Abstract
Objective. Reported adherence in rheumatoid arthritis (RA) varies widely (10.5–98.5%). Variability may result in part from different methods used to measure adherence. Our aims were to quantify adherence to antiarthritis medications for each method and to identify variability and associated factors.
Methods. The systematic literature review examined PubMed, the Cochrane central database, and article reference lists from 1970 to November 2014. Papers with medication adherence data (disease-modifying antirheumatic drugs, steroids, and nonsteroidal antiinflammatory drugs) in adult patients with RA or data on associated factors were included. Adherence rate was recorded for each method. Random-effect metaanalysis estimated adherence for different evaluation methods.
Results. Adherence rate was 66% (95% CI 0.58–0.75). There were no differences in adherence among different measurement methods (interview, questionnaires, etc.). Regression analysis showed that adherence decreases during followup. Among 100 possible factors potentially effecting adherence, 7 adherence-associated factors were found in at least 2 different studies. These were the use of infliximab compared with etanercept or methotrexate (MTX), use of MTX compared to sulfasalazine or to etanercept, belief in the necessity of the medications, older age, and white race.
Conclusion. Overall adherence rate was 66%. We suggest that readers appraise adherence studies according to the medications evaluated, the validity of the method, and the scales and cutpoints.
Adherence was defined by the World Health Organization (WHO) as the extent to which a person’s behavior — taking medication, following a diet, and/or executing lifestyle changes — corresponds with agreed recommendations from a healthcare provider1. As the WHO report stated, “Poor adherence to long-term therapies severely compromises the effectiveness of treatment...1” Therefore, it is important to have a firm understanding of measurement and determinants of adherence in rheumatoid arthritis (RA). The exact prevalence of adherence to medications in patients with RA is unknown. Variability exists regarding apparent adherence among literature reports, and results vary from 10.5% to 98.5%2 across studies. This variability may result in part from different methods used to measure adherence2. Definition of adherence, type of medication, etc., may be involved as well. Further, little is known about predictors for adherence in RA3. Our primary aim was to determine, in RA, the rate of adherence to antiarthritis medications according to the different methods used to measure adherence. We hypothesized that adherence rate is influenced by the method used to measure it.
Our secondary aims were to identify the variability among studies and predictors for adherence.
This is, to the best of our knowledge, the first attempt to estimate adherence rate in RA, both cumulative and separately, for different methods used to measure adherence, including the influence of duration of followup. We also demonstrate the variability of the cutpoints used in different studies to define adherence. Finally, we update the previous review3 that summarized the literature on risk factors for adherence up to 2011.
MATERIALS AND METHODS
Information sources
The systematic literature review (SLR) examined the Medline, Scopus, Cochrane central, and CINAHL databases from 1970 to November 2014 (Supplementary Data 1 available online at jrheum.org) to identify adherence studies to medications in adult patients with RA. Associated reference lists were searched. Only English literature was included. Reviews, case reports, letters, and editorials were not included as primary data. Reviews were used to identify relevant articles and to test the search strategy. Both observational data and data from control groups of randomized controlled trials (RCT) were included.
Study selection and data extraction
All abstracts or titles were screened for potential inclusion by 2 authors independently (Table 1). There was a 93% agreement by the primary readers. After screening of titles and abstracts, eligible papers were fully read and evaluated independently by 2 investigators for further eligibility using standardized data extraction forms (Table 1). Discrepancies not resolved by consensus were adjudicated by a third author (DEF). Data were sought for type of RA population, country, study design, timepoint when adherence was assessed, outcome (percent of adherent/compliant patients), and factors associated with adherence. As a result of careful extraction of the articles as well as reviews6,7, variability within studies was identified according to 5 domains: type of medications, length of drug use, cutpoints defining adherence, ways of defining adherence, and method used to measure adherence.
Inclusion criteria.
Exclusion criteria.
Variability across studies was evaluated according to 3 domains: the method used to measure adherence (questionnaire, etc.), the type of questionnaires used, and the cutpoints to define adherence. An attempt to contact authors was made if further data were needed.
Papers with lower cutpoints compared with most other papers were excluded to reduce variability8,9,10. Nevertheless, they were evaluated for associated factors if they included relevant data. Some studies used verbal and not a numerical scale, such as “taking medications none/some/most/all of the time”. Since most papers used the general concept that adherent patients take their medications most of the time, we considered “taking medication most or all of the time” as relatively high cutpoints.
We used the cutpoints suggested by the authors in their articles for any dichotomizations.
Papers were assigned to subgroups according to the method used to measure adherence. When intrastudy variability was found, we chose the result most congruent with the other studies in each subgroup. In studies that measured adherence at multiple timepoints, we used only the first measurement.
If studies reported the percent of nonadherent patients, we used the formula:
We used the terms reported by the original authors for describing compliance or adherence, as suggested in the WHO report1.
Methodological process for exploring the factors associated with adherence
A list of factors that were examined for possible association with adherence was produced through a literature search. These included age, sex, disease outcomes, etc. Risk factors were categorized as either associated (positively or negatively) or not associated with adherence. All factors were listed in a table that summarized which study examined each factor. Identical factors from different studies were collapsed. Positive association with adherence was considered as negatively associated with nonadherence only if the factor was a dichotomous variable (for example, male/female). Factors were categorized into 5 groups according to the 2003 WHO report1.
Quality assessment
After reviewing several systems for quality assessments [Newcastle-Ottawa quality assessment scale, the UK National Institute for Health and Care Excellence (NICE) guidelines, the Grading of Recommendations Assessment, Development, and Evaluation], we chose the one for observational studies designed specifically for adherence3. Studies were high quality if at least 4 of 5 essential questions regarding participation rate (≥ 80%), reproducibility of method, reduction of recall bias, and selection bias (using consecutive or representative samples) were affirmatively answered, and the total score was at least 7 out of 10. The NICE guidelines were used for RCT, examining for selection bias, performance bias, attrition bias, and detection bias. RCT were considered high quality if at least 3 criteria were fulfilled.
Statistical analysis
Data were collected and reported based on the recommendations for the Metaanalysis by Observational Studies in Epidemiology, because most of the studies were observational, and we did not examine studies evaluating healthcare interventions.
Qualitative assessment of heterogeneity
The included trials were heterogeneous in population, methods to measure adherence, scale, and cutpoints used. Statistical heterogeneity was examined using the I2 statistic. A value > 50% represented substantial heterogeneity.
Between-study heterogeneity was assessed by the Q-statistic test and statistic. P values < 0.1 were considered statistically significant.
The included studies were detailed according to design, populations, quality assessment, and method used to measure adherence (Table 2 and Table 3).
Baseline characteristics.
Definitions, cutpoints, and percent adherence/compliance across studies. Percent of adherent patients across studies that were included in the metaanalysis. Studies were placed into subgroups according to the method used to measure adherence. Scale and cutpoints used to rate adherence are also shown.
Within each method of measurement, if heterogeneity was low, we planned to apply the fixed-effects model. Otherwise, random-effect model using the restricted maximum likelihood methods was applied to estimate percentage of adherence. Forest plots were generated to summarize the overall estimated proportion and the estimated proportion stratified by measurement method based on their fitted model. Influential case diagnostics were performed to test outlying cases. We had planned to perform sensitivity analyses by implementing the leave-one-out diagnostics for each study. We had planned to perform a weighted linear regression using sample size as weights to test the difference among methods used to measure percentages of adherence. We had planned to perform funnel plots and Egger test to investigate the influence of publication bias. All analyses were performed using R3.1.238. The metaanalysis was conducted using the metafor package39. The statistical significance level was 0.05, except for the test of between-study heterogeneity.
RESULTS
Study selection
The search strategy yielded 320 citations (Figure 1). Perusal of the reference lists yielded an additional 5 articles8,14,17,22,23. After applying inclusion/exclusion criteria, 53 articles remained. Following detailed extraction, a further 22 articles were excluded (Figure 1), leaving 31 articles examined for either metaanalysis on percent adherence (n = 24) or associated factors (n = 30; Table 2). The 7 articles included in the associated factors analysis but not the adherence analysis were excluded because they did not have definable cutpoints for adherence34,35,36,37 or because the cutpoints were much lower than the rest of the studies8,9,10. Dichotomization of the scale used to measure adherence was necessary to quantitate adherent patients. Papers that only reported the absolute mean score for all patients but did not use a cutpoint to define which patients were considered adherent were excluded from our metaanalysis because it was not possible to extract the percentage of adherent patients.
Article selection. The search strategy yielded a total of 31 articles, 24 articles for the metaanalysis of adherence rate, and 30 articles for a qualitative analysis of associated factors.
Among the 31 included papers, 1 was excluded from the analysis for associated factors because data were lacking33. Overall, 13,921 patients were included in the metaanalysis for rate of adherence and 67,216 patients were included in the associated-factors analysis.
Quality of studies
Eleven studies were of high quality (Table 2). Seven studies that used prescription claims11,12,13,14,34,35,37 had high scores of 9 out of 10, but had a potential selection bias (not inviting/reporting consecutive patients or a representative sample; Supplementary Table 1 and Supplementary Table 2 available online at jrheum.org).
Variability across studies
Variability across studies was observed in 2 categories: measurement methods and cutpoints.
Among measurement methods, 7 studies used prescription claims11,12,13,14,28,29,30, 6 used interview19,20,21,22,23,24, 7 used questionnaires2,15,16,17,18,31,33, 2 used electronic medication and event monitors (MEMS)26,32, 2 used drug levels24,27, and 2 used pill count24,25. Variability arose within questionnaire studies because questionnaires varied. Three studies used the Compliance Questionnaires in Rheumatology2,18,33, 1 used the Rheumatology Attitudes Index, and 1 used the Drug Record Registry, and all are specific to antiarthritis drugs. The rest used nonspecific questionnaires.
The second category was the cutpoints used. Most studies defined good adherence at the 80% cutpoint (Table 3). Ten studies used categorical scales or a yes/no scale, defined by words to evaluate adherence12,15,17,19,20,21,22,23,24,26.
Variability within studies and selection of relevant data
Type of medications: Three studies (all used prescription claims) measured adherence to several medications11,13,14. Because etanercept (ETN) was the most frequently used medication, ETN was used as our benchmark for the prescription claims group (Table 3).
Length of drug use: Two papers (both used prescription claims) measured adherence in naive versus longterm users11,12. Because most studies did not report these data, we used total adherence data.
Different cutpoints: If multiple cutpoints were recorded, we used the one closest to 80%16,18 (Table 3).
Defining adherence: Including adverse events as a source of nonadherence is less accurate than is desirable because including adverse events confounds the adherence percentages. Nevertheless, we used that definition because most papers did not differentiate among reasons for non-adherence27 (Table 3).
Type of questionnaires: When more than 1 questionnaire was available2,15,21, the one most commonly used or where cutpoints were available18 was used.
Results of metaanalysis
Overall, 66% of patients were adherent to medications (95% CI 0.58–0.75; Figure 2). Weighted linear regression revealed no statistically significant difference among methods used to measure percentage of adherence (p = 0.2). Statistically significant large (I2 = 95.31%) heterogeneity [Q (df = 25) = 466.15, p < 0.001] was observed for overall adherence. Statistically significant heterogeneity was also present in some measuring methods: prescriptions claims [Q (df = 1) = 31.43, p < 0.001, I2 = 96.82%], MEMS [Q (df = 6) = 282.79, p < 0.001, I2 = 98.41%], and interview [Q (df = 5) = 10.03, p < 0.074, I2 = 45.27%]. Random-effects models were applied to these methods, while fixed-effects models were applied to questionnaires, pill count, and drug level.
Metaanalysis of percent of adherent patients by method used to measure adherence. RE: random-effects; MEMS: medication and event monitors.
Sensitivity analysis
We computed various outlier and influential case diagnostics, such as DFFITS and Cook distance, which indicate the influence of deleting 1 study at a time on the model fit and the fitted values. The summary percentages of adherence remained stable, indicating that our results were not driven by any single study. Influential case diagnostics suggested that several studies2,13,17,31 introduced some additional residual heterogeneity into the model (Supplementary Figure 1 available online at jrheum.org).
Publication bias
Asymmetry was observed in the funnel plot (Supplementary Figure 2 available online at jrheum.org); however, the evidence of publication bias detected using Egger test was not statistically significant (p = 0.06).
The cumulative metaanalysis revealed that the summary percentage of adherence converged to the final estimate when more studies were included in the analysis.
Adherence during followup
Seven longitudinal studies measured adherence across time15,19,23,25,26,27,31 (Table 2) with a mean followup of 10.4 months (range 1–36 mos). Most studies included outpatients and 2 studies included patients with early RA. Quality was low in 6 studies and high in 1. Regression analysis of pooled data calculated that percent of adherent patients decreased nearly 1% per month of followup (Supplementary Figure 3 available online at jrheum.org).
Associated factors
One hundred associated factors were identified. Using the WHO categories reported in 2003, we identified 19 patient-related factors, 34 treatment-related factors, 17 condition-related factors, 9 health system factors, and 21 sociodemographic/economic factors. Seven factors were found in at least 2 different studies as having a significant association with adherence with no studies to the contrary (Table 4). Three studies found that better adherence was associated with a belief that the drug was necessary, while 1 study found no association2,9,17,18.
Associated factors. Includes all the factors that were examined for possible association with adherence or nonadherence. The table shows the number of studies that found either positive or negative association with adherence. The total number of studies that looked for an association is given and includes studies that did not find any association. Values are n unless otherwise specified.
Older age was associated with better adherence in 5 studies, but no association with age was found in 10 studies. An association of age with adherence was also found in human immunodeficiency virus (HIV)40, lending some support to this association.
White compared with African American/black ethnicity was associated with adherence in 2 studies, while 1 study did not find this association. A statin study supported the finding of a lower adherence rate among non-whites39. Conflicting findings were found regarding sex2,9,12,13,15,18,19,20,21,23,25,28,37.
Among treatment-related factors13,14,30,34,36, although there were only a few studies, the results were consistent (Table 4). Adherence was better when taking either ETN or infliximab (IFX) than methotrexate (MTX)14,34. Adherence was also better when taking MTX compared with sulfasalazine (SSZ)34,36 and finally, adherence to IFX was better than to ETN13,14,30.
Higher weekly out-of-pocket cost was negatively associated with adherence in 1 study37. On the other hand, higher total healthcare cost11, financial status22, and health maintenance organization insurance37 were positively associated with adherence.
DISCUSSION
Our metaanalysis is the first, to our knowledge, to evaluate adherence quantitatively and to seek a relationship between adherence and the method used to measure it.
Overall, 66% of patients were adherent to antiarthritic medications. Although previous literature suggested that interviews overestimated adherence rate2, our analysis found no statistical differences among the different methods (Figure 2). We also showed that adherence decreases during followup.
A previous SLR3 included 11 studies and 64 associated factors. We identified an additional 15 studies8,12,14,16,24, 25,27,28,29,30,31,32,34,35,36 and analyzed 100 factors.
Importantly, the previous SLR3 did not quantify adherence. In our metaanalysis, all patients were considered adherent because they fulfilled the cutpoint as defined by the authors. Most studies used a cutpoint of > 80% to define adherent patients. We excluded data on persistence, discontinuation, switching, treatment gap or retention rate, and adherence to nonmedical therapy7 (Table 1), as well as 1 study4 that merely used physician opinion to evaluate adherence, which could increase variability. We included 2 RCT because we did not seek to evaluate differences among interventions but sought overall quantitation. We took only the control group, because these patients were not subjected to intervention. Further, we both included and removed the 2 RCT from our analysis and found that it made no significant difference in the results (data not shown).
Associated factors
Age, sex, education, Health Assessment Questionnaire, and disease duration were the most studied risk factors (Table 4). It was not possible to calculate a reliable estimate of the magnitude of the associations from the available data because of the heterogeneity of the methods used and the low quality of most studies. We can only point to trends and interesting findings that may represent targets for further studies. Three studies13,14,30 found IFX to be associated with a higher adherence than ETN. These data suggest that administration under supervision versus self-management promotes adherence. One potential intervention that could be driven from these results is to enhance administration under supervision in patients at risk for nonadherence. Adherence was better when taking either ETN or IFX than when taking MTX14,34. Adherence was better when taking MTX than when taking SSZ34,36. If this association is true, it may be related to the number and size of tablets. One may conclude that complex treatment regimens reduce adherence in RA. Indeed, complex treatment regimens have been associated with nonadherence in cancer41, diabetes42, epilepsy43, hypertension44, and HIV45. However, in RA the data are inconsistent. One study33 found neither an association with the number of doses per day nor with 1–4 daily regimens. Two other studies found no association with the total number of all tablets taken per day10,18. On the other hand, 1 study found a negative association with adherence when using > 3 disease-modifying antirheumatic drugs (DMARD) versus MTX monotherapy12. Another study found negative association with the increased total number of RA medications or the number of antirheumatic tablets taken per day13, while 2 studies did not find such association18,33. Overall, no conclusion can be drawn from the literature regarding regimen complexity and adherence in RA (Table 4).
Patient-related factors represent the resources, knowledge, attitudes, beliefs, perceptions, and expectations of the patient2. In this category, we found beliefs in the efficacy of treatment to be a consistent predictive factor for adherence2,9,17,18. This is in concert with literature in RA3, HIV, and tuberculosis therapy1, contrary to most demographic factors that vary between studies.
Older age was the most examined risk factor, being tested in 15 different RA studies. Again the data are inconsistent. There was a positive association in 5 studies and no association in 10, which is not a robust finding. Yet, older age was also associated with adherence in epilepsy46, diabetes47, bipolar disease48,49, HIV15, and for statin use16.
Data regarding ethnicity may be incomplete because the number of non-white patients recruited into research studies is very low and this may cause a potential lack of power. Overall, sociodemographic predictors of adherence may be of limited value because they are not modifiable, although they may be of some use for risk screening and targeted interventions.
There is uncertainty about the association of nonadherence with disease activity. Three studies found an association between nonadherence and various aspects of higher disease activity such as the Disease Activity Score at 28 joints (DAS28), flare, disability, and swollen joint count12,15,32 or its inverse (better adherence with less pain)21. Conversely, longer duration of morning stiffness was associated with better adherence20 and no association or negative associations with DAS28 were found twice25,31. Conflicting findings were found regarding associations with erythrocyte sedimentation rate12,15,19,20,23 and joint count12,19,21,27. Thus, they do not allow a conclusion regarding an association of disease activity with adherence, much less allow conclusions regarding cause and effect because these studies were not designed to answer this question.
Disease duration was negatively associated with adherence in the study by van den Bemt, et al2, as was found in diabetes50. None of the health system–related factors was repeatedly associated with adherence in more than 1 study.
Quality of evidence and limitations
Variability across and within studies was prominent. Most of the variability could be explained by heterogeneity. Yet heterogeneity was lower than 50% among studies that used questionnaires as well as among studies that used interviews. Among studies that used drug level, MEMS, and pill count, there were few studies and ranges within categories were very wide, and thus the analysis of these studies is of limited power. Further, the quality of studies was generally low. Our search did not include the EMBASE database and the search was limited to the English literature, which could bias the findings.
Many studies referred to antiarthritis medications and did not specify the drugs. In fact, some of the questionnaires and interviews were not specific to antiarthritis medications in any way15,16,18,20, although the questions were asked in the context of a rheumatology clinic.
The potential effect that recruitment has on our metaanalysis results could not be measured because even in studies that recruited consecutive patients, those who are nonadherent to medications are also more likely to miss outpatient appointments and potentially miss being recruited.
Our SLR and metaanalysis found an overall adherence rate to antiarthritis medications (DMARD, nonsteroidal antiinflammatory drugs, and steroids) in RA of 0.66, using cutpoints of ≥ 75% to define adherence. However, heterogeneity was large. Adherence decreased during followup. Seven factors were associated with better adherence (IFX compared with ETN or MTX, use of MTX compared to SSZ or to ETN, belief in the necessity of the medications, older age, and white race compared with African American).
To better interpret existing data, we suggest that readers consider the following: (1) which type of medications were evaluated?; (2) the method used to measure adherence — is it valid?; and (3) was a cutpoint used, and if so, at which level?
Future studies should use valid methods to measure adherence and use of cutpoints on the higher end of the scale. We also suggest moving away from the focus on static predictors and toward modifiable variables such as treatment regimen and psychosocial predictors to develop effective interventions.
ONLINE SUPPLEMENT
Supplementary data for this article are available online at jrheum.org.
- Accepted for publication October 27, 2015.
