instruments used included the Morisky Medication Adherence Scale (MMAS-8)^19
–21 to assess levels of adherence (low, medium, and high); the Anticoagulation Knowledge Tool (AKT) to assess OAC knowledge²²; the Perception of Anticoagulant Treatment Questionnaires (PACT-Q1 and PACT-Q2) to assess treatment expectations, convenience, and satisfaction^23,24; and the modified Cancer Information Overload (CIO) scale to assess health information overload.²⁵ The CIO scale was modified by replacing the word “cancer” with “atrial fibrillation” to suit patients with AF.

The MMAS-8 consists of 8 items and was scored according to the information in the original reports.^19
–21 The MMAS-8 scoring ranges from 0 to 8 and categorizes adherence into 3 levels. A score of “<6” is categorized as low adherence, “6 to <8” as medium adherence, and “8” as high adherence. The MMAS-8 covers questions related to both intentional and unintentional nonadherence. The AKT consists of 2 parts with 20 general items and 8 items related to vitamin K antagonists (VKAs), respectively. In addition to the general questions, participants taking warfarin are also required to answer the VKA-specific items. The scoring of the AKT ranges from 0 to 35. Final knowledge scores were presented as percentages in accordance with the original report.²² The PACT-Q1 scale assessing treatment expectation comprises 7 items on a 5-point Likert scale (1 = not at all, 5 = extremely) and has no global score; as such, each item was scored individually.^23,24 The PACT-Q2 assessing treatment convenience and satisfaction consists of 20 items on a 5-point Likert scale. Global convenience and satisfaction scores were calculated by summing and rescaling the responses on a 0 to 100 scale according to the procedure in the original reports.^23,24 The CIO scale was scored by summing the responses to all 8 items, of which a maximum score of “32” can be obtained as in the original report.²⁵

Recruitment and Sample Size

The survey targeted Australian residents with AF who were taking an OAC. A paid advertisement on Facebook targeted adults older than 18 years. In Australia, the prevalence of AF is 1% to 2%.²⁶ We calculated that a sample of 384 participants would be representative of the estimated 460 000 patients with AF, considering a 5% margin of error and a 95% confidence interval (CI). In Australia, there has been a steady increase in the prescription of DOACs since their introduction in 2011,^27
–29 and a recent study suggested that around 30% of patients with AF taking OAC are currently prescribed warfarin therapy.²⁷ Therefore, we also hypothesized that about 30% of respondents should report taking warfarin therapy in order for the survey to be representative of the Australian population with AF. Screening questions were included in the survey to exclude respondents who did not have a diagnosis of AF or were not currently taking an OAC. Incomplete surveys were excluded from the analysis.

Statistical Analyses

Analyses were conducted using SPSS version 23 (IBM, Armonk, New York). As study tools were administered simultaneously, no causal association was assumed between study variables and adherence. Continuous variables were reported as means and standard deviations, while categorical variables were reported as proportions. An analysis of variance was used to compare patient-related variables across the levels of adherence, while predictors of medication adherence were identified using ordinal regression analyses. Ordinal regression analysis was chosen to model the 3 levels of adherence simultaneously, while considering the ordered relationship (low, medium, and high) between them.³⁰ The proportional odds model was used, which assumes that the odds ratio (OR) is the same for all categories of the response variable.³¹ Appropriateness of this model was further confirmed using the test of parallel lines.³¹ Variables with a P value of less than .1 in the univariate analyses were considered eligible for the multivariate analysis, and a P value of less than .05 was considered statistically significant for all analyses.

Ethics and Consent

The Tasmanian Social Science Human Research Ethics Committee (reference number H0015972) granted ethical approval for the research. Consent was implied by submission of the survey.

Results

There were a total of 924 respondents to the survey, and 386 (41.8%) participants reported taking an OAC for AF and completed the survey. Forty-six respondents (5%) taking an OAC for AF were excluded from the analyses due to missing data. Of the 386 eligible respondents, 69.1% were aged 65 years or older and 74.1% reported taking a DOAC. A total of 212 (54.9%) respondents reported high adherence (Table 1). There were statistically significant associations between current employment and gender with adherence. Respondents who were not working had better adherence compared to those currently employed, while females were more adherent than males. Of note, females had a significantly higher score on 2 items on the MMAS-8 measuring unintentional nonadherence: “Do you ever forget to take your medicine?” (0.81 vs 0.71; P = .04) and “How often do you have difficulty remembering to take your medicine?” (0.93 vs 0.89; P = .02). No significant difference in adherence was observed between patients taking warfarin and DOACs.

Table 1.

Demographic and Clinical Characteristics.

	Low Adherence (MMAS < 6) (n = 54), n (%)	Medium Adherence (MMAS = 6-<8) (n = 120), n (%)	High Adherence (MMAS = 8) (n = 212), n (%)	P Value
Age ≥65	26 (48.2)	75 (62.5)	166 (78.3)	<.000
Gender
Female	28 (51.9)	81 (67.5)	154 (72.6)	.014
Male	26 (48.1)	39 (32.5)	58 (27.4)	.014
Education^a
Year 12 and below	27 (50.0)	53 (44.2)	113 (53.3)	.108
Greater than year 12	27 (50.0)	63 (52.5)	98 (48.7)
Missing	0 (0.0)	4 (3.3)	1 (1.3)
Employment
Not currently working	36 (66.7)	79 (65.8)	170 (80.2)	.007
Currently working	18 (33.3)	41 (34.2)	42 (19.8)	.007
SEIFA
Decile 1-5	26 (48.1)	62 (52.1)	105 (49.8)	.870
Decile 6-10	28 (51.9)	57 (47.9)	106 (50.2)	.870
Duration of therapy
<2 years	23 (42.6)	52 (43.3)	74 (34.9)	.257
>2 years	31 (57.4)	68 (56.7)	138 (65.1)	.257
Type of oral anticoagulant
Warfarin	15 (27.8)	25 (20.8)	60 (28.3)	.620
Apixaban	15 (27.8)	39 (32.5)	67 (31.6)	.620
Rivaroxaban	17 (31.5)	45 (37.5)	61 (28.8)
Dabigatran	7 (13.0)	11 (9.2)	24 (11.3)

Note: Use of the ©MMAS is protected by US and International copyright laws. Permission for use is required. A license agreement is available from Donald E. Morisky, MMAS Research (MORISKY), 14725 NE 20th St Bellevue WA 98007, USA; dmorisky@gmail.com

Abbreviation: MMAS, Morisky Medication Adherence Scale; SEIFA, socioeconomic indexes for areas.

^a ^“Missing” was excluded from the analysis.

Participants in the high adherence group reported the highest mean OAC knowledge score. Compared with the low adherence group, treatment satisfaction scores were greater in the medium (6.8; 95% CI, 2.2-11.5) and high adherence groups (13.6; 95% CI, 9.6-17.7). Similarly, treatment convenience score was greater in both the high and medium adherence groups than the low adherence group. Participants who reported high and medium adherence had a lower information overload score than the low adherence group (Table 2).

Table 2.

Association Between Adherence Levels and Patient-Related Factors.

Parameter	Low Adherence (MMAS < 6) (n = 54)	Medium Adherence (MMAS = 6-<8) (n = 120)	High Adherence (MMAS = 8) (n = 212)
Knowledge Mean (SD)	66.3 (13.8)	66.2 (13.7)	69.0 (14.0)
Mean difference (95% CI)^a	NA	0.0 (−4.5 to 4.4)	2.7 (−1.5 to 6.9)
Convenience Mean (SD)	83.7 (11.4)	85.9 (12.7)	91.0 (7.5)
Mean difference (95% CI)^a	NA	2.2 (−1.8 to 6.2)	7.4 (4.1 to 10.7)
Satisfaction Mean (SD)	58.9 (15.7)	65.8 (13.9)	72.6 (13.0)
Mean difference (95% CI)^a	NA	6.8 (2.2 to 11.5)	13.6 (9.6 to 17.7)
Information overload Mean (SD)	18.1 (4.2)	17.7 (3.6)	16.0 (3.9)
Mean difference (95% CI)^a	NA	−0.3 (−1.6 to 0.9)	−2.1 (−3.3 to −0.9)

Abbreviations: CI, confidence interval; MMAS, Morisky Medication Adherence Scale; NA, not applicable; SD, standard deviation.

^aMean difference compared with the low adherence group.

The ordinal regression analysis showed that age, gender, treatment satisfaction, information overload, concerns about making mistake when taking OACs, and cost of medication were significant predictors of adherence (Table 3). The model indicated that females were almost twice as likely to be adherent as males (OR, 1.7; 95% CI, 1.1-2.6; P = .02), while patients aged 65 years or younger were less likely to be adherent to OAC than older patients (OR, 0.5; 95% CI, 0.3-0.9; P = .01). The predictors included in the multivariate model explained 29.8% of the variation in adherence (Nagelkerke R ² = 0.298; χ² = 113.76, P < .001).

Table 3.

Result of the Multiple POM Using Adherence Level as 3 Ordered Response Categories.^a

Characteristics	Odds Ratio (95% CI)	P Value
Age (≤65 years)	0.54 (0.33-0.88)	.013
Gender (female)	1.69 (1.08-2.63)	.023
Employment (not working)	1.41 (0.85-2.36)	.187
Knowledge score	1.01 (1.00-1.03)	.180
Satisfaction score	1.04 (1.02-1.06)	<.001
Convenience score	1.00 (0.98-1.02)	.954
Information overload score	0.94 (0.89-1.00)	.048
A1. How confident are you that your anticoagulant treatment will prevent blood clots?	1.11 (0.15-1.47)	.497
A2. Do you expect that your anticoagulant treatment will relieve some of the symptoms you experience?	1.06 (0.89-1.27)	.521
A5. How concerned are you about making mistakes when taking your anticoagulant treatment?	0.69 (0.57-0.83)	<.001
A6. How important is it for you to take care of your anticoagulant treatment by yourself?	1.11 (0.90-1.38)	.334
A7. How concerned are you about how much you may have to pay for your anticoagulant treatment?	0.84 (0.72-0.97)	.020

Abbreviations: CI, confidence interval; POM, proportional odds model.

^aNagelkerke R ² = 29.8%.

Discussion

Approximately 50% of respondents reported suboptimal adherence (low or medium adherence) to OACs in this study, which highlights the need to support OAC adherence among Australian patients with AF. Specifically, patients who are aged ≤65 years, dissatisfied with their therapy, with higher perception of information overload, and concerned about making mistakes with taking OAC or the cost of their medications should be targeted for support and education. Adherence to OAC remains a challenge in clinical practice as numerous studies have reported suboptimal adherence in patients with AF.^7,8,10 Castellucci et al reported that 43% of DOAC users and 44% of warfarin users displayed poor adherence to OAC therapy.⁸ Similarly, Davis et al reported that 50% of patients taking OAC were poorly adherent to their therapy.⁷ The result of the present study is therefore consistent with those of previous studies.

The present study has some limitations. The use of self-reported adherence has been associated with overestimated adherence levels³²; however, the MMAS-8 has been validated in several populations.^33

–36 Recruiting online may have also led to the inclusion of patients not adequately representative of the broader population of patients with AF. Although studies have found demographic and baseline characteristics to be comparable in respondents recruited online and face-to-face, online respondents tend to be significantly younger.³⁷ As a result, this study may have focused more on younger patients with AF. The present study also did not capture comorbidities of participants, as this could potentially influence medication adherence. Lastly, a number of participants with AF taking OAC were excluded from the final analyses due to incomplete responses. Demographic and clinical characteristics of excluded participants were, however, similar to those included in the analyses. Despite the potential limitations, our study is strengthened by the inclusion of participants taking warfarin and DOACs, therefore capturing responses from participants taking a spectrum of OAC medications used in clinical practice. Additionally, given the large sample size and the recruitment of participants nationally, our findings may be applicable to a wider population of Australian adults with AF.

The relationship between age and adherence has been investigated in patients with cardiovascular diseases. Although different study designs and age-group classifications have been employed, the available evidence suggests that younger patients are more likely to display suboptimal adherence to prescribed medication.³⁸ This is consistent with our finding that patients aged 65 years and younger were more likely to display suboptimal adherence to OAC. The reason for better adherence in older patients could be related to the presence of multiple comorbidities, thereby making older patients more concerned about their health, leading to better medication-taking behavior.³⁸ Since suboptimal adherence to OAC is a significant concern in AF management, more emphasis should be placed on younger patients, as they may require additional support with managing their medication.

The relationship between gender and adherence to medication is inconsistent. Some studies have reported females to have better adherence to prescribed medication,^39,40 while other studies have reported the contrary.^41,42 In addition, some studies have not reported any association between gender and adherence levels.^43,44 The lower adherence in males in the present study could be attributed to their responses on 2 questions related to unintentional nonadherence; this suggests unintentional nonadherence is the likely cause of lower adherence in males. In clinical practice, collaborative efforts should be made by health-care workers to understand if patients’ suboptimal adherence behavior is intentional or unintentional. This would assist in the development of appropriate strategies for improving medication-taking behavior. For example, reminders and pill organizers, among other strategies, may be beneficial in resolving unintentional nonadherence.⁴⁵

Although satisfaction with treatment was a significant predictor of adherence in the multivariate analysis, treatment convenience was not. This suggests that how reassured patients felt after taking an OAC is a more important determinant of adherence than difficulty experienced with OAC therapy. This is further supported by the similar adherence levels observed between respondents taking warfarin and the more convenient DOAC therapy.⁴⁶ While satisfaction has been consistently associated with adherence in both cardiovascular and noncardiovascular diseases,⁴⁷ one study in patients taking warfarin has reported no association, although the number of warfarin users who were adherent was very low.⁴⁸ The positive association observed in the present study is consistent with the broader literature. Therefore, efforts should be made to evaluate patients’ satisfaction with OAC therapy in routine practice and interventions to improve satisfaction should be incorporated where necessary. Patient satisfaction could be improved by encouraging better patient–health-care professional communication, such that patients’ beliefs, expectations, and preferences are considered in the choice of OAC therapy.⁴⁹ Future studies should aim to determine whether improvement in patients’ satisfaction positively influence medication adherence.

The significant negative association observed between the perception of information overload and adherence could be due to overemphasis of the negative effects, especially adverse effects, of OAC therapy. An understanding of patients’ beliefs concerning their therapy can be useful in resolving suboptimal adherence because health beliefs that are based on skewed or inaccurate information can have a negative consequence on health behavior.⁵⁰ Given the importance of information in shaping health beliefs⁵⁰ and the subsequent impact of health beliefs on medication adherence,⁵¹ health-care workers need to be educated on how to communicate health information to patients taking OACs. Research should be conducted to ascertain patients’ beliefs regarding OAC, as this would assist in tailoring educational interventions accordingly.

Patients who were more concerned about making mistakes when taking a prescribed OAC and the cost of their medication were more likely to display suboptimal adherence. While there is a paucity of studies focusing on patients’ concerns about OAC therapy, studies in patients taking analgesics⁵² and corticosteroids⁵³ have reported medication-related concerns to be inversely associated with adherence. Thus, identifying and addressing patients’ concerns could help in improving adherence.

Cost of medication has been identified as a major barrier to adherence in different populations and this is also consistent with the findings of our study.³⁸ However, the Australian government subsidizes the cost of OAC medications through the Pharmaceutical Benefits Scheme.⁵⁴ As such, the reason why cost is associated with suboptimal adherence in this population remains unclear and may be related to the demographic of the respondents in the survey. Qualitative studies are necessary to investigate patients’ concerns with OAC therapy and potential reasons why medication cost is a barrier to adherence in this population.

Other important factors that may affect medication adherence include the type of health-care setting⁵⁵ and polypharmacy.⁵⁶ Evidence suggests that patients receiving treatment in a specialized health-care setting are more likely to be adherent compared to those in a community-based setting.⁵⁵ Also, medication adherence has been reported to be negatively correlated with the number of medication patients are required to take.⁵⁶ It is therefore important to adequately identify probable causes of suboptimal adherence in order to properly address the issue.

Conclusion

Self-reported suboptimal adherence to OAC was common in this study. Predictors of suboptimal adherence included male gender, younger patients, lower satisfaction with therapy, higher burden of health information, and more concerns about making mistakes when taking OACs and cost. These findings suggest that identifying and resolving modifiable patient-related factors has the potential to improve adherence to OAC. Interventions to improve patients’ satisfaction with therapy, better communicate health information, and address OAC-related concerns should be incorporated into the care process for patients with AF receiving OAC therapy.

Footnotes

Authors’ Note

Obamiro Kehinde contributed to conception and design, contributed to acquisition, analysis, and interpretation, drafted the manuscript, critically revised the manuscript, gave final approval, and agrees to be accountable for all aspects of work ensuring integrity and accuracy. Chalmers Leanne, Lee Kenneth, Bereznicki Bonnie and Bereznicki Luke contributed to conception and design, contributed to analysis and interpretation, critically revised the manuscript, gave final approval, agrees to be accountable for all aspects of work ensuring integrity and accuracy.

Acknowledgement

Use of the ©MMAS is protected by US and International copyright laws. Permission for use is required. A license agreement is available from Donald E. Morisky, MMAS Research (MORISKY), 14725 NE 20th St Bellevue WA 98007, USA; dmorisky@gmail.com

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Bereznicki Luke and Chalmers Leanne have received consultancy funding from Aspen Pharmacare Australia for the development of education materials related to warfarin therapy. Luke Bereznicki has also received consultancy funding from Boehringer Ingelheim Pty Ltd for the development of educational materials for dabigatran/atrial fibrillation and provision of expert advice regarding the optimal use of anticoagulants in the prevention of stroke.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Kehinde O. Obamiro, MSc

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Adherence to Oral Anticoagulants in Atrial Fibrillation: An Australian Survey

Abstract

Background:

Methods:

Results:

Conclusion:

Keywords

Introduction

Methods

Survey Instruments and Scoring

Recruitment and Sample Size

Statistical Analyses

Ethics and Consent

Results

Discussion

Conclusion

Footnotes

Authors’ Note

Acknowledgement

Declaration of Conflicting Interests

Funding

ORCID iD

References