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

Temporal characteristics and associated factors of discontinuation and outcomes after percutaneous coronary intervention

Background: Medication adherence in patients after percutaneous coronary intervention (PCI) is suboptimal, and discontinuation is common. Information on the temporal characteristics and associated factors of discontinuation and outcomes after PCI is insufficient to improve medication adherence interventions. Methods: We conducted a single-center retrospective study of post-PCI patients by telephone survey and medical record extraction. Temporal characteristics and associated factors of discontinuation and outcomes were examined by survival curve analysis, Cox regression, or time-dependent Cox regression. Results: Discontinuation and major adverse cardiovascular events (MACE) after PCI had similar temporal characteristics, with the highest incidence in the first year, followed by a decline. Temporary discontinuation was associated with pre-PCI medication nonadherence (HR 1.63; 95% CI: 1.09-2.43), lack of medication necessity (HR 2.33; 95% CI: 1.44-3.78), economic difficulties (HR 2.09; 95% CI: 1.26-3.47), routine disruption (HR 2.09; 95% CI: 1.10-3.99), and emotional distress (HR 2.76; 95% CI: 1.50-5.09). Permanent discontinuation was associated with residence in rural areas (HR 4.18; 95% CI: 1.84-9.46) or small to medium-sized cities (HR 4.21; 95% CI: 1.82-9.73), lack of medication necessity (HR 10.60; 95% CI: 6.45-17.41), and side effects (HR 3.30; 95% CI: 1.94-5.62). The MACE after PCI was associated with pre-PCI hypertension (HR 1.42; 95% CI: 1.04-1.96), two coronary stents (HR 1.42; 95% CI: 1.01-1.99) or three coronary stents (HR 1.66; 95% CI: 1.11-2.49) compared to one coronary stent up to this PCI, and temporary discontinuation (≤60 months HR 2.18; 95% CI: 1.47-3.25; >60 months HR 8.82; 95% CI: 3.65-21.28). Conclusion: Discontinuation and MACE after PCI have similar temporal characteristics, temporary discontinuation and permanent discontinuation have different associated factors, and the former is associated with MACE. These findings may provide guidance for medication adherence interventions.

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.

Quality of measurement properties of medication adherence instruments in cardiovascular diseases and type 2 diabetes mellitus: a systematic review and meta-analysis

BACKGROUND

Medication adherence has a major impact on reducing mortality and healthcare costs related to the treatment of cardiovascular diseases and diabetes mellitus. Selecting the best patient-reported outcome measure (PROM) among the many available for this kind of patient is extremely important. This study aims to critically assess, compare and synthesize the quality of the measurement properties of patient-reported outcome measures to assess medication adherence among patients with cardiovascular diseases and/or type 2 diabetes mellitus.

METHODS

This review followed the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guidelines and was reported according to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). The searches were performed in Web of Science, SCOPUS, PubMed, CINAHL, EMBASE, LILACS, PsycINFO, and ProQuest (gray literature).

RESULTS

A total of 110 records encompassing 27 different PROMs were included in the review. The included records were published between 1986 and 2023, most of which reported studies conducted in the United States and were published in English. None of the PROMs were classified in the category "a", thus being recommended for use due to the quality of its measurement properties. The PROMs that should not be recommended for use (category "c") are the MTA, GMAS, DMAS-7, MALMAS, ARMS-D, and 5-item questionnaire. The remaining PROMs, e.g., MMAS-8, SMAQ, MEDS, MNPS, ARMS-12, MGT, MTA-OA, MTA-Insulin, LMAS-14, MARS-5, A-14, ARMS-10, IADMAS, MAQ, MMAS-5, ProMAS, ARMS-7, 3-item questionnaire, AS, 12-item questionnaire, and Mascard were considered as having the potential to be recommended for use (category "b").

CONCLUSION

None of the included PROMs met the criteria for being classified as trusted and recommended for use for patients with cardiovascular diseases and/or type 2 diabetes mellitus. However, 21 PROMs have the potential to be recommended for use, but further studies are needed to ensure their quality based on the COSMIN guideline for systematic reviews of PROMs.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42019129109.

Self-reported adherence and reasons for nonadherence among patients with low proportion of days covered for antihypertension medications

BACKGROUND: Incorporation of pharmacy fill data into the electronic health record has enabled calculations of medication adherence, as measured by proportion of days covered (PDC), to be displayed to clinicians. Although PDC values help identify patients who may be nonadherent to their medications, it does not provide information on the reasons for medication-taking behaviors. OBJECTIVE: To characterize self-reported adherence status to antihypertensive medications among patients with low refill medication adherence. Our secondary objective was to identify the most common reasons for nonadherence and examine the patient sociodemographic characteristics associated with these barriers. METHODS: Participants were adult patients seen in primary care clinics of a large, urban health system and on antihypertensive therapy with a PDC of less than 80% based on 6-month linked electronic health record-pharmacy fill data. We administered a validated medication adherence screener and a survey assessing reasons for antihypertensive medication nonadherence. We used descriptive statistics to characterize these data and logistic and Poisson regression models to assess the relationship between sociodemographic characteristics and adherence barriers. RESULTS: The survey was completed by 242 patients (57% female; 61.2% White; 79.8% not Latino/a or Hispanic). Of these patients, 45% reported missing doses of their medications in the last 7 days. In addition, 48% endorsed having at least 1 barrier to adherence and 38.4% endorsed 2 or more barriers. The most common barriers were being busy and having difficulty remembering to take medications. Compared with White participants, Black participants (incident rate ratio = 2.49; 95% CI = 1.93-3.22) and participants of other races (incident rate ratio = 2.16; 95% CI = 1.62-2.89) experienced a greater number of barriers. CONCLUSIONS: Nearly half of patients with low PDC reported nonadherence in the prior week, suggesting PDC can be used as a screening tool. Augmenting PDC with brief self-report tools can provide insights into the reasons for nonadherence. DISCLOSURES: Dr Kharmats, Ms Martinez, Dr Belli, Ms Zhao, Dr Mann, Dr Schoenthaler, and Dr Blecker received grants from the National Institute of Health/National Heart, Lung, Blood Institute. Dr Voils holds a license by Duke University for the DOSE-Nonadherence measure and is a consultant for New York University Grossman School of Medicine. This research was supported by the NIH (R01HL156355). Dr Kharmats received a postdoctoral training grant from the National Institutes of Health (5T32HL129953-04). Dr Voils was supported by a Research Career Scientist award from the Health Services Research & Development Service of the Department of Veterans Affairs (RCS 14-443). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the United States Government.

Disagreement between pharmacy claims and direct interview to identify patients with non-adherence to chronic cardiometabolic medications

BACKGROUND

Accurate methods of identifying patients with suboptimal adherence to cardiometabolic medications are needed, and each approach has benefits and tradeoffs.

METHODS

We used data from a large trial of patients with poorly controlled cardiometabolic disease and evidence of medication non-adherence measured using pharmacy claims data whose adherence was subsequently assessed during a telephone consultation with a clinical pharmacist. We then evaluated if the pharmacist assessment agreed with the non-adherence measured using claims. When pharmacist and claims assessments disagreed, we identified reasons why claims were insufficient and used multivariable modified Poisson regression to identify patient characteristics associated with disagreement.

RESULTS

Of 1,069 patients identified as non-adherent using claims (proportion of days covered [PDC] <80%), 646 (60.4%) were confirmed as non-adherent on pharmacist interview. For the 423 patients (39.6%) where the interview disagreed with the claims, the most common reasons were paying cash or using an alternate insurance (36.6%), medication discontinuation or regimen change (32.8%), and recently becoming adherent (26.7%). Compared to patients whose claims and interview both showed non-adherence, patients whose interview disagreed with claims were less likely to miss outpatient office visits (RR:0.91, 95%CI:0.85-0.97) and more likely to have a baseline PDC above the median (RR:1.35, 95%CI:1.10-1.64).

CONCLUSIONS

Among patients identified as non-adherent by claims, 39.6% were observed to be adherent when assessed during pharmacist consultation. This discrepancy was largely driven by paying out-of-pocket, using alternative insurance, or medication discontinuation or change. These findings have important implications for using pharmacy claims to identify and intervene upon medication non-adherence.

Trends in HIV Stigma Experienced by People Living With HIV in the Netherlands: A Comparison of Cross-Sectional Surveys Over Time

We investigated whether HIV stigma has changed in recent years. We compared data on stigma settings and manifestations from 2007 (n = 667) and, specifically for health care, 2009 (n = 262), to data acquired in 2019/2020 (n = 258). Results showed reductions in stigma from friends, family, acquaintances, at work, in the financial services sector, and in media, but stigmatizing messages in media remained highly prevalent. Stigma in the LGBTQI+ community, with sexual partners, and while partying also remained prevalent and, disconcertingly, relatively unchanged. Stigma in health care increased. HIV stigma was positively related to psychological distress, and negatively related to social support and medication adherence. Further, most participants were familiar with U=U and PrEP, but 13.3% questioned the accuracy of U=U. Stigma reduction efforts should focus on reducing stigma in media, in the LGBTQI+ community and while dating, and in health care, with U=U as a key message.

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).

Exploring patient experiences coping with using multiple medications: a qualitative interview study

OBJECTIVE

Long-term adherence to evidence-based medications in cardiometabolic diseases remains poor, despite extensive efforts to develop and test interventions and deploy clinician performance incentives. The limited success of interventions may be due to ignored factors such as patients' experience of medication-taking. Despite being potentially addressable by clinicians, these factors have not been sufficiently explored, which is particularly important as patients use increasing numbers of medications. The aim is to explore patient perspectives on medication-taking, medication properties that are barriers to adherence, and coping strategies for their medication regimen.

DESIGN

Individual, in-person, semistructured qualitative interviews.

SETTING

Urban healthcare system.

PARTICIPANTS

Twenty-six adults taking ≥2 oral medications for diabetes, hypertension or hyperlipidaemia with non-adherence. Interviews were digitally recorded and transcribed. Data were analysed using developed codes to generate themes. Representative quotations were selected to illustrate themes.

RESULTS

Participants' mean age was 55 years, 46% were female and 39% were non-white. Six key themes were identified: (1) medication-taking viewed as a highly inconvenient action (that patients struggle to remember to do); (2) negative implications because of inconvenience or illness perceptions; (3) actual medication regimens can deviate substantially from prescribed regimens; (4) certain medication properties (especially size and similar appearance with others) may contribute to adherence deviations; (5) development of numerous coping strategies to overcome barriers and (6) suggestions to make medication-taking easier (including reducing drug costs, simplifying regimen or dosing frequency and creating more palatable medications).

CONCLUSION

Patients with poor adherence often find taking prescription medications to be undesirable and take them differently than prescribed in part due to properties of the medications themselves and coping strategies they have developed to overcome medication-taking challenges. Interventions that reduce the inconvenience of medication use and tailor medications to individual needs may be a welcome development.

Patient characteristics associated with use of TNF vs interleukin inhibitors as first-line biologic treatment for psoriatic arthritis

BACKGROUND: Previous studies have examined treatment patterns among patients who use tumor necrosis factor (TNF) inhibitors for psoriatic arthritis (PsA). However, little data exist for a comparison between the TNF inhibitor treatment pattern and that of newly available biologics such as interleukin (IL)-12/23 or 17 inhibitors in the United States. OBJECTIVES: To (a) examine patient characteristics and their association with initiation of TNF inhibitors vs IL-12/23 or 17 inhibitors among PsA patients and (2) compare treatment persistence of PsA patients who initiated TNF inhibitors vs IL-12/23 or 17 inhibitors as first-line biologic treatment in a real-world setting in the United States. METHODS: Using claims data from MarketScan (2013-2017), we identified a cohort of PsA patients who initiated TNF inhibitors or IL-12/23 or 17 inhibitors. The primary outcome was treatment persistence, defined as continuous use of the index drug at 1 year, regardless of refill gaps. The secondary outcome was treatment persistence with high adherence at 1 year (ie, refill gaps ≤ 30 days). Multivariable logistic regression was used to assess the association between patient characteristics and treatment initiation and persistent use of TNF inhibitors vs IL-12/23 or 17 inhibitors. RESULTS: We identified 3,180 TNF inhibitor initiators and 214 IL-12/23 or 17 inhibitor initiators. Initiators of IL-12/23 or 17 inhibitors had more comorbidities than TNF inhibitor initiators. The proportion of patients with treatment persistence was 53.0% in TNF inhibitor initiators and 53.7% in IL-12/23 or 17 inhibitor initiators; 37.1% of TNF inhibitor users and 24.8% of IL-12/23 or 17 inhibitor users were treatment persistent with high adherence. There was no difference in 1-year treatment persistence between the 2 groups after adjusting for baseline characteristics (adjusted odds ratio [aOR] for TNF inhibitors vs IL-12/23 or 17 inhibitors: 0.86, 95% CI = 0.63-1.15). However, use of TNF inhibitors was associated with a greater treatment persistence with high adherence compared with use of IL-12/23 or 17 inhibitors (aOR = 1.61, 95% CI = 1.15-2.26). CONCLUSIONS: PsA patients who initiated an IL 12/23 or 17 inhibitor had a greater comorbidity burden compared with those who initiated TNF inhibitors. Treatment persistence was similar between the 2 groups, whereas medication adherence was higher with TNF inhibitors than with IL 12/23 or 17 inhibitors during the first year of treatment. DISCLOSURES: This study was funded by an investigator-initiated research grant from Pfizer, Inc (grant number: WI235988). The content is solely the responsibility of the authors. The sponsor was given the opportunity to make nonbinding comments on a draft of the manuscript. Publication of the manuscript was not contingent on approval by the sponsor. Kim has received research grants to the Brigham and Women's Hospital from Roche, AbbVie, and Bristol-Myers Squibb for unrelated topics. Merola is a consultant and/or investigator for BMS, AbbVie, Dermavant, Lilly, Novartis, Janssen, UCB, Sun Pharma, and Pfizer. Jin, Chen, Lee, and Landon have nothing to disclose.

Preferences for mHealth Technology and Text Messaging Communication in Patients With Type 2 Diabetes: Qualitative Interview Study

Background

Individuals with diabetes need regular support to help them manage their diabetes on their own, ideally delivered via mechanisms that they already use, such as their mobile phones. One reason for the modest effectiveness of prior technology-based interventions may be that the patient perspective has been insufficiently incorporated.

Objective

This study aims to understand patients’ preferences for mobile health (mHealth) technology and how that technology can be integrated into patients’ routines, especially with regard to medication use.

Methods

We conducted semistructured qualitative individual interviews with patients with type 2 diabetes from an urban health care system to elicit and explore their perspectives on diabetes medication–taking behaviors, daily patterns of using mobile technology, use of mHealth technology for diabetes care, acceptability of text messages to support medication adherence, and preferred framing of information within text messages to support diabetes care. The interviews were digitally recorded and transcribed. The data were analyzed using codes developed by the study team to generate themes, with representative quotations selected as illustrations.

Results

We conducted interviews with 20 participants, of whom 12 (60%) were female and 9 (45%) were White; in addition, the participants’ mean glycated hemoglobin A_1c control was 7.8 (SD 1.1). Overall, 5 key themes were identified: patients try to incorporate cues into their routines to help them with consistent medication taking; many patients leverage some form of technology as a cue to support adherence to medication taking and diabetes self-management behaviors; patients value simplicity and integration of technology solutions used for diabetes care, managing medications, and communicating with health care providers; some patients express reluctance to rely on mobile technology for these diabetes care behaviors; and patients believe they prefer positively framed communication, but communication preferences are highly individualized.

Conclusions

The participants expressed some hesitation about using mobile technology in supporting diabetes self-management but have largely incorporated it or are open to incorporating it as a cue to make medication taking more automatic and less burdensome. When using technology to support diabetes self-management, participants exhibited individualized preferences, but overall, they preferred simple and positively framed communication. mHealth interventions may be improved by focusing on integrating them easily into daily routines and increasing the customization of content.

Reinforcing adherence to lipid-lowering therapy after an acute coronary syndrome: A pragmatic randomized controlled trial

BACKGROUND AND AIMS

Achieving the low-density lipoprotein cholesterol (LDL-C) goal following an acute coronary syndrome (ACS) is a milestone often missed due to suboptimal adherence to secondary prevention treatments. Whether improved adherence could result in reduced LDL-C levels is unclear. We aimed to evaluate whether an educational-motivational intervention increases long-term lipid-lowering therapy (LLT) adherence and LDL-C goal attainment rate among post-ACS patients.

METHODS

IDEAL-LDL was a parallel, two-arm, single-center, pragmatic, investigator-initiated randomized controlled trial. Hospitalized patients for ACS were randomized to a physician-led integrated intervention consisting of an educational session at baseline, followed by regular motivational interviewing phone sessions or usual care. Co-primary outcomes were the LLT adherence (measured by Proportion of Days Covered (PDC); good adherence defined as PDC>80%), and LDL-C goal (<70 mg/dl or 50% reduction from baseline) achievement rate at one year.

RESULTS

In total, 360 patients (mean age 62 years, 81% male) were randomized. Overall, good adherence was positively associated with LDL-C goal achievement rate at one year. Median PDC was higher in the intervention group than the control group [0.92 (IQR, 0.82-1.00) vs. 0.86 (0.62-0.98); p = 0.03] while the intervention group had increased odds of good adherence (odds ratio: 1.76 (95% confidence interval 1.02 to 2.62; p = 0.04). However, neither the LDL-C goal achievement rate (49.6% in the intervention vs. 44.9% in the control group; p = 0.49) nor clinical outcomes differed significantly between the two groups.

CONCLUSIONS

Α multifaceted intervention improved LLT adherence in post-ACS patients without a significant difference in LDL-C goal attainment.