Article

Factors Influencing Oral Adherence: Qualitative Metasummary and Triangulation With Quantitative Evidence

oral adherence, barriers, facilitators
CJON 2015, 19(3), 6-30. DOI: 10.1188/15.S1.CJON.6-30

Background: Concern about adherence to oral agents among patients with cancer has grown as more oral agents are being used for cancer treatment. Knowledge of common factors that facilitate or inhibit adherence to oral medication regimens can be beneficial to clinicians in identifying patients at risk for nonadherence, in planning care to address barriers to adherence, and in educating patients about ways to improve adherence.

Objectives: The focus of this review is to synthesize the evidence about factors that influence adherence and identify implications for practice.

Methods: Literature was searched via PubMed and CINAHL®. Evidence regarding factors influencing adherence was synthesized using a metasummary of qualitative research and triangulated with findings from quantitative research.

Findings: Forty-four factors influencing adherence were identified from 159 research studies of patients with and without cancer. Factors associated with adherence in oncology and non-oncology cases included provider relations, side effects, forgetfulness, beliefs about medication necessity, establishing routines for taking medication, social support, ability to fit medications in lifestyle, cost, and medication knowledge. Among patients with cancer, depression and negative expectations of results also were shown to have a negative relationship to adherence.

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    Lack of patient adherence to prescribed oral medications in the setting of chronic disease is not a new problem, and a substantial amount of literature is devoted to this issue. In oncology practice, the growth of oral versus IV chemotherapy has brought the concern of adherence to the forefront in cancer care. The problem of nonadherence involves numerous contributing factors that have been documented, predominantly in the setting of chronic disease. Many of these factors may be applicable to patients with cancer. Understanding the reasons why patients do not adhere to prescribed medication regimens, and the factors that appear to facilitate adherence, can be helpful to develop strategies to improve adherence.

    The purpose of this article was to synthesize the evidence regarding factors that inhibit or facilitate medication adherence in patients with chronic disease and patients with cancer. Inhibiting factors identified may be helpful to clinicians to recognize those patients who are at increased risk for nonadherence and in need of intervention to address this problem. Identification of the factors that are associated with improved adherence can be incorporated into interventions with patients to promote adherence.

    Methods

    Literature Search and Inclusion Criteria

    Three initial literature searches were conducted to retrieve evidence from qualitative studies exploring medication adherence, studies of technology applied to the problem of oral adherence, and studies of adherence interventions in patients with and without cancer from PubMed and CINAHL®. Inclusion criteria were any type of research design, systematic review, or meta-analysis; a study focus on medication adherence; a sample consisting of patients aged 18 years or older; a study reporting findings regarding factors associated with adherence to medications; and studies where medications were self-administered by the patient. Exclusion criteria were non-research studies or systematic reviews, samples of patients with psychiatric illness or substance abuse, and self-administered medications taken by other than the oral route or inhaled medication in cases of asthma. Intervention studies as well as descriptive studies were used as long as factors influencing adherence were reported. After the initial literature search, ongoing searching was conducted in PubMed and CINAHL using monthly auto alerts. Evidence included in this article was retrieved through December 4, 2014.

    Initial review of article titles and abstracts was conducted against inclusion and exclusion criteria. Duplicates and papers meeting exclusion criteria were eliminated. Abstracts of remaining references were then reviewed by a second individual, and full papers were obtained for those that appeared to meet inclusion criteria. When abstracts were not available, the full paper was obtained for review. Articles that did not meet inclusion criteria on full-article review were then eliminated.

    Full-search terms per database used are shown in Figure 1. A diagram of the search process and selection results is shown in Figure 2. Qualitative evidence was synthesized via a metasummary approach. Quantitative findings were then summarized and triangulated with qualitative results.

    Qualitative Metasummary

    Qualitative studies were read in full, and all factors associated with adherence were identified and labeled from themes described and exemplars reported. Information from each subsequent study was compared to previously labeled factors. Content that was judged to be consistent with a previously identified factor was given the same factor label. Additional factors that did not fit into a prior grouping were uniquely identified for each study and labeled separately. This process was continued for all studies until all factors identified were appropriately grouped and labeled. All articles were read in full a second time to ensure all factors were represented and factor labeling was consistent. A 50% random sample of qualitative and mixed-methods studies were reviewed by another investigator to establish reliability in the identification and labeling of factors. Disagreements in the identification or labeling of factors were resolved by consensus.

    Articles were not appraised for quality. Instead, each publication was mined for evidence, specifically regarding factors associated with or influencing oral adherence. This approach has been suggested as more appropriate than comparison of studies to some a priori standards to provide a realist synthesis of evidence (Leeman et al., 2010).

    The method for metasummary of qualitative findings was derived from Sandelowski, Barroso, and Voils (2007). A frequency effect size, the percentage of studies included in the review in which the factor was present, was calculated for each factor. An intensity effect size was calculated for each study by dividing the number of findings with frequency effect sizes greater than 20% contained in that report by the total number of findings with the same effect size across all reports. This was conducted to determine if any individual study provided an extraordinary number of factors, or if factors were predominantly derived from weaker studies. The authors arbitrarily chose an effect size of greater than 20% to represent a cut point in frequency to identify factors with a meaningful effect size.

    The authors of the current article diverged from the method described by Sandelowski et al. (2007) and did not include quantitative studies in calculation of effect sizes. Qualitative work, with its open-ended or semistructured approach, provides an equal opportunity for any concept, or factor, to emerge. Identification of factors from quantitative work would only occur if study measurements were preselected to include data collection for a specific factor. For this reason, the authors chose to apply the method of effect size calculation only to qualitative studies and the qualitative portions of mixed-methods studies. Findings from quantitative studies were summarized independently. Qualitative and quantitative results were then triangulated to determine similarities across both study types.

    Quantitative Synthesis

    Quantitative findings regarding factors associated with adherence were identified from the article text as well as data shown in tables of results. Factors were identified as having a positive, negative, or no relationship to the measured outcome in the study. Only factors that were reported to have a statistically significant association were identified as positive or negative.

    Results

    After full article review, 159 studies were included in analysis, including 83 quantitative, 46 qualitative, 17 mixed methods, 9 systematic reviews, 3 meta-analyses, and 1 integrated review. Study samples ranged from 10–101,028. Large quantitative studies were those using sources, such as insurance and pharmacy claims databases, for descriptive analyses. Samples included patients taking oral medications for HIV, diabetes, coronary artery disease, congestive heart failure, osteoporosis, hypertension, chronic obstructive pulmonary disease, tuberculosis, renal failure, and cancer; patients using inhaled steroids for asthma; and patients taking oral contraceptives or antibiotics. Some studies were conducted on focused samples of patients with low income, older adult patients, those with potential ethnic or language barriers, or those with known high or low levels of adherence. Sample characteristics and other study aspects for each study included are shown in Table 1.

    Factors Identified Across All Studies

    Forty-four discrete factors were identified, including aspects of beliefs and preferences about health and medication, medication-related experience, personal factors, treatment factors, health system and care delivery, health and disease condition, knowledge, and supports. Factors identified in each of these categories are shown in Figure 3. Exemplars from qualitative studies demonstrating factors with the highest effect sizes are shown in Figure 4.

    Qualitative Metasummary and Triangulation With Quantitative Results

    Intensity effect sizes ranged from 10%–80%. The data did not show that any particular study contributed excessively to summary results. Frequency effect sizes ranged from 2%–42%. Results of frequency effect size calculations and triangulation with quantitative findings are shown in Table 2. As shown here, 11 factors had a greater than 20% effect size: provider relations, side effects, forgetfulness, necessity, routinization, support, lifestyle fit, cost, medication knowledge, pill burden, and regimen complexity. Overall, good agreement was noted between qualitative and quantitative results. Although the majority of evidence showed that the higher the pill burden, the lower the adherence, several studies did show a positive relationship between pill burden and adherence (Efficace et al., 2012; Halkitis, Palamar, & Mukherjee, 2008; Mallick, Cai, & Wogen, 2013; Noens et al., 2009; Sawesi, Carpenter, & Jones, 2014; Verbrugge et al., 2013). This suggests that some patients who need to take numerous medications have developed strategies to facilitate their own adherence.

    Findings in Patients With Cancer

    Results of quantitative studies involving only patients with cancer taking oral agents for cancer (OACs) are shown in Table 3. These included long-term hormonal therapy or other OACs. Only one qualitative study was found for patients taking OACs, so only quantitative results for this group were synthesized. Factors that had a positive influence or association with adherence were the same for patients with cancer and others, including necessity, support, lifestyle fit, provider relations, and medication knowledge. Factors that had a negative relationship with adherence that were common for both patients with and without cancer were side effects, forgetfulness, difficulty incorporating medication taking into lifestyle, and cost or lack of insurance coverage. In addition, among patients with cancer, depression and negative expectations about the effectiveness of the medication emerged as frequent factors associated with nonadherence. Among patients who did not have cancer, regimen complexity and pill burden were additional factors that negatively influenced adherence. These factors had mixed results among patients with cancer.

    Demographic Factors

    Findings regarding the association of age and gender were mixed. Thirty-five articles examined the relationship between age and adherence. Nine studies reported that older age was associated with lower adherence (Atella & Kopinska, 2014; Barcenas et al., 2012; Bestvina et al., 2014; Cook et al., 2009; Curtis et al., 2009; Kahn et al., 2007; Klepin et al., 2013; Puts et al., 2014; Streeter, Schwartzberg, Husain, & Johnsrud, 2011). Eighteen studies reported a positive relationship between older age and adherence (Barclay et al., 2007; Confavreux et al., 2012; Ettenhofer et al., 2009; Gatti, Jacobson, Gazmararian, Schmotzer, & Kripalani, 2009; Gwadry-Sridhar et al., 2013; Hershman et al., 2010; Jamous et al., 2011; Lebel et al., 2012; Mallick et al., 2013; Sedjo & Devine, 2011; Taira et al., 2007; Tarantino et al., 2010; Tiv et al., 2012; Unni & Farris, 2011; Van Dyk, 2011; Wagg, Compion, Fahey, & Siddiqui, 2012; Wells, Peterson, Ahmedani, & Williams, 2013; Wong et al., 2012). These studies included patients on oral chemotherapy, tamoxifen, antiretroviral therapy, and medications for various chronic diseases.

    Two systematic reviews of studies in patients with cancer (Gater et al., 2012; Verbrugghe et al., 2013) and one mixed-methods study (Watt et al., 2010) reported mixed results regarding the association of age and adherence. Three studies reported lack of any significant relationship between age and adherence among women with breast cancer (Partridge et al., 2010; Ruddy et al., 2012) and older adults (Schuz et al., 2011). Two systematic reviews of studies in patients with cancer reported lower adherence in younger as well as older study participants (Mathes, Antoine, Pieper, & Eikermann, 2014; Sawesi et al., 2014).

    The relationship between gender and adherence showed conflicting results across 17 studies and two systematic reviews. Five studies reported lower adherence among women (Atella & Kopinska, 2014; Curtis et al., 2009; Gallagher, Warwick, Chenoweth, Stein-Parbury, & Milton-Wildey, 2011; Wells et al., 2013; Wileman et al., 2011), and five reported higher adherences among women (Janakan & Seneviratne, 2008; Kamau, Olson, Zipp, & Clark, 2011; Li, Wallhagen, & Froelicher, 2008; Wagner & Ryan, 2004; Wong et al., 2012). Systematic reviews reported mixed findings regarding the association of gender with adherence among patients on oral therapies for cancer (Gater et al., 2012; Mathes et al., 2014). Six studies reported no significant association between gender and adherence (Bestvina et al., 2014; Hadji, Kyvernitakis, Albert, Jockwig, & Kostev, 2014; Johnson et al., 2006; Mallick et al., 2013; Molloy et al., 2009; Tiv et al., 2012). Samples included patients on oral therapies for cancer and patients with varied chronic diseases.

    Five studies showed a consistent relationship between race and adherence. Lower adherence was associated with non-Caucasian race in three studies (Cooper et al., 2010; Nekhlyudov et al., 2011; Wells et al., 2013) and two systematic reviews of studies in cancer (Puts et al., 2014; Sawesi et al., 2014). One study showed no relationship between race and adherence (Kirk & Hudis, 2008). Individual studies were conducted in the United States and the United Kingdom.

    The association between education level and adherence was examined in four studies. Findings were mixed. Two failed to demonstrate a relationship between education level and adherence (Avila, Aliti, Feijó, & Rabelo, 2011; Bestvina et al., 2014). One large descriptive study of ethnic minority group patients on antihypertensive medications showed a direct correlation between education level and adherence (Taira et al., 2007). A study of patients on imatinib also showed a positive relationship between higher education level and oral adherence (Noens et al., 2009)

    Language was associated with nonadherence in one study of economically disadvantaged individuals with lupus and rheumatoid arthritis (Gariga Popa-Lisseanu et al., 2005) and one meta-analysis of studies of patients taking antiretroviral medications (Ortego et al., 2012). Language barriers may have an influence on adherence for a variety of reasons.

    Discussion

    Qualitative metasummary is a recognized method for synthesis of qualitative research findings. The approach used here provides an objective way to identify the relative frequency of factors observed that provides an effect size. This enables comparison of the relative strength of individual factors in the phenomenon of medication adherence. Triangulation with quantitative study findings provides further confirmation of the relevance of factors identified. The approach of metasummary of qualitative findings and triangulation with quantitative results used and volume of research included in this synthesis can be seen as strengths of this work.

    The number of different factors identified supports the understanding that oral adherence is a multidimensional behavioral issue, and some patients have unique experiences and beliefs that influence adherence. On the other hand, the commonalities seen across studies with various patient types suggest that many problems and strategies affecting oral adherence are shared and unexceptional. Those factors with high-frequency effect sizes may be applicable to a majority of patients. Information provided from the current article’s synthesis of evidence can be used to identify those patients who may be at high risk for nonadherence and to tailor interventions to address specific needs. Information also can be used to educate patients about factors and strategies, such as routinization, that can be helpful to promote adherence.

    The belief in the necessity of taking medication was the most frequent factor associated with improved adherence. Reports of overadherence point to the importance of ensuring that belief is accompanied by appropriate medication knowledge. Allen and Williamson (2014) reported a case of a patient who overcomplied with adjuvant capecitabine and did not report serious side effects because he felt that taking the medication was important to treat his cancer. He did not want to have dose reductions in the fear that the treatment would be less effective. DiBonaventura, Copher, Basurto, Faria, and Lorenzo (2014) reported that patients valued the attribute of effectiveness highest in the preference ratings, and were willing to trade off side effects for survival gains.

    The complexity of regimens involving OACs has been suggested as a barrier to adherence among patients with cancer (Prasad, Massey, & Fojo, 2014; Spoelstra et al., 2013). It was surprising that this was not found in the subset of evidence from studies of patients with cancer. This may be caused by the relatively limited evidence in patients with cancer. Further research in the area of adherence to oral agents in patients with cancer may alter these findings.

    Cost was a frequent barrier to adherence among patients with cancer and others. For patients taking OACs, a number of programs have been administered by pharmaceutical companies to assist with cost concerns. Nurses need to be aware of these programs and assist patients to seek financial assistance.

    Studies included in the current article varied substantially in terms of the types of illnesses involved and the countries in which studies were conducted. Although one might expect important differences across various cultural groups, factors with the highest frequency effect sizes cut across many of these groups. This suggests that individuals who need to take medications have many common human experiences that influence their adherence behaviors.

    Demographic characteristics, such as older age and female gender, have been suggested as factors that have a positive influence on adherence. This review did not find a consistent relationship between age or gender and adherence. Results reported in the current article are in concert with those reported in other systematic reviews (Gater et al., 2012; Mathes et al., 2014; Sawesi et al., 2014; Verbrugghe et al., 2013).

    Few studies examined the links between race, ethnicity, language, or level of formal education and medication adherence. Although the evidence is limited, findings showed a relationship between non-Caucasian race, language barriers, lower education level, and nonadherence. Whether these factors are related to each other is unclear. These characteristics may contribute to inadequate medication knowledge or influence the nature of relationships with healthcare providers, two factors that have more proximal influence on adherence. In studies conducted with African American patients with HIV and hypertension (Gaston & Alleyne-Green, 2013; Lewis, 2012; Ogedegbe, Harrison, Robbins, Mancuso, & Allegrante, 2004), provider relations were shown to influence adherence.

    Limitations

    The major limitations of this article are related to the nature of qualitative work and the relatively limited evidence in patients with cancer. Within individual qualitative studies, different individuals may create different classification and labeling of themes and concepts. The same could be true in the process used in this article to synthesize qualitative findings. This limitation was adjusted to some extent by secondary review of identified factors to establish reliability.

    Most quantitative studies reported only demographic data, because many of these studies obtained data for analysis from large pharmacy or insurance databases. The most meaningful information to explore the patient experience was provided in studies that used qualitative methods. Only one qualitative study was identified that focused on oral adherence among patients with cancer.

    For the purpose of the current article, adherence was seen to include medication initiation and persistence. The specific factors that influence patient decision making and adherence behavior may have differed in various phases of adhering to a medication regimen. This review did not attempt to differentiate between these phases or between intentional and unintentional nonadherence or between over- and underadherence. None of the studies included specifically examined the issue of overadherence.

    Conclusion

    In patients taking OACs as well as patients taking medications for other diseases, factors that facilitate adherence and factors that have a negative effect on oral adherence were similar. Substantial evidence indicates that belief in the necessity of taking the medication as prescribed, establishing regular routines for medication taking, the ability to fit self-medication into one’s lifestyle, medication knowledge, support from family and others, and positive relationships with healthcare providers can facilitate adherence. Factors associated with lower adherence in all types of patients were side effects of the medication, forgetfulness, and cost. In patients with cancer, the presence of depression and negative expectations about medication efficacy also had negative influences on adherence. Across all studies, no consistent relationship was seen between age or gender and adherence. Fairly consistent findings were noted among a few studies that individuals of non-Caucasian race and those with primary languages other than English had lower adherence.

    Implications for Practice and Research

    As shown here, many factors that influence adherence are relevant for patients with cancer taking OACs, as well as other types of patients. Clinicians can use the evidence provided here in assessing individual patient situations to identify presence of factors that negatively influence adherence behavior. This may distinguish patients on OACs who are most at risk for nonadherence and provide direction for working with patients to manage or remove barriers to adherence. This type of assessment could enable providers to tailor adherence-related interventions to individual patient needs. Evidence provided regarding those factors that are reported to promote adherence can be incorporated into patient psychoeducational and supportive interventions. Patients may benefit from knowledge about experiences of others and the strategies they found to be helpful, such as establishing routines and ways to incorporate taking medication into one’s lifestyle.

    Findings show that provision of education and information to improve medication knowledge can have a positive influence on adherence; however, the number of factors with high effect size shown in the current article suggests that education about medications alone is not likely to be sufficient to manage adherence. The authors’ findings point to the particular importance of screening for and managing depression and the effective management of side effects to promote adherence to oral therapies among patients with cancer. Monitoring and managing side effects have been suggested as approaches that can have a significant influence on adherence among patients with cancer (Winkeljohn, 2010). Evidence provided also points to the importance of developing a positive relationship and partnership with patients as part of an approach to promote adherence.

    The area of dealing with patient adherence to OACs could benefit from further qualitative research to enrich the understanding of the dynamics of adherence behaviors in this patient population. Research to test interventions designed to address factors that most frequently affect adherence would be beneficial. To date, substantial research has been conducted on technological interventions aimed at addressing the factor of forgetfulness, such as text messaging and automated voice reminders, and several studies have examined the impact of out-of-pocket cost reduction on adherence. Less attention has been paid to interventions designed to address other common barriers and facilitators to adherence. Little evidence exists regarding the connections between race, ethnicity, and educational level to adherence, and evidence suggests that these factors can influence adherence. Additional research into these relationships and interventions to address these differences is needed among patients taking OACs.

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    About the Author(s)

    Margaret Irwin, PhD, RN, MN, and Lee Ann Johnson, MSN, RN, are research associates at the Oncology Nursing Society in Pittsburgh, PA. The authors take full responsibility for the content of the article. The authors did not receive honoraria for this work. The content of this article has been reviewed by independent peer reviewers to ensure that it is balanced, objective, and free from commercial bias. No financial relationships relevant to the content of this article have been disclosed by the authors, planners, independent peer reviewers, or editorial staff. Irwin can be reached at mirwin@ons.org, with copy to editor at CJONEditor@ons.org. (Submitted December 2014. Revision submitted January 2015. Accepted for publication January 10, 2015.)

     

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