Comparison of Pharmacy Claims and Electronic Pill Bottles for Measurement of Medication Adherence Among Myocardial Infarction Patients

Leveraging Habits to Improve Adherence to Gout Medications: A Qualitative Study

OBJECTIVE

This study investigates patients' medication-taking routines and the feasibility of harnessing habit formation through context cues and rewards to improve medication adherence.

METHODS

Semistructured qualitative interviews with patients with gout from an urban health care system were conducted to explore typical medication-taking behavior, experiences using electronic pill bottles, barriers to adherence, existing context cues, and potential cues and rewards for habit-forming behavior. Medication-taking patterns were recorded for six weeks using electronic pill bottles before interviews to inform discussion. Transcribed interviews were analyzed to generate themes using codes developed by the study team, with representative quotations selected as illustrations.

RESULTS

We conducted interviews with 15 individuals (mean age 60.6 [SD 20.3] years, three women [20%], and nine White patients [60%]). Pill bottle-recorded adherence to urate-lowering therapy (ULT) was high (mean 0.91 [SD 0.10]), and one patient was experiencing an active gout flare. Five key themes emerged: (1) reasons for nonadherence, (2) internal and external motivations for adherence, (3) structured routines around taking medications, (4) rewards for good medication adherence, and (5) the role of pill cap technology in medication-taking.

CONCLUSION

The importance of a predictable, structured routine in which participants could incorporate their medication-taking behavior emerged as a key factor that promoted consistent adherence. Further, identifying context cues and reminders seemed to promote incorporation of medication-taking into routines. Therefore, habit-based interventions that use context cues to establish routines around medication-taking may be a feasible strategy to improve adherence in patients with chronic conditions such as gout.

The impact of using reinforcement learning to personalize communication on medication adherence: findings from the REINFORCE trial

Text messaging can promote healthy behaviors, like adherence to medication, yet its effectiveness remains modest, in part because message content is rarely personalized. Reinforcement learning has been used in consumer technology to personalize content but with limited application in healthcare. We tested a reinforcement learning program that identifies individual responsiveness ("adherence") to text message content and personalizes messaging accordingly. We randomized 60 individuals with diabetes and glycated hemoglobin A1c [HbA1c] ≥ 7.5% to reinforcement learning intervention or control (no messages). Both arms received electronic pill bottles to measure adherence. The intervention improved absolute adjusted adherence by 13.6% (95%CI: 1.7%-27.1%) versus control and was more effective in patients with HbA1c 7.5- < 9.0% (36.6%, 95%CI: 25.1%-48.2%, interaction p < 0.001). We also explored whether individual patient characteristics were associated with differential response to tested behavioral factors and unique clusters of responsiveness. Reinforcement learning may be a promising approach to improve adherence and personalize communication at scale.

Intensifying approaches to address clinical inertia among cardiovascular disease risk factors: A narrative review

OBJECTIVE

Clinical inertia, the absence of treatment initiation or intensification for patients not achieving evidence-based therapeutic goals, is a primary contributor to poor clinical outcomes. Effectively combating clinical inertia requires coordinated action on the part of multiple representatives including patients, clinicians, health systems, and the pharmaceutical industry. Despite intervention attempts by these representatives, barriers to overcoming clinical inertia in cardiovascular disease (CVD) risk factor control remain.

METHODS

We conducted a narrative literature review to identify individual-level and multifactorial interventions that have been successful in addressing clinical inertia.

RESULTS

Effective interventions included dynamic forms of patient and clinician education, monitoring of real-time patient data to facilitate shared decision-making, or a combination of these approaches. Based on findings, we describe three possible multi-level approaches to counter clinical inertia - a collaborative approach to clinician training, use of a population health manager, and use of electronic monitoring and reminder devices.

CONCLUSION

To reduce clinical inertia and achieve optimal CVD risk factor control, interventions should consider the role of multiple representatives, be feasible for implementation in healthcare systems, and be flexible for an individual patient's adherence needs.

PRACTICE IMPLICATIONS

Representatives (e.g., patients, clinicians, health systems, and the pharmaceutical industry) could consider approaches to identify and monitor non-adherence to address clinical inertia.

A systematic review and meta-analysis on impact of suboptimal use of antidepressants, bisphosphonates, and statins on healthcare resource utilisation and healthcare cost

Background

Depression, osteoporosis, and cardiovascular disease impose a heavy economic burden on society. Understanding economic impacts of suboptimal use of medication due to nonadherence and non-persistence (non-MAP) for these conditions is important for clinical practice and health policy-making.

Objective

This systematic literature review aims to assess the impact of non-MAP to antidepressants, bisphosphonates and statins on healthcare resource utilisation and healthcare cost (HRUHC), and to assess how these impacts differ across medication classes.

Methods

A systematic literature review and an aggregate meta-analysis were performed. Using the search protocol developed, PubMed, Cochrane Library, ClinicalTrials.gov, JSTOR and EconLit were searched for articles that explored the relationship between non-MAP and HRUHC (i.e., use of hospital, visit to healthcare service providers other than hospital, and healthcare cost components including medical cost and pharmacy cost) published from November 2004 to April 2021. Inverse-variance meta-analysis was used to assess the relationship between non-MAP and HRUHC when reported for at least two different populations.

Results

Screening 1,123 articles left 10, seven and 13 articles on antidepressants, bisphosphonates, and statins, respectively. Of those, 27 were rated of good quality, three fair and none poor using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. In general, non-MAP was positively associated with HRUHC for all three medication classes and most prominently for bisphosphonates, although the relationships differed across HRUHC components and medication classes. The meta-analysis found that non-MAP was associated with increased hospital cost (26%, p = 0.02), outpatient cost (10%, p = 0.01), and total medical cost excluding pharmacy cost (12%, p<0.00001) for antidepressants, and increased total healthcare cost (3%, p = 0.07) for bisphosphonates.

Conclusions

This systematic literature review is the first to compare the impact of non-MAP on HRUHC across medications for three prevalent conditions, depression, osteoporosis and cardiovascular disease. Positive relationships between non-MAP and HRUHC highlight inefficiencies within the healthcare system related to non-MAP, suggesting a need to reduce non-MAP in a cost-effective way.

Technologies for Medication Adherence Monitoring and Technology Assessment Criteria: Narrative Review

BACKGROUND

Accurate measurement and monitoring of patient medication adherence is a global challenge because of the absence of gold standard methods for adherence measurement. Recent attention has been directed toward the adoption of technologies for medication adherence monitoring, as they provide the opportunity for continuous tracking of individual medication adherence behavior. However, current medication adherence monitoring technologies vary according to their technical features and data capture methods, leading to differences in their respective advantages and limitations. Overall, appropriate criteria to guide the assessment of medication adherence monitoring technologies for optimal adoption and use are lacking.

OBJECTIVE

This study aims to provide a narrative review of current medication adherence monitoring technologies and propose a set of technology assessment criteria to support technology development and adoption.

METHODS

A literature search was conducted on PubMed, Scopus, CINAHL, and ProQuest Technology Collection (2010-present) using the combination of keywords medication adherence, measurement technology, and monitoring technology. The selection focused on studies related to medication adherence monitoring technology and its development and use. The technological features, data capture methods, and potential advantages and limitations of the identified technology applications were extracted. Methods for using data for adherence monitoring were also identified. Common recurring elements were synthesized as potential technology assessment criteria.

RESULTS

Of the 3865 articles retrieved, 98 (2.54%) were included in the final review, which reported a variety of technology applications for monitoring medication adherence, including electronic pill bottles or boxes, ingestible sensors, electronic medication management systems, blister pack technology, patient self-report technology, video-based technology, and motion sensor technology. Technical features varied by technology type, with common expectations for using these technologies to accurately monitor medication adherence and increase adoption in patients' daily lives owing to their unobtrusiveness and convenience of use. Most technologies were able to provide real-time monitoring of medication-taking behaviors but relied on proxy measures of medication adherence. Successful implementation of these technologies in clinical settings has rarely been reported. In all, 28 technology assessment criteria were identified and organized into the following five categories: development information, technology features, adherence to data collection and management, feasibility and implementation, and acceptability and usability.

CONCLUSIONS

This narrative review summarizes the technical features, data capture methods, and various advantages and limitations of medication adherence monitoring technology reported in the literature and the proposed criteria for assessing medication adherence monitoring technologies. This collection of assessment criteria can be a useful tool to guide the development and selection of relevant technologies, facilitating the optimal adoption and effective use of technology to improve medication adherence outcomes. Future studies are needed to further validate the medication adherence monitoring technology assessment criteria and construct an appropriate technology assessment framework.

Identifying temporal patterns of adherence to antidepressants, bisphosphonates and statins, and associated patient factors

Background

Group-based trajectory modelling (GBTM) has recently been explored internationally as an improved approach to measuring medication adherence (MA) by differentiating between alternative temporal patterns of nonadherence. To build on this international research, we use the method to identify temporal patterns of medication adherence to antidepressants, bisphosphonates or statins, and their associations with patient characteristics.

Objectives

The objectives include identification of MA types using GBTM, exploration of features and associated patient characteristics of each MA type, and identification of the advantages of GBTM compared to the traditional proportion of days covered (PDC) measure.

Data and methods

We used 45 and Up Study survey data which contains information about demographics, family, health, diet, work and lifestyle of 267,153 participants aged at least 45 years across New South Wales, Australia. This data was linked to participant records of medication use, outpatient and inpatient care, and death. Our study participants initiated use of antidepressants (9287 participants), bisphosphonates (1660 participants) or statins (10,242 participants) during 2012–2016. MA types were identified from 180-day patterns of medication use for antidepressants and 360-day patterns for bisphosphonates and statins. Multinomial and binomial logistic regressions were performed to estimate participant characteristics associated with GBTM MA and PDC MA, respectively.

Results

Three GBTM MA types were identified for antidepressants and six for bisphosphonates and statins. For all three medications, MA types included: almost fully adherent; decreasing adherence and early discontinuation. The additional nonadherent types for bisphosphonates and statins were improved adherence, low adherence and later discontinuation. Participant characteristics impacting GBTM MA and PDC MA were consistent. However, several associations were uniquely found for GBTM MA as compared to PDC MA.

Conclusion

GBTM permits clinicians, policy-makers and researchers to differentiate between alternative nonadherence patterns, allowing them to better identify patients at risk of poor adherence and tailor interventions accordingly.

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Medication adherence was categorised using group-based trajectory modelling (GBTM).

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GBTM categories include adherence, early discontinuation and decreasing adherence.

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Demographic, economic, health and other factors determined GBTM categories.

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GBTM provides additional information to better target adherence interventions.

Reevaluating medication adherence in the era of digital health

INTRODUCTION

Medication adherence is a worldwide issue impacting more than half the population. The cost associated with nonadherence is tremendous and has spurred the growth of novel technologies to address this growing problem.

AREAS COVERED

This perspective covers the different digital health medication adherence tools that have come to market in the past decade and their clinical impact. These digital interventions and their applicability to medication adherence across different stakeholders are then evaluated.

EXPERT OPINION

Digital health will play a significant role in creating new pathways to care in the 2020s. However, the current design of medication adherence tools has not demonstrated a clinical impact that will be relevant for the digital health space without a change in redesign factoring in relevant stakeholders' incentives to address adherence issues. A focus on only adherence has not yielded the economic or clinical benefit as expected, which is likely due to a lack of focus on broader drug-related problems (DRPs) that are causative factors beyond adherence alone. As such, adherence tools will see disparate uptake, likely due to condition-specific interventions rather than adherence issues as a whole, and future endeavors will need to address the larger DRP considerations to actualize clinical outcomes.

Evidence of the association between adherence to treatment and mortality among patients with COPD monitored at a public disease management program in Brazil

Objective

To evaluate the association between adherence to treatment and mortality among Chronic Obstructive Pulmonary Disease (COPD) patients treated in the Brazilian public health system.

Methods

This is cohort study of moderate-to-severe COPD patients monitored in a public pharmaceutical care-based Disease Management Program (DMP). All subjects who died one year after the beginning of the cohort were age-matched with those who remained alive at the end of the cohort period. Treatment adherence was measured through pharmacy records. Patients who received at least 90% of the prescribed doses were considered adherent to treatment.

Results

Of the 333 patients (52.8% age ≥ 65 years, 67.9% male), 67.3% were adherent to treatment (adherence rate, 87.2%). Mortality was associated with lack of adherence (p = 0.04), presence of symptoms (mMRC ≥ 2) and COPD treatment use. The death was associated with non-adherence, presence of symptoms and previous hospitalization. After adjustment, non-adherent patients to treatment were almost twice times likely to die compared to those adherents (Hazard Ratio (HR) 1.86; CI 1.16-2.98, p = 0.01).

Conclusion

Non-adherence to treatment was associated with higher mortality among moderate-to-severe COPD patients treated in the Brazilian public health system. Strategies to monitor and optimize adherence should be strengthened to reduce COPD-related mortality.

REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial

INTRODUCTION

Achieving optimal diabetes control requires several daily self-management behaviours, especially adherence to medication. Evidence supports the use of text messages to support adherence, but there remains much opportunity to improve their effectiveness. One key limitation is that message content has been generic. By contrast, reinforcement learning is a machine learning method that can be used to identify individuals' patterns of responsiveness by observing their response to cues and then optimising them accordingly. Despite its demonstrated benefits outside of healthcare, its application to tailoring communication for patients has received limited attention. The objective of this trial is to test the impact of a reinforcement learning-based text messaging programme on adherence to medication for patients with type 2 diabetes.

METHODS AND ANALYSIS

In the REinforcement learning to Improve Non-adherence For diabetes treatments by Optimising Response and Customising Engagement (REINFORCE) trial, we are randomising 60 patients with suboptimal diabetes control treated with oral diabetes medications to receive a reinforcement learning intervention or control. Subjects in both arms will receive electronic pill bottles to use, and those in the intervention arm will receive up to daily text messages. The messages will be individually adapted using a reinforcement learning prediction algorithm based on daily adherence measurements from the pill bottles. The trial's primary outcome is average adherence to medication over the 6-month follow-up period. Secondary outcomes include diabetes control, measured by glycated haemoglobin A1c, and self-reported adherence. In sum, the REINFORCE trial will evaluate the effect of personalising the framing of text messages for patients to support medication adherence and provide insight into how this could be adapted at scale to improve other self-management interventions.

ETHICS AND DISSEMINATION

This study was approved by the Mass General Brigham Institutional Review Board (IRB) (USA). Findings will be disseminated through peer-reviewed journals, clinicaltrials.gov reporting and conferences.

TRIAL REGISTRATION NUMBER

Clinicaltrials.gov (NCT04473326).

Randomised controlled trial targeting habit formation to improve medication adherence to daily oral medications in patients with gout

INTRODUCTION

Medication adherence for patients with chronic conditions such as gout, a debilitating form of arthritis that requires daily medication to prevent flares, is a costly problem. Existing interventions to improve medication adherence have only been moderately effective. Habit formation theory is a promising strategy to improve adherence. The cue-reward-repetition principle posits that habits are formed by repeatedly completing an activity after the same cue and having the action rewarded every time. Over time, cues become increasingly important whereas rewards become less salient because the action becomes automatic. Leveraging the cue-reward-repetition principle could improve adherence to daily gout medications.

METHODS AND ANALYSIS

This three-arm parallel randomised controlled trial tests an adaptive intervention that leverages the repetition cue-reward principle. The trial will began recruitment in August 2021 in Boston, Massachusetts, USA. Eligible patients are adults with gout who have been prescribed a daily oral medication for gout and whose most recent uric acid is above 6 mg/dL. Participants will be randomised to one of three arms and given electronic pill bottles. In the two intervention arms, participants will select a daily activity to link to their medication-taking (cue) and a charity to which money will be donated every time they take their medication (reward). Participants in Arm 1 will receive reminder texts about their cue and their charity reward amount will be US$0.50 per day of medication taken. Arm 2 will be adaptive; participants will receive a US$0.25 per adherent-day and no reminder texts. If their adherence is <75% 6 weeks postrandomisation, their reward will increase to US$0.50 per adherent-day and they will receive reminder texts. The primary outcome is adherence to gout medications over 18 weeks.

ETHICS AND DISSEMINATION

This trial has ethical approval in the USA. Results will be published in a publicly accessible peer-reviewed journal.

TRIAL REGISTRATION NUMBER

NCT04776161.

Validation of EHR medication fill data obtained through electronic linkage with pharmacies

BACKGROUND: Recent linkages between electronic health records (EHRs) and pharmacy data hold opportunity for up-to-date assessment of medication adherence at the point of care. OBJECTIVE: To validate linked EHR-pharmacy data, which can be used for point-of-care interventions for concordance with insurance claims data for patients in a large health care delivery system. METHODS: We performed a retrospective cohort study of adult patients with an active antihypertensive medication order and seen as outpatients between August 25, 2019, and August 31, 2019. Pharmacy fill information was obtained from the EHR via linkages with Surescripts pharmacy and pharmacy benefit manager data, as well as from insurance claims available at our institution. We matched antihypertensive medication fills observed in the linked EHR-pharmacy database with available fills in the insurance claims database and calculated the percentage of medication fills that were available in each database. We estimated medication adherence using proportion of days covered in the linked EHR-pharmacy database and in the insurance claims database. RESULTS: Of 26,679 patients with hypertension, 23,348 (87.5%) had at least 1 antihypertensive medication fill recorded in the linked EHR-pharmacy database. Of 1,501 patients matched with the insurance database and with a documented medication fill, a fill was present for 1,484 (98.9%) and 1,259 (83.9%) patients in the linked EHR-pharmacy and insurance databases, respectively. Of 12,109 medication fills recorded in the insurance data, we found an overlap of 11,060 (91.3%) fills with the linked EHR-pharmacy database. The linked EHR-pharmacy database also contained 18,232 of 19,281 (94.6%) medication fills present in either database. Measured medication adherence was higher for patients when based on linked EHR-pharmacy data compared with insurance claims data (42% vs 30%, P < 0.001). CONCLUSIONS: Linked EHR-pharmacy data captured medication fills for the vast majority of patients and resulted in higher estimates of adherence than insurance claims. Our results suggest that pharmacy fill data available in the EHR have sufficient reliability to be used for point-of-care assessment of medication adherence. DISCLOSURES: This study was supported by grant R01HL155149 from the National Heart, Lung, and Blood Institute. Allen Thorpe provided funding for the NYU Langone Health Learning Health System Program, which helped fund this project. The authors have nothing to disclose.

Comparison of a new 3-item self-reported measure of adherence to medication with pharmacy claims data in patients with cardiometabolic disease

BACKGROUND

Less than half of patients with cardiometabolic disease consistently take prescribed medications. While health insurers and some delivery organizations use claims to measure adherence, most clinicians do not have access during routine interactions. Self-reported scales exist, but their practical utility is often limited by length or cost. By contrast, the accuracy of a new 3-item self-reported measure has been demonstrated in individuals with HIV. We evaluated its concordance with claims-based adherence measures in cardiometabolic disease.

METHODS

We used data from a recently-completed pragmatic trial of patients with cardiometabolic conditions. After 12 months of follow-up, intervention subjects were mailed a survey with the 3-item measure that queries about medication use in the prior 30 days. Responses were linearly transformed and averaged. Adherence was also measured in claims in month 12 and months 1-12 of the trial using proportion of days covered (PDC) metrics. We compared validation metrics for non-adherence for self-report (average <0.80) compared with claims (PDC <0.80).

RESULTS

Of 459 patients returning the survey (response rate: 43.5%), 50.1% were non-adherent in claims in month 12 while 20.9% were non-adherent based on the survey. Specificity of the 3-item metric for non-adherence was high (month 12: 0.83). Sensitivity was relatively poor (month 12: 0.25). Month 12 positive and negative predictive values were 0.59 and 0.52, respectively.

CONCLUSIONS

A 3-item self-reported measure has high specificity but poor sensitivity for non-adherence versus claims in cardiometabolic disease. Despite this, the tool could help target those needing adherence support, particularly in the absence of claims data.

Association of Self-Reported Medication Adherence with Potentially Inappropriate Medications in Elderly Patients: A Cross-Sectional Pilot Study

BACKGROUND

Polypharmacy (PP) and potentially inappropriate medications (PIMs) cause problematic drug-related issues in elderly patients; however, little is known about the association between medication adherence and PP and PIMs. This study evaluated the association of self-reported medication adherence with PP and PIMs in elderly patients.

METHODS

A cross-sectional pilot study was conducted using data collected from electronic medical records of 142 self-administering patients aged ≥65 years, excluding emergency hospitalization cases. Self-reported medication adherence was assessed using the visual analogue scale (VAS).

RESULTS

Of the 142 patients, 91 (64.1%) had PP and 80 (56.3%) used at least one PIM. In univariate analysis, patients with a VAS score of 100% had a significantly higher number of female patients and ≥1 PIM use compared to other patients. We found no association between the VAS score and PP. In multivariable analysis, the use of PIMs was significantly associated with a VAS score of 100% (odds ratio = 2.32; 95% confidence interval = 1.16-4.72; p = 0.017).

CONCLUSIONS

Use of PIMs by elderly patients is significantly associated with self-reported medication adherence. Pharmacists should pay more attention to prescribed medications of self-administering elderly patients in order to improve their prescribing quality.

Medication adherence for atrial fibrillation patients: triangulating measures from a smart pill bottle, e-prescribing software, and patient communication through the electronic health record

OBJECTIVE

Our primary objectives were to examine adherence rates across two technologies (e-prescribing software and smart pill bottle) with cross-validation from alert-triggered messaging within the patient electronic health record (EHR) portal and to explore the benefits and challenges faced by atrial fibrillation (AF) patients in using a smart pill bottle.

MATERIALS AND METHODS

We triangulated the rate of oral anticoagulant medication adherence among 160 AF patients over 6 months using an EHR in combination with data from the AdhereTech© Wireless Smart Pill Bottle and Surescripts©. In addition, we collected qualitative feedback on patients' Smart Pill Bottle usage through structured interviews with 153 participants.

RESULTS

Patients maintained an average adherence rate of 90.0% according to the smart pill bottle; however, when dose misses were calibrated based on patient or provider feedback, the adjusted adherence was 93.6%. Surescripts adherence rates for refills were 92.2%. Participants generally found the bottle easy to operate but suggested that its size and functionality did not fit seamlessly into their existing routine, as many used weekly pill organizers to manage multiple medications.

DISCUSSION

Though each method of tracking adherence has positive and negative attributes, combining them and seeking patient feedback may help capture a more accurate adherence rate than any single technological intervention. Technologies may have different design considerations for research and consumer use.

CONCLUSION

Overall, these technologies provide useful but imperfect adherence data for research purposes, and smart pill bottles could be improved with patient-centered design.

LAY SUMMARY

Medication adherence is very important for those with chronic health issues. For those with heart disease, medication adherence not only offers opportunities for improving quality of life, but it also can be life-saving. Nonetheless, many patients with heart disease, including those with atrial fibrillation (the target group for this study) do not take their medications regularly. As technologies advance, there is unprecedented opportunity to track patients' medication adherence through various methods, which might provide motivation and information to patients as they make daily choices about medication use. In this study, we cross-referenced the results of two of these measures over 6 months-a smart pill bottle, which we used to track pill bottle openings, and e-prescribing software, which we used to track medication refills. We also supplemented these measures with nurse-patient communication via the EHR messaging portal to record exceptions (eg, travel and medication changes) and interviewed patients about their medication use during the 6-month trial. Overall, the tracking technologies worked relatively well to track patient (n = 160) medication behavior; however, they did not capture exceptions. Hence, triangulating data from different sources, with a patient feedback loop, appears critical for gathering accurate data on medication adherence.

User Acceptability and Technical Robustness Evaluation of a Novel Smart Pill Bottle Prototype Designed to Support Medication Adherence

PURPOSE

Smart medication adherence monitoring devices can provide objective and granular drug utilization data and help patients engaging with their treatment. In this proof-of-concept study, the acceptability and technical robustness of a novel smart pill bottle prototype (SPBP) were assessed in order to allow further optimization.

METHODS

The SPBP is an app-controlled automatic dispense system, capturing real-time data on a web-based platform, which sends text reminders and measures storage conditions. A heterogeneous group of ten volunteers was asked to dispense placebo capsules with the SPBP and to follow a predefined dosing schedule for a trial period of 2 weeks. Afterwards, a questionnaire was filled out during a short interview. Primary outcome was dispense adherence as measured by the bottle. Other study outcomes included system acceptability (System Usability Scale [SUS]), self-reported adherence (MARS) and technical robustness of the bottle's mechanics (electronic pill dispenser) and sensors (bottle temperature).

RESULTS

The overall dispense adherence rate as measured by the SPBP was 88%. All participants completed the study and four participants had an adherence rate of 100% during the study. The dispense adherence rates corresponded well with participants' self-reported adherence with an average MARS total score of 23.6 (out of 25). Participants judged the system easy to use, with a mean SUS score of 79.3 (range: 57.5-97.5). The overall mean temperature difference between the bottle sensor and calibrated external sensor was -0.82°C (range: -1.37°C to -0.21°C).

CONCLUSION

The SPBP was well accepted and this study provides data for further optimization and follow-up studies. Smart adherence technologies such as these may change the way healthcare professionals, trialists and patients manage medication adherence.

Interventions to Improve Medication Adherence: A Review

IMPORTANCE

Among adults with chronic illness, 30% to 50% of medications are not taken as prescribed. In the United States, it is estimated that medication nonadherence is associated with 125 000 deaths, 10% of hospitalizations, and $100 billion in health care services annually.

OBSERVATIONS

PubMed was searched from January 1, 2000, to September 6, 2018, for English-language randomized clinical trials of interventions to improve medication adherence. Trials of patients younger than 18 years, trials that used self-report as the primary adherence outcome, and trials with follow-up periods less than 6 months were excluded; 49 trials were included. The most common methods of identifying patients at risk for nonadherence were patient self-report, electronic drug monitors (pill bottles), or pharmacy claims data to measure gaps in supply. Patient self-report is the most practical method of identifying nonadherent patients in the context of clinical care but may overestimate adherence compared with objective methods such as electronic drug monitors and pharmacy claims data. Six categories of interventions, and characteristics of successful interventions within each category, were identified: patient education (eg, recurrent and personalized telephone counseling sessions with health educators); medication regimen management (using combination pills to reduce the number of pills patients take daily); clinical pharmacist consultation for chronic disease co-management (including education, increased frequency of disease monitoring via telephone or in-person follow-up visits, and refill reminders); cognitive behavioral therapies (such as motivational interviewing by trained counselors); medication-taking reminders (such as refill reminder calls or use of electronic drug monitors for real-time monitoring and reminding); and incentives to promote adherence (such as reducing co-payments and paying patients and clinicians for achieving disease management goals). The choice of intervention to promote adherence will depend on feasibility and availability within a practice or health system. Successful interventions that are also clinically practical include using combination pills to reduce daily pill burden, clinical pharmacist consultation for disease co-management, and medication-taking reminders such as telephone calls to prompt refills (maximum observed absolute improvements in adherence of 10%, 15%, and 33%, respectively).

CONCLUSIONS AND RELEVANCE

Adherence can be assessed and improved within the context of usual clinical care, but more intensive and costly interventions that have demonstrated success will require additional investments by health systems.

Estimating Adherence Based on Prescription or Dispensation Information: Impact on Thresholds and Outcomes. A Real-World Study With Atrial Fibrillation Patients Treated With Oral Anticoagulants in Spain

Objective: To estimate drug exposure, Proportion of Days Covered (PDC) and percentage of patients with PDC ≥ 80% from a cohort of atrial fibrillation patients initiating oral anticoagulant (OAC) treatment. We employed three different approaches to estimate PDC, using either data from prescription and dispensing (PD cohort) or two common designs based on dispensing information only, requiring at least one (D1) or at least two (D2) refills for inclusion in the cohorts. Finally, we assessed the impact of adherence on health outcomes according to each method. Methods: Population-based retrospective cohort of all patients with Non Valvular Atrial Fibrillation (NVAF), who were newly prescribed acenocoumarol, apixaban, dabigatran or rivaroxaban from November 2011 to December 2015 in the region of Valencia (Spain). Patients were followed for 12 months to assess adherence using three different approaches (PD, D1 and D2 cohorts). To analyze the relationship between adherence (PDC ≥ 80) defined according to each method of calculation and health outcomes (death for any cause, stroke or bleeding) Cox regression models were used. For the identification of clinical events patients were followed from the end of the adherence assessment period to the end of the available follow-up period. Results: PD cohort included all patients with an OAC prescription (n = 38,802), D1 cohort excluded fully non-adherent patients (n = 265) and D2 cohort also excluded patients without two refills separated by 180 days (n = 2,614). PDC ≥ 80% ranged from 94% in the PD cohort to 75% in the D1 cohort. Drug exposure among adherent (PDC ≥ 80%) and non-adherent (PDC < 80%) patients was different between cohorts. In adjusted analysis, high adherence was associated with a reduced risk of death [Hazard Ratio (HR): from 0.82 to 0.86] and (except in the PD cohort) the risk for ischemic stroke (HR: from 0.61 to 0.64) without increasing the risk of bleeding. Conclusion: Common approaches to assess adherence using measures based on days' supply exclude groups of non-adherent patients and, also, misattribute periods of doctors' discontinuation to patient non-adherence, misestimating adherence overall. Physician-initiated discontinuation is a major contributor to reduced OAC exposure. When using the PDC80 threshold, very different groups of patients may be classified as adherent or non-adherent depending on the method used for the calculation of days' supply measures. High adherence and high exposure to OAC treatment in NVAF patients is associated with better health outcomes.

Patients with Asthma Prescribed Once-Daily Fluticasone Furoate/Vilanterol or Twice-Daily Fluticasone Propionate/Salmeterol as Maintenance Treatment: Analysis from a Claims Database

Introduction

There is a paucity of data describing prescribing patterns and adherence to therapy of inhaled corticosteroids (ICS) in combination with long-acting β₂-agonists (LABA) in the Japanese population in clinical practice.

Methods

This was a non-interventional, retrospective, cohort study of patients who were prescribed medication for asthma, using data from the Japan Medical Data Center Claims Database. Data from patients aged ≥ 15 years with a prescription of asthma drugs between December 2014 and October 2015 (Day 0, the index date when asthma medication was initiated) were analysed in 12-month pre-index and post-index periods. Part 1 focused on baseline characteristics and epidemiological outcomes in the pre- and post-index period in the overall asthma population, whereas comparing medication adherence [number of prescribed days per year and proportion of days covered (PDC)] between ICS/LABA-naïve patients treated with once-daily fluticasone furoate/vilanterol (FF/VI) and twice-daily fluticasone propionate/salmeterol (FP/SAL) was the primary endpoint in Part 2.

Results

Of the available patient data (N = 2,953,652), 28,699 patients were identified as having asthma. ICS/LABA was the main asthma treatment prescribed; 11,167 (38.9%) patients were continuous ICS/LABA users. In ICS/LABA-naïve asthma patients, treatment with once-daily FF/VI was associated with higher medication adherence compared with twice-daily FP/SAL; mean [standard deviation (SD)] number of prescribed days per year was 97.8 (115.9) for FF/VI versus 80.5 (92.7) for FP/SAL (p = 0.04), mean (SD) PDC was 26.7% (31.5) for FF/VI versus 21.9% (24.8) for FP/SAL (p = 0.04). FF/VI was also associated with a lower rate of treatment discontinuation and no difference in use of short-acting beta₂-agonists or oral corticosteroids compared with FP/SAL.

Conclusions

ICS/LABA was the major prescribed asthma treatment in Japan. Medication adherence was greater with FF/VI, which may indicate that patients are more likely to adhere to once-daily FF/VI versus twice-daily FP/SAL.

Funding

This study was funded by GSK (study sponsor).

Study Registration

GSK Study No. 207264, GSK Study Register site: https://www.gsk-clinicalstudyregister.com/search/?search_terms=207264.

Electronic supplementary material

The online version of this article (10.1007/s41030-018-0084-4) contains supplementary material, which is available to authorized users.