The use of medication adherence guidelines in medication taking behaviour research

Not obtaining a medication the first time it is prescribed: primary non-adherence to cardiovascular pharmacotherapy

Primary medication non-adherence describes the situation when a first prescription for a new medication is never filled. Primary non-adherence is an important, yet understudied aspect of reduced effectiveness of pharmacotherapy. This review summarizes the frequency, impact, reasons, predictors, and interventions regarding primary non-adherence to cardiovascular/cardiometabolic drugs. The current literature reveals a high prevalence of primary non-adherence. The individual risk of primary non-adherence is determined on multiple factors, e.g., primary non-adherence of lipid-lowering drugs is higher compared to antihypertensive medications. However, the overall rate of primary non-adherence is > 10%. Additionally, this review identifies specific areas for research to better understand why patients forgo evidence-based beneficial pharmacotherapy and to explore targeted interventions. At the same time, measures to reduce primary non-adherence-once proven to be effective-may represent an important new opportunity to reduce cardiovascular diseases.

Opioid Nonadherence Risk Prediction of Patients with Cancer-Related Pain Based on Five Machine Learning Algorithms

Objectives

Opioid nonadherence represents a significant barrier to cancer pain treatment efficacy. However, there is currently no effective prediction method for opioid adherence in patients with cancer pain. We aimed to develop and validate a machine learning (ML) model and evaluate its feasibility to predict opioid nonadherence in patients with cancer pain.

Methods

This was a secondary analysis from a cross-sectional study that included 1195 patients from March 1, 2018, to October 31, 2019. Five ML algorithms, such as logistic regression (LR), random forest, eXtreme Gradient Boosting, multilayer perceptron, and support vector machine, were used to predict opioid nonadherence in patients with cancer pain using 43 demographic and clinical factors as predictors. The predictive effects of the models were compared by the area under the receiver operating characteristic curve (AUC_ROC), accuracy, precision, sensitivity, specificity, and F1 scores. The value of the best model for clinical application was assessed using decision curve analysis (DCA).

Results

The best model obtained in this study, the LR model, had an AUC_ROC of 0.82, accuracy of 0.82, and specificity of 0.71. The DCA showed that clinical interventions for patients at high risk of opioid nonadherence based on the LR model can benefit patients. The strongest predictors for adherence were, in order of importance, beliefs about medicines questionnaire (BMQ)-harm, time since the start of opioid, and BMQ-necessity. Discussion. ML algorithms can be used as an effective means of predicting adherence to opioids in patients with cancer pain, which allows for proactive clinical intervention to optimize cancer pain management. This trial is registered with ChiCTR2000033576.

Validity, sensitivity and specificity of a measure of medication adherence instrument among patients taking oral anticoagulants

Although self-report instruments are currently considered a valuable tool for measuring adherence, due to their low cost and ease of implementation, there are still important factors that impact measurement accuracy, such as social desirability and memory bias. Thus, the Global Assessment of Medication Adherence Instrument (GEMA) was developed to provide an accurate measure of this construct. The aim of this study was to evaluate the properties of the measurement of the Global Evaluation of Medication Adherence Instrument (GEMA) among patients with chronic diseases. A methodological study was conducted in the public hospital of the state of São Paulo, Brazil. The adherence to anticoagulants as well as the international normalized ratio (INR) was assessed on 127 patients. Besides GEMA, two other instruments were used to assess adherence: the Morisky Medication Adherence Scale-8 (MMAS-8) and the Measurement of Adhesion to Treatments (MAT). The GEMA presented a satisfactory level of specificity (0.76) to identify adherents among those with a stable INR, low sensitivity (0.43) for the identification of non-adherents among those with an unstable INR, and a Positive Predictive Value of 0.70. Positive and weak to moderate correlations were observed between the proportion of doses assessed with GEMA and the scores on the MMAS-8 (r = .26 and r = .22, respectively) and the MAT (r = .22 and r = .30, respectively). The GEMA presented good practicality, acceptability, and evidence of specificity regarding the stability of the INR. The validity of the construct was partially supported by the relationship with self-reported measures of adherence.

Italian Translation and Validation of the Original ABC Taxonomy for Medication Adherence

Medication adherence represents a complex and multifaceted process. Standardized terminology is essential to enable a reproducible process in various languages. The study's aim was to translate and adapt the original Ascertaining Barriers for Compliance (ABC) Taxonomy on medication adherence, first proposed in 2012, into Italian language. The study was carried out according to the Preferred Methods for Translation of the ABC Taxonomy for Medication Adherence adopted by the ESPACOMP. Key steps included: (1) a systematic literature review using PubMed and Embase according to the PRISMA Guidelines to identify published Italian terms and definitions, and Italian adherence experts; (2) a forward translation of terms and definitions; (3) panelists' selection; (4) a three-round Delphi survey. From the systematic review, 19 studies allowed detection of 4 terms, 4 definitions and 767 Italian experts. To these, Italian ESPACOMP members and experts though snowball sampling were added. The identified Italian adherence experts received the Delphi questionnaire. The Italian ABC Taxonomy was achieved after three rounds of Delphi survey by reaching at least a moderate consensus on unambiguous naming and definition of medication adherence-related terms. The Taxonomy is intended to be used in research, academic, and professional fields in order to harmonize adherence terminology and avoid confusion in comparing research findings.

Gaming for Adherence to Medication using Ehealth in Rheumatoid arthritis (GAMER) study: a randomised controlled trial

Objective

To examine the effect on adherence to disease modifying anti-rheumatic drugs (DMARDs) in participants with rheumatoid arthritis (RA) of a serious game that targeted implicit attitudes toward medication.

Methods

A multicentre randomised controlled trial (RCT) was performed with adults with RA that used DMARDs and possessed a smartphone/tablet. Control and intervention groups received care as usual. The intervention group played the serious game at will during 3 months. Game play data and online questionnaires Compliance Questionnaire on Rheumatology (CQR), Beliefs about Medicine Questionnaire (BMQ), Health Assessment Questionnaire (HAQ) and Rheumatoid Arthritis Disease Activity Index (RADAI) were collected. Primary outcome was DMARD implementation adherence operationalised as the difference in proportion of non-adherent participants (<80% taking adherence) between intervention and control group after 3 months using a Chi-squared test. Two sample t-tests and Wilcoxon rank-sum test were performed to test for differences on secondary outcomes.

Results

Of the 110 intervention participants that started the study, 87 participants (79%) installed the game and had a median playtime of 9.7 hours at 3 months. Overall, 186 participants completed the study. Adherence in intervention group (63%) and control group (54%) did not differ significantly (p=0.13) at 3 months. Neither were there differences oberved in CQR continuous score, beliefs about medication (BMQ) or clinical outcomes (HAQ and RADAI).

Conclusion

A serious game aimed at reinterpreting attitudes toward medication failed to show an effect on adherence to DMARDs or clinical outcomes in patients with RA. The game was played frequently indicating that it can be an effective channel for reaching patients.

Trial registration number

NL7217.

Medication Adherence in Chronic Older Patients: An Italian Observational Study Using Medication Adherence Report Scale (MARS-5I)

Background: the world population is aging, and the prevalence of chronic diseases is increasing. Chronic diseases affect the quality of life of patients and contribute toward increased healthcare costs if patients do not adhere to treatment. This study defines the medication adherence levels of patients with chronic diseases. Methods: an observational cross-sectional study was carried out. Patients aged 65 years and older with chronic diseases were included in this study. The medication adherence report scale was used. Results: overall, 98 patients aged 65 years and older were included. The mean age of responders was 78.65 years. Study population: 71.43% were always adherent; 9.79% often adherent; 14.89% sometimes adherent; 3.87% rarely adherent; and 1% never adherent. The internal consistency of the MARS-5I was good: Cronbach’s alfa value of 0.77. Conclusions: the MARS-5I is an effective self-report instrument to measure the medication adherence of patients. However, further studies are needed to explore factors affecting medication adherence to avoid clinical consequences for patients and high healthcare costs for healthcare facilities. Healthcare communication could be improved to ensure better transitional care.