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Multiple modality approach to assess adherence to medications across time in Multiple Sclerosis
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Multiple modality approach to assess adherence to medications across time in Multiple Sclerosis Efrat Neter , Anat Wolkowitz , Lea Glass-Marmor , Idit Lavi , Sharonne Ratzabi , Izabella Leibkovitz , Ariel Miller PII: DOI: Reference:
S2211-0348(20)30027-4 https://doi.org/10.1016/j.msard.2020.101951 MSARD 101951
To appear in:
Multiple Sclerosis and Related Disorders
Received date: Revised date: Accepted date:
2 October 2019 1 November 2019 12 January 2020
Please cite this article as: Efrat Neter , Anat Wolkowitz , Lea Glass-Marmor , Idit Lavi , Sharonne Ratzabi , Izabella Leibkovitz , Ariel Miller , Multiple modality approach to assess adherence to medications across time in Multiple Sclerosis, Multiple Sclerosis and Related Disorders (2020), doi: https://doi.org/10.1016/j.msard.2020.101951
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HIGHLIGHTS
One of few studies in MS employing multiple measures in medication adherence.
Different measures of medication adherence yielded different adherence levels.
Adherence rates to different administration routes of DMTs were similar.
Medication Possession Ratio did not fully correspond to medical files’ information.
Patients’ engagement with electronic monitoring devices needs cultivation.
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Multiple modality approach to assess adherence to medications across time in Multiple Sclerosis
Efrat Neter1, Anat Wolkowitz2, Lea Glass-Marmor 2, Idit Lavi3, Sharonne Ratzabi2, Izabella Leibkovitz2, Ariel Miller2,4
1 2
Ruppin Academic Center, Israel
Rappaport Faculty of Medicine & Research Institute, Technion Institute of Technology, Haifa, Israel 3
Department of Community Medicine & Epidemiology, Carmel Medical Center, Haifa, Israel
4
Multiple Sclerosis Center & Neuroimmunology Unit, Carmel Medical Center, Haifa, Israel
Corresponding author: Efrat Neter, [email protected];
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Abstract Background: Medication adherence is especially challenging in a chronic condition such as Relapsing Multiple Sclerosis (RMS). Medication adherence among persons with MS (PwMS) is usually assessed via a single measure, mostly electronic pharmacy records. Objectives: Assess medication adherence in multiple modes across time among PwMS; examine consistency across time and associations between measures. Methods: PwMS (N=194) were surveyed prospectively at three time points (baseline, 6 and 12 months later) and their health records and medication claims were retrospectively obtained. Adherence score was based on medication possession ratio (MPR) and two patient-reported outcome (PRO) measures. Electronic monitoring devices assessing medication adherence were also initiated. Results: MPR of each nonadherent PwMS, once compared to medical records containing prescription changes, was found as underestimating adherence. MPR was between the two PROs in identifying nonadherence and associations between the measures and across time was moderate (Kappa ranged 0.37-0.42). The use of electronic monitoring devices was not adopted by patients. A score indicated adherence as 66% and 64.9% at Time1 and Time 2, respectively, with 21.1% of PwMS nonadherent at both time points. Adherence did not vary significantly by DMT type. Conclusions: Being a dynamic behavior, medication adherence should be repeatedly monitored by using multiple modalities and focused on in clinician-patient encounters, especially in chronic diseases such as MS, which requires long-term treatments. Applying PROs in monitoring medication adherence would facilitate implementation of Participatory Medicine and patient-centered strategies in MS care.
Key words: Adherence, Medication claims, Multiple Sclerosis, Participatory
Medicine, PROs
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Effective treatments that modify the course of relapsing multiple sclerosis (RMS) are available (McGraw and Lublin, 2013). These Disease Modifying Treatments (DMTs) reduce the number of clinical relapses, MRI activity and seem to slow the progression of disability (Tintore et al., 2018). Oral formulations, which have been approved in recent years, increased anticipation for enhanced tolerability (Warrender-Sparkes et al., 2015) and consequently potential increased adherence (Menzin et al., 2013) as well as efficacy. However, efficacy depends, among others, on medication taking behavior; indeed, variability in the initiation, execution and persistence of medication adherence may explain the difference between the trialbased efficacy and ‘real-world’ effectiveness (Blaschke et al., 2012). Medication taking of DMTs is challenging in RMS: periods of disease inactivity may turn PwMS complacent on adherence (Bruce and Lynch, 2011), chronic symptoms are not reduced by DMTs while adverse side effects of DMTs reduce the quality of life among PwMS. Medication adherence is defined as the extent of correspondence between medication taking behavior and the recommendations made by the clinician with respect to the timing, dosage, and frequency (World Health Organization (WHO), 2003). Its measurement has been identified as one of the key challenges to the field (Molloy and O’Carroll, 2017). Indeed, the diversity in the measurement of adherence is often pinpointed as an explanation to the substantially varying nonadherence reported in reviews (Blaschke et al., 2012; Inauen et al., 2017). This paper will focus on assessing adherence among PwMS using multiple measures. Direct methods, often referred to as "objective", include therapeutic drug monitoring and measurements of the drug or a metabolite, thus providing an evidence of drug ingestion. The introduction of intravenous periodic infusion as a standard MS 4
treatment (e.g., Ocrelizumab, Natalizumab) may foster new findings on medication adherence via direct measures (Tintore et al., 2018). Prevalent indirect methods include both modalities considered objective such as electronic administrative databases and electronic monitoring devices, and modalities considered subjective such as patients' reports. Electronic administrative databases, the most often used in studying medication adherence to DMTs among PwMS (Bergvall et al., 2014; Eriksson et al., 2018; Teter et al., 2014; Wong et al., 2011), has shown adherence as moderate (e.g., two thirds at six months and less than half at two years after initiation) with oral medication usually yielding higher adherence than injectable DMTs (Hansen et al., 2015; Johnson et al., 2017; Warrender-Sparkes et al., 2015), the former ranging between 60-80% adherence at 1year follow-up. Few studies on DMTs among PwMS reported higher adherence (Agashivala et al., 2013; Evans et al., 2016; Hao et al., 2017), hovering at 75-90% among PwMS who are adherent 80% of the time. Electronic Monitoring Devices (EMD) are either a box or a bottle or an injection containing medication which transmits a signal upon its opening or activation, considered a necessary precursor to consumption (Williams et al., 2013). Few medication interventions among PwMS employed this method (Fernández et al., 2016; Yeh et al., 2017; Zecca et al., 2017). Lastly, patient-reports scales have been scarcely used among PwMS in recent years (Bruce et al., 2010; Hao et al., 2017; Warrender-Sparkes et al., 2015; Zecca et al., 2017). Studies have compared the different measurement (Dima et al., 2017; Inauen et al., 2017; Moran et al., 2017a; Phillips et al., 2013; Zecca et al., 2017) in terms of convergent validity and predictive validity of outcomes in different disease (Williams
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et al., 2013). With exceptions (Zecca et al., 2017), the association between measures is mostly moderate and not always consistent. Moreover, there is considerable withinperson variation over time (Inauen et al., 2017). The conclusions that emerge from most such studies is that there is no standard method to evaluate adherence but rather that each method gathers different aspects of the situation and has its strengths and weaknesses. Hence, the recommendation is to use multiple measures. Few adherence studies have used multiple measures of adherence among PwMS (Bruce et al., 2010; Hao et al., 2017; Yeh et al., 2017; Zecca et al., 2017). It is therefore the objectives of this paper to (1) compare adherence rates as reported by various measures/methods and to examine association between measures of adherence; specifically, we used electronic pharmacy records, patient-reported scales, and EMD. (2) Compare adherence rates across time; and (3) Compare adherence rates in various methods among PwMS to two administration routes of DMTs – oral and self-injected.
Methods Participants PwMS treated with DMTs and followed at Carmel MS Center in Haifa, Israel: 194 at baseline and 6 months later (Time 1) and 154 at 12 months (Time 2) since baseline. Participants' selection and analysis (Time 1 and Time 2) is described at Table 1, where the exclusion criteria are specified. Design and Procedure A prospective observational study design was used. The data analyzed were collected as part of a broader, on-going research study investigating adherence of PwMS to regularly prescribed DMTs. Between February 2016 and August 2018, data were 6
collected in a large single-center. The survey and neurological evaluation were administered at baseline, 6 months (Time 1, median length of 6.8 months) and 12 months later (Time 2, median length of 6.4 months from Time 1). Medication possession data were retrieved retrospectively for the same periods. The study was approved by an Internal Review Board and registered (clinical trials registry #NCT02488343). All participants were provided written informed consent confirming that they were free to leave the study at any time. Measures Medication withdrawal records were retrieved from the computerized dataset of 'Clalit Health Services'; these were available for 118 PwMS in the prospective study who are members of this Health Maintenance Organization (HMO) and not for 53 PwMS treated at the clinic yet are members of other HMOs. Medication Possession Ratio (MPR) was computed for each PwMS based on her/his medication type and the initial prescription: it was estimated as the total days with index medication supply within the refill interval divided by the number of days between the first prescription data and the last prescription date. Using the commonly accepted threshold of MPR ≥ 80% (Andrade et al., 2006; Hess et al., 2006), PwMS were considered adherent if they were above the threshold and non-adherent when they were below this threshold. Patient-reported outcomes measures - Multiple Sclerosis Treatment Adherence Questionnaire (MS-TAQ; (Wicks et al., 2011)) and Probabilistic Medication Adherence Scale (ProMAS; (Kleppe et al., 2015)). The ProMAS is an overall estimation 18-item questionnaire tapping adherence behaviors (e.g., "I have never changed my medicine use myself", "When I am away from home, I occasionally do not take my medicines") to which respondents indicate 'yes, true' (coded as 1) or 'no, not true' (coded as 0). Higher individual's adherence scores represent better adherence 7
rates. Adherence categories are low (sum score 0-4), medium low (sum score 5-9), medium-high (sum score 10-14) and high (sum score 15-18). Internal reliabilities of the ProMas were baseline=0.87, Time 1=0.82 and Time 2=0.84. The items from MS-TAQ used in this analysis tapped whether the participant did not take a prescribed dose in the last four weeks and the reported number of these doses. In cases of reported non-adherence, the percentage was calculated per regiment. Additionally, patients were asked to bring empty blisters of oral medications to the clinic. A score of adherence, comprising of MPR and PROs (i.e., MS-TAQ and ProMAS), was computed so that good adherence was defined as either => 80% medication claims per regiment (medication possession ratio (MPR)), or => 80% self-reported medication use by MS-TAQ or being at the medium-high and high categories of ProMAS. Low adherence was defined as the complement. Two EMDs were used. The first is the self-injected interferon beta-1a which recorded each use of an injection in terms of timing and dosage (www.msdialog.com). The second EMD was an electronic box for the oral medications, constructed especially for this study so as to fit the size of the blisters (24X19X5.5 centimeters). In order to refrain from intervention (at this observational stage), the boxes dispensed to patients only recorded their opening and did not include other optional features such as timed encouragement messaging or feedback to the patient. Data analysis Descriptive analyses for demographics and background characteristics were conducted and reported for all the prospective study participants. For categorical
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variables, counts and percentages are provided whereas means and standard deviations (SDs) are presented for continuous variables. Percentage of adherence, as measured by MPR, was calculated for the four medications under study. Subsequently, every non-adherent PwMS was checked in medical files, to correct for possible regiment change, and initial and corrected MPR per medication are presented. Adherence, as assessed by MPR (corrected) and PRO measures, is presented across Time 1 and Time 2 and a score of adherence, based on both MPR and PRO, was constructed such that non-adherence was defined as either detected/reported by one of the PRO or MPR. Additionally, measures of association and agreement were calculated both between measures at the same time point and across times (Pearson, Kappa and inter-class correlation). Finally, adherence to oral and self-injected medication was compared by means of a chi-square test.
Results
Participants’ characteristics Table 2 presents the characteristics of the participants, as measured at baseline. As can be seen, two-thirds of the participants were women, the mean age was 40.6 (SD=13.8), most participants were of Jewish descent, the majority of them attained higher education and described their social economic situation as average or above. Their physical disability score was relatively low (M=2.6, SD=2.0) and the majority described their health as average and above average. The mean MS duration was M= 7.8 (SD=7.2). Adherence as assessed by different measures and across time
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MPR for the four medications under study are presented in Table 3, both initial figures and corrected, the latter following examination of the medical files of each non-adherent person. As can be seen, at Time1 the differences between the initial and corrected MPR are considerable. A close examination of the changes uncovered that the majority of participants did not change medication (n=109, 67.3%), but about a third of PwMS switched medication (n=47, 29.0%) and a few had the dosage dropped (n=5, 3.1%). In one case the research team had informal information on cessation. Those who switched medication were adherent till they were instructed by the physician to halt the current medication; each such case was ascertained by close inspection of the medical files and these corrections constitute most the MPR corrections. The differences at Time 2 between the initial and corrected MPR were smaller or null (for glatiramer acetate). The reasons for modifications (switched medication or decrease in dosage) were the following: side effects reported by the PwMS (n=23), planning of pregnancy (12), relapse (n=10) and non-tolerability reported by patients (n=6). Adherence rates, as assessed by MPR and the two PROs (MS-TAQ and ProMas), were then compared, collapsing across the different medications. Data is presented in Table 4, indicating very high adherence by MS-TAQ (~96% at both time points), adherence rates located at the desired standard of 80% by MPR (80.5% at Time 1 and 81.7% at Time 2), and less than the standard of 80% adherence by ProMas (72.3% at Time 1 and 69.9% at Time 2). Adherence data as measured by EMD was available only for interferon beta 1a and for very few PwMS. As EMD data for interferon beta 1a, reflecting use of injections and available to the clinic through a web site, existed only for 10 PwMS and only for 5 PwMS at both Time 1 and Time 2, no statistical analyses were pursued. The EMD 10
in the form of electronic boxes were not adopted by participants; PwMS complained that the boxes were too big and did not suit carrying outside of their homes; that they required electrical contact and were thus ill-suited for the workplace; and that children at home asked about the boxes. The use of this EMD was discontinued. Consistency across times (Time 1 and Time 2) within each measure was examined by computing a Pearson correlation (for continuous data) and Kappa (for categorical data). ProMas had the highest consistency (r=0.68, p<0.001; 109/139 consistency among PwMS across times, 84.2% of agreement, Kappa=0.47, p<0.001; interclass correlation=0.80, p <0.001), with MPR following (85/102 consistency among PwMS across times, 83.3% total agreement, Kappa=0.44, p<0.001); MS-TAQ showed high consistency across times in terms of total agreement (r=0.41; 137/147 consistency among PwMS across times, 93.2% total agreement). Finally, the score of adherence was the same at Time 1 and Time 2 (either indicating adherence or nonadherence) at 100/151 of the PwMS (72.8%), Kappa=0.40, p=<0.001). Table 4 displays adherence (N and %) across time and measure, as well as total agreement in each measure across time (i.e. consistency identifying people as either adherence or on-adherent) and percentage of PwMS consistently identified as non-adherent. Consistency between the different measures was also examined. ProMas and MSTAQ were significantly associated, though moderately (r=0.30 and 0.42, p's<.05, time2 and time3, respectively). These measures were at 73.1% of the instances in agreement at Time 1 and 69.6% agreement at Time 2, Kappa=0.42, p <.001 and Kappa=0.38, p <.001, respectively. The PROs and MPR were also associated, being at agreement on adherence in the range of 74.5%-84.7% of the instances. Specifically, ProMas and MPR at Time 1 had 76.7.1% agreement, Kappa=0.37, p <.0001; ProMas and MPR at Time 2 had 74.5% agreement, Kappa=0.26, p=.0007. MS-TAQ and 11
MPR at Time 1: 79.5% agreement, Kappa=0.07, p=.245; MS-TAQ and MPR at Time 2: 84.7% agreement, Kappa=0.23, p =.007). Adherence across types of medication and routes of administrations (selfinjected vs. oral) A score of adherence, comprising of MPR and PROs, was computed (see methods). Adherence was computed at each time points for each type of medication and displayed at Table 5. A chi-square test was conducted to compare adherence between the DMT types. There was no significant difference between the different medications at Time 1 and Time 2, χ 2 (3) = 2.29, p=0.515, and χ 2 (3) = 0.64, p=0.887, respectively, nor was a significant difference when self-injected medications were compared to oral medications. Higher adherence in the oral route of administration can be seen at both time points, yet the difference was non-significant (χ 2 (1) = 0.60 and 0.14 at Time 1 and Time 2, and p=0.437 and 0.705, respectively).
Discussion This analysis of multiple measures of adherence among PwMS yielded the following major findings. First, no significant differences in adherence rates were found between the DMTs types at both time points. This result departs from some previous findings (Bergvall et al., 2014; Devonshire et al., 2011; Menzin et al., 2013) yet similar to other results (Hansen, Evans, 2016). Relatedly, adherence rates found in this study fall within the range uncovered in other studies (Conway et al., 2018; Devonshire et al., 2011; Evans et al., 2016; Hansen et al., 2015; Menzin et al., 2013) and may reflect the relatively short time of one-year follow-up. Secondly, different measures yielded different adherence levels. This finding resemble prior work (Dima et al., 2017; Molloy and O’Carroll, 2017; Moran et al., 12
2017b; Phillips et al., 2013). Few studies on adherence among PwMS employed multiple measures (Johnson et al., 2017; Yeh et al., 2017) and these also documented variance among measures (Yeh et al., 2017). Interestingly, a PRO, specifically ProMas, used for the first time among PwMS, detected more nonadherence than MPR by tapping nuanced, subtle behaviors surrounding adherence. The scale affords clinicians to discuss specific behaviors surrounding nonadherence with PwMS. Relatedly, the introduced index of adherence, based on identifying nonadherence by either of the measures, afforded the identification of more nonadherent PwMs who were then targeted for counselling. Thirdly, only moderate levels of agreement were found between the different measures (70-85% total agreement) and somewhat higher total agreement were recorded within the same measure across time (72-93% total agreement), indicating that medication taking behavior among 25% -28% of PwMS varies across time. Relatedly, identifying those who are consistently nonadherent, around 20% of PwMS when using the more sensitive measure (i.e., ProMas), is a priority, as these patients do not benefit fully from their treatment. The fourth finding emerging from the study is that the indicator of MPR does not fully reflect the 'real life' situation: raw MPRs adherence rates were lower than corrected MPSs, which were based on PwMS’ medical files. It seems time elapses before revised regimen are updated, and in cases of switching medication (which requires a period of no medication taking) MPR does not fully reflect adherence. It should be noted that MPR corrections were smaller at Time 2 (than at Time 1), possibly reflecting stabilization in medication taking behavior. Lastly, regime modifications, such as dosage change, may reflect a compromise struck between the clinician and PwMS, departing from the intended or “ought” dosage. 13
The final major finding pertains to EMDs. PwMS did not adopt the provided electronic boxes and their reservations indicated that the device was ill-suited to their needs and did not provide them any benefit. Retrospectively, PwMS should have taken part in the construction of this EMD, both in the stage of preference elicitation and in testing, so that difficulties with the specific EMD could have been addressed. Similarly, the RebifSmart electronic device attached to IFN β1 was activated so as to transmit the information only among a minority of PwMS and fewer had it operational a year after initiation. Our findings depart from previous work (Fernández et al., 2016) where the EMD device was fully operational. This clearly indicates that patient training on the operation of these devices is important in routine clinical care and issues of training resources need to be addressed. Implications Our study indicates that MPR data should be considered with some caution. The measure is usually viewed in the literature as overestimating adherence (Hess et al., 2006) yet an examination of each nonadherent patient’s file, usually not carried out, indicated MPR is an underestimation of adherence, as pharmacy data of the HMO was not promptly updated to account for modifications in prescriptions. The most important implication arising from our findings is that adherence should be measured in multiple ways, as recommended in the literature (Williams et al., 2013). Moreover, it should be assessed repeatedly, and then addressed in clinical encounters with patients (Kronish and Moise, 2017). Repeated assessments are critical in this chronic condition, as adherence was demonstrated to change over time: many people around 30% - did not behave the same across time and being adherent at one point did not guarantee it will stay so. Consistency across time in adherence is important in
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treating MS with DMTs; guidelines of scientific associations (Rae-Grant et al., 2018) posit that treatment needs to continue over a long term to gain benefits. Uncovering nonadherence is the first step to explore its reasons and search, jointly with the patient, for suitable remedies.
Limitations and strengths The current work has several limitations. First, the study is based on a relatively small sample located in one clinic. Second, follow-up time was currently 1 year only. Third, the planned EMDs were not adopted by PwMS in our study and clearly warrant more care in design, training and maintenance. The strengths of the study are that it employed several measures of adherence, including a recent PRO covering a wide range of adherence behaviors (Kleppe et al., 2015), and that each non-adherent case was examined closely: the medical file was examined so as to verify nonadherence and the patient was later referred to intervention focusing on nonadherence.
In conclusion, this prospective longitudinal study of PwMS assessed adherence using PROs coupled with retrospective analysis of medication claims. The multiple measurement uncovered medication taking among MS patients at a moderate level and as a changing, dynamic behavior. Hence, it is imperative to continuously assess medication adherence in multiple ways and address it repeatedly in clinician-patient encounters. Additionally, this study supports the notion that applying PROs in monitoring medication adherence would facilitate implementation of Participatory Medicine and patient-centered strategies in MS care (Lejbkowicz et al., 2012). 15
Disclosures
Funding/Support: This work was supported in part by Novartis Pharmaceuticals, Biogen Idec and Merck Serono. Financial Disclosures: Dr. Miller has served on the scientific advisory board, and received personal compensation for consulting and/or speaking activities and/ or honoraria and/or received grant support for research from: Avanir Pharmaceuticals; Bayer-Schering Pharma; Biogen Idec; Mapi Pharma; Medison Pharma Ltd.; Merck Serono; Novartis, Sanofi-Genzyme and Teva Pharmaceutical Industries Ltd.
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Assessing adherence to multiple medications and in daily life among patients with multimorbidity. Psychol. Health 0446, 1–16. https://doi.org/10.1080/08870446.2016.1275632 Johnson, K.M., Zhou, H., Lin, F., Ko, J.J., Herrera, V., 2017. Real-world adherence and persistence to oral disease-modifying therapies in multiple sclerosis patients over 1 year. J. Manag. Care Spec. Pharm. 23, 844–852. https://doi.org/10.18553/jmcp.2017.23.8.844 Kleppe, M., Lacroix, J., Ham, J., Midden, C., 2015. The development of the ProMAS: a Probabilistic Medication Adherence Scale. Patient Prefer. Adherence 9, 355– 367. https://doi.org/Doi 10.2147/Ppa.S76749 Kronish, I.M., Moise, N., 2017. In Search of a “Magic Pill” for Medication Nonadherence. JAMA Intern. Med. 5, 631–632. https://doi.org/10.1001/jamainternmed.2016.9658 Lejbkowicz, I., Caspi, O., Miller, A., 2012. Participatory medicine and patient empowerment towards personalized healthcare in multiple sclerosis. Expert Rev. Neurother. 12, 343–52. https://doi.org/10.1586/ern.11.161 McGraw, C.A., Lublin, F.D., 2013. Interferon Beta and Glatiramer Acetate Therapy. Neurotherapeutics 10, 2–18. https://doi.org/10.1007/s13311-012-0163-4 Menzin, J., Caon, C., Nichols, C., White, L.A., Friedman, M., Pill, M.W., 2013. Narrative Review of the Literature on Adherence to Disease-Modifying Therapies Among Patients with Multiple Sclerosis. J. Manag. Care Pharm. 19, S24–S40. https://doi.org/10.18553/jmcp.2013.19.s1.S24 Molloy, G.J., O’Carroll, R.E., 2017. Medication adherence across the lifespan: Theory, methods, interventions and six grand challenges. Psychol. Heal. 32, 1169–1175. https://doi.org/10.1080/08870446.2017.1316850 Moran, C., Doyle, F., Sulaiman, I., Bennett, K., Greene, G., Molloy, G.J., Reilly, R.B., Costello, R.W., Mellon, L., 2017a. The INCA(TM) (Inhaler Compliance Assessment(TM)): A comparison with established measures of adherence. Psychol. Health 0446, 1–21. https://doi.org/10.1080/08870446.2017.1290243 Moran, C., Doyle, F., Sulaiman, I., Bennett, K., Greene, G., Molloy, G.J., Reilly, R.B., Costello, R.W., Mellon, L., 2017b. The INCATM (Inhaler Compliance Assessment TM): A comparison with established measures of adherence. Psychol. Health 32, 1266–1287. https://doi.org/10.1080/08870446.2017.1290243 Phillips, A.L., Leventhal, H., Leventhal, E.A., 2013. Assessing theoretical predictors of long-term medication adherence: Patients’ treatment-related beliefs, experiential feedback and habit development. Psychol. Heal. 28, 1135–1151. https://doi.org/10.1080/08870446.2013.793798 Rae-Grant, A., Day, G.S., Marrie, R.A., Rabinstein, A., Cree, B.A.C., Gronseth, G.S., Haboubi, M., Halper, J., Hosey, J.P., Jones, D.E., Lisak, R., Pelletier, D., Potrebic, S., Sitcov, C., Sommers, R., Stachowiak, J., Getchius, T.S.D., Merillat, S.A., Pringsheim, T., 2018. Practice guideline recommendations summary: Disease-modifying therapies for adults with multiple sclerosis: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology. Neurology 90, 777–788. 18
https://doi.org/10.1212/WNL.0000000000005347 Teter, B., Agashivala, N., Kavak, K., Chouhfeh, L., Hashmonay, R., WeinstockGuttman, B., 2014. Characteristics influencing therapy switch behavior after suboptimal response to first-line treatment in patients with multiple sclerosis. Mult. Scler. J. 20, 830–836. https://doi.org/10.1177/1352458513513058 Tintore, M., Vidal-Jordana, A., Sastre-Garriga, J., 2018. Treatment of multiple sclerosis — success from bench to bedside. Nat. Rev. Neurol. 1–3. https://doi.org/10.1038/s41582-018-0082-z Warrender-Sparkes, M., Spelman, T., Izquierdo, G., Trojano, M., Lugaresi, A., Grand’Maison, F., Havrdova, E., Horakova, D., Boz, C., Oreja-Guevara, C., Alroughani, R., Iuliano, G., Duquette, P., Girard, M., Terzi, M., Hupperts, R., Grammond, P., Petersen, T., Fernandez-Bolanos, R., Fiol, M., Pucci, E., Lechner-Scott, J., Verheul, F., Cristiano, E., Van Pesch, V., Petkovska-Boskova, T., Moore, F., Kister, I., Bergamaschi, R., Saladino, M.L., Slee, M., Barnett, M., Amato, M.P., Shaw, C., Shuey, N., Young, C., Gray, O., Kappos, L., Butzkueven, H., Kalincik, T., Jokubaitis, V., 2015. The effect of oral immunomodulatory therapy on treatment uptake and persistence in multiple sclerosis. Mult. Scler. 22, 520–532. https://doi.org/10.1177/1352458515594041 Wicks, P., Massagli, M., Kulkarni, A., Dastani, H., 2011. Use of an online community to develop patient-reported outcome instruments: The multiple sclerosis treatment adherence questionnaire (MS-TAQ). J. Med. Internet Res. 13. https://doi.org/10.2196/jmir.1687 Williams, A.B., Amico, K.R., Bova, C., Womack, J.A., 2013. A proposal for quality standards for measuring medication adherence in research. AIDS Behav. 17, 284–297. https://doi.org/10.1007/s10461-012-0172-7 Wong, J., Gomes, T., Mamdani, M., Manno, M., O’Connor, P.W., 2011. Adherence to multiple sclerosis disease-modifying therapies in Ontario is low. Can. J. Neurol. Sci. 38, 429–433. World Health Organization (WHO), 2003. Adherence to long-term therapies; Evidence for action, World Health Organisation. Yeh, E.A., Grover, S.A., Powell, V.E., Alper, G., Banwell, B.L., Edwards, K., Gorman, M., Graves, J., Lotze, T.E., Mah, J.K., Mednick, L., Ness, J., Obadia, M., Slater, R., Waldman, A., Waubant, E., Schwartz, C.E., 2017. Impact of an electronic monitoring device and behavioral feedback on adherence to multiple sclerosis therapies in youth: results of a randomized trial. Qual. Life Res. 26, 2333–2349. https://doi.org/10.1007/s11136-017-1571-z Zecca, C., Disanto, G., Mühl, S., Gobbi, C., 2017. Subjective patient-reported versus objective adherence to subcutaneous interferon β-1a in multiple sclerosis using RebiSmart®: The CORE study. BMC Neurol. 17, 1–5. https://doi.org/10.1186/s12883-017-0952-9
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Table 1. Enrollment of Participants
Participants recruitment (n=226)
Excluded (n=32): Not meeting criteria DMT > 1 Year
Participants screening for eligibility at baseline (n=194)
Time 1 follow-up (n=194)
2
Language literacy
14
Declined to participate
2
Cognitive impairment
3
Moved to another clinic
8
Discontinued treatment (pregnancy intention, side effects)
3
Not analyzed at Time 2 follow-up:
Time 2 follow-up (n=154) Analyzed at Time 2 (n=146)
20
No Time 2 data yet
40
Switched to non-study DMT
3
Lost to follow up
5
Table 2. Baseline demographic and clinical characteristics (N=194) Age, Mean (SD)
40.6 (13.8)
Gender, N (%) Male Female
53 (27.3) 120 (72.7)
Ethnicity Jewish
137 (70.6%)
PCI
52 (26.8)
Other
5 (2.6%)
Marital Status, Married
117 (60.3)
Education Secondary
59 (30.4)
Post-secondary
31 (16.0)
Tertiary
104 (53.6)
Social Economic Status Low
14 (7.4)
Average and above
176 (92.6)
Comorbidity Yes
38 (19.6)
No
140 (72.2)
Do not know
8 (4.1)
Physical disability (EDSS, time 0) MS duration, in years
2.60 (2.0) 7.81 (7.2)
EDSS: Expanded Disability Status Scale;
21
Table 3. Medication possession ratio (MPR) across times (n=118): raw adherence (=>80%) and corrected adherence based on medical files; n adherent/n* and (%) MPR
IFN
GA
Fingolimod
Teriflunomide
Time1, raw MPR
30/48 (62.5)
10/14 (71.4)
9/24 (37.5)
26/55 (47.3)
Time1, corrected MPR
36/48 (70.5)
13/14 (92.9)
17/24 (70.8)
49/55 (89.1)
Time2, raw MPR
20/31 (64.5)
10/10 (100.0) 9/16 (56.3)
39/49 (79.6)
Time2, corrected MPR
22/31 (71.0)
10/10 (100.0) 11/16 (68.8)
44/49 (89.8)
*note that MPR data is available for only part of the PwMS participating in the study IFN: interferon beta 1a; GA: glatiramer acetate
22
Table 4. Adherence (n, %) across time, as assessed by MPR, PROM and Score
Measure
Time1
Time2
% total agreement
% non-adherent
MPR
107 (80.5)
85 (81.7)
Time 1 – Time 2
at both Time 1 and Time2
83.3
9.8
MS-TAQ
186 (96.4)
139 (93.9)
93.2
0.6
ProMas
136 (72.3)
102 (69.9)
84.2
18.0
Adherence Score*
128 (66.0)
100 (64.9)
72.8
21.1
* score is based on nonadherence on either of the above measures
23
Table 5. Score of Adherence across time by DMT type; adherent/n in medication (%)
IFN
GA
Fingolimod
Teriflunomide
Time 1 (n=188)
42/67 (62.7)
14/21 (66.7)
22/36 (61.1)
47/64 (73.4)
Time 2 (n=146)
28/42 (66.7)
13/20 (65.0)
15/26 (57.7)
38/58 (65.5)
Oral
Self-Injected
Time1 (n=188)
69/100 (69.0)
56/88 (63.6)
Time 2 (n=146)
53/84 (63.1)
41/62 (66.1)
IFN: interferon beta 1a; GA: glatiramer acetate
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Multiple modality approach to assess adherence to medications across time in Multiple Sclerosis Efrat Neter , Anat Wolkowitz , Lea Glass-Marmor , Idit Lavi , Sharonne Ratzabi , Izabella Leibkovitz , Ariel Miller PII: DOI: Reference:
S2211-0348(20)30027-4 https://doi.org/10.1016/j.msard.2020.101951 MSARD 101951
To appear in:
Multiple Sclerosis and Related Disorders
Received date: Revised date: Accepted date:
2 October 2019 1 November 2019 12 January 2020
Please cite this article as: Efrat Neter , Anat Wolkowitz , Lea Glass-Marmor , Idit Lavi , Sharonne Ratzabi , Izabella Leibkovitz , Ariel Miller , Multiple modality approach to assess adherence to medications across time in Multiple Sclerosis, Multiple Sclerosis and Related Disorders (2020), doi: https://doi.org/10.1016/j.msard.2020.101951
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HIGHLIGHTS
One of few studies in MS employing multiple measures in medication adherence.
Different measures of medication adherence yielded different adherence levels.
Adherence rates to different administration routes of DMTs were similar.
Medication Possession Ratio did not fully correspond to medical files’ information.
Patients’ engagement with electronic monitoring devices needs cultivation.
1
Multiple modality approach to assess adherence to medications across time in Multiple Sclerosis
Efrat Neter1, Anat Wolkowitz2, Lea Glass-Marmor 2, Idit Lavi3, Sharonne Ratzabi2, Izabella Leibkovitz2, Ariel Miller2,4
1 2
Ruppin Academic Center, Israel
Rappaport Faculty of Medicine & Research Institute, Technion Institute of Technology, Haifa, Israel 3
Department of Community Medicine & Epidemiology, Carmel Medical Center, Haifa, Israel
4
Multiple Sclerosis Center & Neuroimmunology Unit, Carmel Medical Center, Haifa, Israel
Corresponding author: Efrat Neter, [email protected];
2
Abstract Background: Medication adherence is especially challenging in a chronic condition such as Relapsing Multiple Sclerosis (RMS). Medication adherence among persons with MS (PwMS) is usually assessed via a single measure, mostly electronic pharmacy records. Objectives: Assess medication adherence in multiple modes across time among PwMS; examine consistency across time and associations between measures. Methods: PwMS (N=194) were surveyed prospectively at three time points (baseline, 6 and 12 months later) and their health records and medication claims were retrospectively obtained. Adherence score was based on medication possession ratio (MPR) and two patient-reported outcome (PRO) measures. Electronic monitoring devices assessing medication adherence were also initiated. Results: MPR of each nonadherent PwMS, once compared to medical records containing prescription changes, was found as underestimating adherence. MPR was between the two PROs in identifying nonadherence and associations between the measures and across time was moderate (Kappa ranged 0.37-0.42). The use of electronic monitoring devices was not adopted by patients. A score indicated adherence as 66% and 64.9% at Time1 and Time 2, respectively, with 21.1% of PwMS nonadherent at both time points. Adherence did not vary significantly by DMT type. Conclusions: Being a dynamic behavior, medication adherence should be repeatedly monitored by using multiple modalities and focused on in clinician-patient encounters, especially in chronic diseases such as MS, which requires long-term treatments. Applying PROs in monitoring medication adherence would facilitate implementation of Participatory Medicine and patient-centered strategies in MS care.
Key words: Adherence, Medication claims, Multiple Sclerosis, Participatory
Medicine, PROs
3
Effective treatments that modify the course of relapsing multiple sclerosis (RMS) are available (McGraw and Lublin, 2013). These Disease Modifying Treatments (DMTs) reduce the number of clinical relapses, MRI activity and seem to slow the progression of disability (Tintore et al., 2018). Oral formulations, which have been approved in recent years, increased anticipation for enhanced tolerability (Warrender-Sparkes et al., 2015) and consequently potential increased adherence (Menzin et al., 2013) as well as efficacy. However, efficacy depends, among others, on medication taking behavior; indeed, variability in the initiation, execution and persistence of medication adherence may explain the difference between the trialbased efficacy and ‘real-world’ effectiveness (Blaschke et al., 2012). Medication taking of DMTs is challenging in RMS: periods of disease inactivity may turn PwMS complacent on adherence (Bruce and Lynch, 2011), chronic symptoms are not reduced by DMTs while adverse side effects of DMTs reduce the quality of life among PwMS. Medication adherence is defined as the extent of correspondence between medication taking behavior and the recommendations made by the clinician with respect to the timing, dosage, and frequency (World Health Organization (WHO), 2003). Its measurement has been identified as one of the key challenges to the field (Molloy and O’Carroll, 2017). Indeed, the diversity in the measurement of adherence is often pinpointed as an explanation to the substantially varying nonadherence reported in reviews (Blaschke et al., 2012; Inauen et al., 2017). This paper will focus on assessing adherence among PwMS using multiple measures. Direct methods, often referred to as "objective", include therapeutic drug monitoring and measurements of the drug or a metabolite, thus providing an evidence of drug ingestion. The introduction of intravenous periodic infusion as a standard MS 4
treatment (e.g., Ocrelizumab, Natalizumab) may foster new findings on medication adherence via direct measures (Tintore et al., 2018). Prevalent indirect methods include both modalities considered objective such as electronic administrative databases and electronic monitoring devices, and modalities considered subjective such as patients' reports. Electronic administrative databases, the most often used in studying medication adherence to DMTs among PwMS (Bergvall et al., 2014; Eriksson et al., 2018; Teter et al., 2014; Wong et al., 2011), has shown adherence as moderate (e.g., two thirds at six months and less than half at two years after initiation) with oral medication usually yielding higher adherence than injectable DMTs (Hansen et al., 2015; Johnson et al., 2017; Warrender-Sparkes et al., 2015), the former ranging between 60-80% adherence at 1year follow-up. Few studies on DMTs among PwMS reported higher adherence (Agashivala et al., 2013; Evans et al., 2016; Hao et al., 2017), hovering at 75-90% among PwMS who are adherent 80% of the time. Electronic Monitoring Devices (EMD) are either a box or a bottle or an injection containing medication which transmits a signal upon its opening or activation, considered a necessary precursor to consumption (Williams et al., 2013). Few medication interventions among PwMS employed this method (Fernández et al., 2016; Yeh et al., 2017; Zecca et al., 2017). Lastly, patient-reports scales have been scarcely used among PwMS in recent years (Bruce et al., 2010; Hao et al., 2017; Warrender-Sparkes et al., 2015; Zecca et al., 2017). Studies have compared the different measurement (Dima et al., 2017; Inauen et al., 2017; Moran et al., 2017a; Phillips et al., 2013; Zecca et al., 2017) in terms of convergent validity and predictive validity of outcomes in different disease (Williams
5
et al., 2013). With exceptions (Zecca et al., 2017), the association between measures is mostly moderate and not always consistent. Moreover, there is considerable withinperson variation over time (Inauen et al., 2017). The conclusions that emerge from most such studies is that there is no standard method to evaluate adherence but rather that each method gathers different aspects of the situation and has its strengths and weaknesses. Hence, the recommendation is to use multiple measures. Few adherence studies have used multiple measures of adherence among PwMS (Bruce et al., 2010; Hao et al., 2017; Yeh et al., 2017; Zecca et al., 2017). It is therefore the objectives of this paper to (1) compare adherence rates as reported by various measures/methods and to examine association between measures of adherence; specifically, we used electronic pharmacy records, patient-reported scales, and EMD. (2) Compare adherence rates across time; and (3) Compare adherence rates in various methods among PwMS to two administration routes of DMTs – oral and self-injected.
Methods Participants PwMS treated with DMTs and followed at Carmel MS Center in Haifa, Israel: 194 at baseline and 6 months later (Time 1) and 154 at 12 months (Time 2) since baseline. Participants' selection and analysis (Time 1 and Time 2) is described at Table 1, where the exclusion criteria are specified. Design and Procedure A prospective observational study design was used. The data analyzed were collected as part of a broader, on-going research study investigating adherence of PwMS to regularly prescribed DMTs. Between February 2016 and August 2018, data were 6
collected in a large single-center. The survey and neurological evaluation were administered at baseline, 6 months (Time 1, median length of 6.8 months) and 12 months later (Time 2, median length of 6.4 months from Time 1). Medication possession data were retrieved retrospectively for the same periods. The study was approved by an Internal Review Board and registered (clinical trials registry #NCT02488343). All participants were provided written informed consent confirming that they were free to leave the study at any time. Measures Medication withdrawal records were retrieved from the computerized dataset of 'Clalit Health Services'; these were available for 118 PwMS in the prospective study who are members of this Health Maintenance Organization (HMO) and not for 53 PwMS treated at the clinic yet are members of other HMOs. Medication Possession Ratio (MPR) was computed for each PwMS based on her/his medication type and the initial prescription: it was estimated as the total days with index medication supply within the refill interval divided by the number of days between the first prescription data and the last prescription date. Using the commonly accepted threshold of MPR ≥ 80% (Andrade et al., 2006; Hess et al., 2006), PwMS were considered adherent if they were above the threshold and non-adherent when they were below this threshold. Patient-reported outcomes measures - Multiple Sclerosis Treatment Adherence Questionnaire (MS-TAQ; (Wicks et al., 2011)) and Probabilistic Medication Adherence Scale (ProMAS; (Kleppe et al., 2015)). The ProMAS is an overall estimation 18-item questionnaire tapping adherence behaviors (e.g., "I have never changed my medicine use myself", "When I am away from home, I occasionally do not take my medicines") to which respondents indicate 'yes, true' (coded as 1) or 'no, not true' (coded as 0). Higher individual's adherence scores represent better adherence 7
rates. Adherence categories are low (sum score 0-4), medium low (sum score 5-9), medium-high (sum score 10-14) and high (sum score 15-18). Internal reliabilities of the ProMas were baseline=0.87, Time 1=0.82 and Time 2=0.84. The items from MS-TAQ used in this analysis tapped whether the participant did not take a prescribed dose in the last four weeks and the reported number of these doses. In cases of reported non-adherence, the percentage was calculated per regiment. Additionally, patients were asked to bring empty blisters of oral medications to the clinic. A score of adherence, comprising of MPR and PROs (i.e., MS-TAQ and ProMAS), was computed so that good adherence was defined as either => 80% medication claims per regiment (medication possession ratio (MPR)), or => 80% self-reported medication use by MS-TAQ or being at the medium-high and high categories of ProMAS. Low adherence was defined as the complement. Two EMDs were used. The first is the self-injected interferon beta-1a which recorded each use of an injection in terms of timing and dosage (www.msdialog.com). The second EMD was an electronic box for the oral medications, constructed especially for this study so as to fit the size of the blisters (24X19X5.5 centimeters). In order to refrain from intervention (at this observational stage), the boxes dispensed to patients only recorded their opening and did not include other optional features such as timed encouragement messaging or feedback to the patient. Data analysis Descriptive analyses for demographics and background characteristics were conducted and reported for all the prospective study participants. For categorical
8
variables, counts and percentages are provided whereas means and standard deviations (SDs) are presented for continuous variables. Percentage of adherence, as measured by MPR, was calculated for the four medications under study. Subsequently, every non-adherent PwMS was checked in medical files, to correct for possible regiment change, and initial and corrected MPR per medication are presented. Adherence, as assessed by MPR (corrected) and PRO measures, is presented across Time 1 and Time 2 and a score of adherence, based on both MPR and PRO, was constructed such that non-adherence was defined as either detected/reported by one of the PRO or MPR. Additionally, measures of association and agreement were calculated both between measures at the same time point and across times (Pearson, Kappa and inter-class correlation). Finally, adherence to oral and self-injected medication was compared by means of a chi-square test.
Results
Participants’ characteristics Table 2 presents the characteristics of the participants, as measured at baseline. As can be seen, two-thirds of the participants were women, the mean age was 40.6 (SD=13.8), most participants were of Jewish descent, the majority of them attained higher education and described their social economic situation as average or above. Their physical disability score was relatively low (M=2.6, SD=2.0) and the majority described their health as average and above average. The mean MS duration was M= 7.8 (SD=7.2). Adherence as assessed by different measures and across time
9
MPR for the four medications under study are presented in Table 3, both initial figures and corrected, the latter following examination of the medical files of each non-adherent person. As can be seen, at Time1 the differences between the initial and corrected MPR are considerable. A close examination of the changes uncovered that the majority of participants did not change medication (n=109, 67.3%), but about a third of PwMS switched medication (n=47, 29.0%) and a few had the dosage dropped (n=5, 3.1%). In one case the research team had informal information on cessation. Those who switched medication were adherent till they were instructed by the physician to halt the current medication; each such case was ascertained by close inspection of the medical files and these corrections constitute most the MPR corrections. The differences at Time 2 between the initial and corrected MPR were smaller or null (for glatiramer acetate). The reasons for modifications (switched medication or decrease in dosage) were the following: side effects reported by the PwMS (n=23), planning of pregnancy (12), relapse (n=10) and non-tolerability reported by patients (n=6). Adherence rates, as assessed by MPR and the two PROs (MS-TAQ and ProMas), were then compared, collapsing across the different medications. Data is presented in Table 4, indicating very high adherence by MS-TAQ (~96% at both time points), adherence rates located at the desired standard of 80% by MPR (80.5% at Time 1 and 81.7% at Time 2), and less than the standard of 80% adherence by ProMas (72.3% at Time 1 and 69.9% at Time 2). Adherence data as measured by EMD was available only for interferon beta 1a and for very few PwMS. As EMD data for interferon beta 1a, reflecting use of injections and available to the clinic through a web site, existed only for 10 PwMS and only for 5 PwMS at both Time 1 and Time 2, no statistical analyses were pursued. The EMD 10
in the form of electronic boxes were not adopted by participants; PwMS complained that the boxes were too big and did not suit carrying outside of their homes; that they required electrical contact and were thus ill-suited for the workplace; and that children at home asked about the boxes. The use of this EMD was discontinued. Consistency across times (Time 1 and Time 2) within each measure was examined by computing a Pearson correlation (for continuous data) and Kappa (for categorical data). ProMas had the highest consistency (r=0.68, p<0.001; 109/139 consistency among PwMS across times, 84.2% of agreement, Kappa=0.47, p<0.001; interclass correlation=0.80, p <0.001), with MPR following (85/102 consistency among PwMS across times, 83.3% total agreement, Kappa=0.44, p<0.001); MS-TAQ showed high consistency across times in terms of total agreement (r=0.41; 137/147 consistency among PwMS across times, 93.2% total agreement). Finally, the score of adherence was the same at Time 1 and Time 2 (either indicating adherence or nonadherence) at 100/151 of the PwMS (72.8%), Kappa=0.40, p=<0.001). Table 4 displays adherence (N and %) across time and measure, as well as total agreement in each measure across time (i.e. consistency identifying people as either adherence or on-adherent) and percentage of PwMS consistently identified as non-adherent. Consistency between the different measures was also examined. ProMas and MSTAQ were significantly associated, though moderately (r=0.30 and 0.42, p's<.05, time2 and time3, respectively). These measures were at 73.1% of the instances in agreement at Time 1 and 69.6% agreement at Time 2, Kappa=0.42, p <.001 and Kappa=0.38, p <.001, respectively. The PROs and MPR were also associated, being at agreement on adherence in the range of 74.5%-84.7% of the instances. Specifically, ProMas and MPR at Time 1 had 76.7.1% agreement, Kappa=0.37, p <.0001; ProMas and MPR at Time 2 had 74.5% agreement, Kappa=0.26, p=.0007. MS-TAQ and 11
MPR at Time 1: 79.5% agreement, Kappa=0.07, p=.245; MS-TAQ and MPR at Time 2: 84.7% agreement, Kappa=0.23, p =.007). Adherence across types of medication and routes of administrations (selfinjected vs. oral) A score of adherence, comprising of MPR and PROs, was computed (see methods). Adherence was computed at each time points for each type of medication and displayed at Table 5. A chi-square test was conducted to compare adherence between the DMT types. There was no significant difference between the different medications at Time 1 and Time 2, χ 2 (3) = 2.29, p=0.515, and χ 2 (3) = 0.64, p=0.887, respectively, nor was a significant difference when self-injected medications were compared to oral medications. Higher adherence in the oral route of administration can be seen at both time points, yet the difference was non-significant (χ 2 (1) = 0.60 and 0.14 at Time 1 and Time 2, and p=0.437 and 0.705, respectively).
Discussion This analysis of multiple measures of adherence among PwMS yielded the following major findings. First, no significant differences in adherence rates were found between the DMTs types at both time points. This result departs from some previous findings (Bergvall et al., 2014; Devonshire et al., 2011; Menzin et al., 2013) yet similar to other results (Hansen, Evans, 2016). Relatedly, adherence rates found in this study fall within the range uncovered in other studies (Conway et al., 2018; Devonshire et al., 2011; Evans et al., 2016; Hansen et al., 2015; Menzin et al., 2013) and may reflect the relatively short time of one-year follow-up. Secondly, different measures yielded different adherence levels. This finding resemble prior work (Dima et al., 2017; Molloy and O’Carroll, 2017; Moran et al., 12
2017b; Phillips et al., 2013). Few studies on adherence among PwMS employed multiple measures (Johnson et al., 2017; Yeh et al., 2017) and these also documented variance among measures (Yeh et al., 2017). Interestingly, a PRO, specifically ProMas, used for the first time among PwMS, detected more nonadherence than MPR by tapping nuanced, subtle behaviors surrounding adherence. The scale affords clinicians to discuss specific behaviors surrounding nonadherence with PwMS. Relatedly, the introduced index of adherence, based on identifying nonadherence by either of the measures, afforded the identification of more nonadherent PwMs who were then targeted for counselling. Thirdly, only moderate levels of agreement were found between the different measures (70-85% total agreement) and somewhat higher total agreement were recorded within the same measure across time (72-93% total agreement), indicating that medication taking behavior among 25% -28% of PwMS varies across time. Relatedly, identifying those who are consistently nonadherent, around 20% of PwMS when using the more sensitive measure (i.e., ProMas), is a priority, as these patients do not benefit fully from their treatment. The fourth finding emerging from the study is that the indicator of MPR does not fully reflect the 'real life' situation: raw MPRs adherence rates were lower than corrected MPSs, which were based on PwMS’ medical files. It seems time elapses before revised regimen are updated, and in cases of switching medication (which requires a period of no medication taking) MPR does not fully reflect adherence. It should be noted that MPR corrections were smaller at Time 2 (than at Time 1), possibly reflecting stabilization in medication taking behavior. Lastly, regime modifications, such as dosage change, may reflect a compromise struck between the clinician and PwMS, departing from the intended or “ought” dosage. 13
The final major finding pertains to EMDs. PwMS did not adopt the provided electronic boxes and their reservations indicated that the device was ill-suited to their needs and did not provide them any benefit. Retrospectively, PwMS should have taken part in the construction of this EMD, both in the stage of preference elicitation and in testing, so that difficulties with the specific EMD could have been addressed. Similarly, the RebifSmart electronic device attached to IFN β1 was activated so as to transmit the information only among a minority of PwMS and fewer had it operational a year after initiation. Our findings depart from previous work (Fernández et al., 2016) where the EMD device was fully operational. This clearly indicates that patient training on the operation of these devices is important in routine clinical care and issues of training resources need to be addressed. Implications Our study indicates that MPR data should be considered with some caution. The measure is usually viewed in the literature as overestimating adherence (Hess et al., 2006) yet an examination of each nonadherent patient’s file, usually not carried out, indicated MPR is an underestimation of adherence, as pharmacy data of the HMO was not promptly updated to account for modifications in prescriptions. The most important implication arising from our findings is that adherence should be measured in multiple ways, as recommended in the literature (Williams et al., 2013). Moreover, it should be assessed repeatedly, and then addressed in clinical encounters with patients (Kronish and Moise, 2017). Repeated assessments are critical in this chronic condition, as adherence was demonstrated to change over time: many people around 30% - did not behave the same across time and being adherent at one point did not guarantee it will stay so. Consistency across time in adherence is important in
14
treating MS with DMTs; guidelines of scientific associations (Rae-Grant et al., 2018) posit that treatment needs to continue over a long term to gain benefits. Uncovering nonadherence is the first step to explore its reasons and search, jointly with the patient, for suitable remedies.
Limitations and strengths The current work has several limitations. First, the study is based on a relatively small sample located in one clinic. Second, follow-up time was currently 1 year only. Third, the planned EMDs were not adopted by PwMS in our study and clearly warrant more care in design, training and maintenance. The strengths of the study are that it employed several measures of adherence, including a recent PRO covering a wide range of adherence behaviors (Kleppe et al., 2015), and that each non-adherent case was examined closely: the medical file was examined so as to verify nonadherence and the patient was later referred to intervention focusing on nonadherence.
In conclusion, this prospective longitudinal study of PwMS assessed adherence using PROs coupled with retrospective analysis of medication claims. The multiple measurement uncovered medication taking among MS patients at a moderate level and as a changing, dynamic behavior. Hence, it is imperative to continuously assess medication adherence in multiple ways and address it repeatedly in clinician-patient encounters. Additionally, this study supports the notion that applying PROs in monitoring medication adherence would facilitate implementation of Participatory Medicine and patient-centered strategies in MS care (Lejbkowicz et al., 2012). 15
Disclosures
Funding/Support: This work was supported in part by Novartis Pharmaceuticals, Biogen Idec and Merck Serono. Financial Disclosures: Dr. Miller has served on the scientific advisory board, and received personal compensation for consulting and/or speaking activities and/ or honoraria and/or received grant support for research from: Avanir Pharmaceuticals; Bayer-Schering Pharma; Biogen Idec; Mapi Pharma; Medison Pharma Ltd.; Merck Serono; Novartis, Sanofi-Genzyme and Teva Pharmaceutical Industries Ltd.
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https://doi.org/10.1212/WNL.0000000000005347 Teter, B., Agashivala, N., Kavak, K., Chouhfeh, L., Hashmonay, R., WeinstockGuttman, B., 2014. Characteristics influencing therapy switch behavior after suboptimal response to first-line treatment in patients with multiple sclerosis. Mult. Scler. J. 20, 830–836. https://doi.org/10.1177/1352458513513058 Tintore, M., Vidal-Jordana, A., Sastre-Garriga, J., 2018. Treatment of multiple sclerosis — success from bench to bedside. Nat. Rev. Neurol. 1–3. https://doi.org/10.1038/s41582-018-0082-z Warrender-Sparkes, M., Spelman, T., Izquierdo, G., Trojano, M., Lugaresi, A., Grand’Maison, F., Havrdova, E., Horakova, D., Boz, C., Oreja-Guevara, C., Alroughani, R., Iuliano, G., Duquette, P., Girard, M., Terzi, M., Hupperts, R., Grammond, P., Petersen, T., Fernandez-Bolanos, R., Fiol, M., Pucci, E., Lechner-Scott, J., Verheul, F., Cristiano, E., Van Pesch, V., Petkovska-Boskova, T., Moore, F., Kister, I., Bergamaschi, R., Saladino, M.L., Slee, M., Barnett, M., Amato, M.P., Shaw, C., Shuey, N., Young, C., Gray, O., Kappos, L., Butzkueven, H., Kalincik, T., Jokubaitis, V., 2015. The effect of oral immunomodulatory therapy on treatment uptake and persistence in multiple sclerosis. Mult. Scler. 22, 520–532. https://doi.org/10.1177/1352458515594041 Wicks, P., Massagli, M., Kulkarni, A., Dastani, H., 2011. Use of an online community to develop patient-reported outcome instruments: The multiple sclerosis treatment adherence questionnaire (MS-TAQ). J. Med. Internet Res. 13. https://doi.org/10.2196/jmir.1687 Williams, A.B., Amico, K.R., Bova, C., Womack, J.A., 2013. A proposal for quality standards for measuring medication adherence in research. AIDS Behav. 17, 284–297. https://doi.org/10.1007/s10461-012-0172-7 Wong, J., Gomes, T., Mamdani, M., Manno, M., O’Connor, P.W., 2011. Adherence to multiple sclerosis disease-modifying therapies in Ontario is low. Can. J. Neurol. Sci. 38, 429–433. World Health Organization (WHO), 2003. Adherence to long-term therapies; Evidence for action, World Health Organisation. Yeh, E.A., Grover, S.A., Powell, V.E., Alper, G., Banwell, B.L., Edwards, K., Gorman, M., Graves, J., Lotze, T.E., Mah, J.K., Mednick, L., Ness, J., Obadia, M., Slater, R., Waldman, A., Waubant, E., Schwartz, C.E., 2017. Impact of an electronic monitoring device and behavioral feedback on adherence to multiple sclerosis therapies in youth: results of a randomized trial. Qual. Life Res. 26, 2333–2349. https://doi.org/10.1007/s11136-017-1571-z Zecca, C., Disanto, G., Mühl, S., Gobbi, C., 2017. Subjective patient-reported versus objective adherence to subcutaneous interferon β-1a in multiple sclerosis using RebiSmart®: The CORE study. BMC Neurol. 17, 1–5. https://doi.org/10.1186/s12883-017-0952-9
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Table 1. Enrollment of Participants
Participants recruitment (n=226)
Excluded (n=32): Not meeting criteria DMT > 1 Year
Participants screening for eligibility at baseline (n=194)
Time 1 follow-up (n=194)
2
Language literacy
14
Declined to participate
2
Cognitive impairment
3
Moved to another clinic
8
Discontinued treatment (pregnancy intention, side effects)
3
Not analyzed at Time 2 follow-up:
Time 2 follow-up (n=154) Analyzed at Time 2 (n=146)
20
No Time 2 data yet
40
Switched to non-study DMT
3
Lost to follow up
5
Table 2. Baseline demographic and clinical characteristics (N=194) Age, Mean (SD)
40.6 (13.8)
Gender, N (%) Male Female
53 (27.3) 120 (72.7)
Ethnicity Jewish
137 (70.6%)
PCI
52 (26.8)
Other
5 (2.6%)
Marital Status, Married
117 (60.3)
Education Secondary
59 (30.4)
Post-secondary
31 (16.0)
Tertiary
104 (53.6)
Social Economic Status Low
14 (7.4)
Average and above
176 (92.6)
Comorbidity Yes
38 (19.6)
No
140 (72.2)
Do not know
8 (4.1)
Physical disability (EDSS, time 0) MS duration, in years
2.60 (2.0) 7.81 (7.2)
EDSS: Expanded Disability Status Scale;
21
Table 3. Medication possession ratio (MPR) across times (n=118): raw adherence (=>80%) and corrected adherence based on medical files; n adherent/n* and (%) MPR
IFN
GA
Fingolimod
Teriflunomide
Time1, raw MPR
30/48 (62.5)
10/14 (71.4)
9/24 (37.5)
26/55 (47.3)
Time1, corrected MPR
36/48 (70.5)
13/14 (92.9)
17/24 (70.8)
49/55 (89.1)
Time2, raw MPR
20/31 (64.5)
10/10 (100.0) 9/16 (56.3)
39/49 (79.6)
Time2, corrected MPR
22/31 (71.0)
10/10 (100.0) 11/16 (68.8)
44/49 (89.8)
*note that MPR data is available for only part of the PwMS participating in the study IFN: interferon beta 1a; GA: glatiramer acetate
22
Table 4. Adherence (n, %) across time, as assessed by MPR, PROM and Score
Measure
Time1
Time2
% total agreement
% non-adherent
MPR
107 (80.5)
85 (81.7)
Time 1 – Time 2
at both Time 1 and Time2
83.3
9.8
MS-TAQ
186 (96.4)
139 (93.9)
93.2
0.6
ProMas
136 (72.3)
102 (69.9)
84.2
18.0
Adherence Score*
128 (66.0)
100 (64.9)
72.8
21.1
* score is based on nonadherence on either of the above measures
23
Table 5. Score of Adherence across time by DMT type; adherent/n in medication (%)
IFN
GA
Fingolimod
Teriflunomide
Time 1 (n=188)
42/67 (62.7)
14/21 (66.7)
22/36 (61.1)
47/64 (73.4)
Time 2 (n=146)
28/42 (66.7)
13/20 (65.0)
15/26 (57.7)
38/58 (65.5)
Oral
Self-Injected
Time1 (n=188)
69/100 (69.0)
56/88 (63.6)
Time 2 (n=146)
53/84 (63.1)
41/62 (66.1)
IFN: interferon beta 1a; GA: glatiramer acetate
24