Methodological considerations on estimating medication adherence from self-report, electronic monitoring, and electronic healthcare databases using the TEOS framework

Relationship between adherence to oral antidiabetic drugs and control of type 2 diabetes mellitus

INTRODUCTION AND OBJECTIVES

Poor adherence to oral antidiabetic drugs (Adh-OAD) is a risk factor for poor control of type 2 diabetes mellitus (T2DM). Therefore, it is necessary to quantify the Adh-OAD. This quantification is possible through electronic dispensing records from the community pharmacy. The objective was to evaluate the influence of the Adh-OAD on the control of T2DM and the percentage of glycosylated hemoglobin (%HbA1c) in the patient.

MATERIALS AND METHODS

A cross-sectional descriptive observational study was conducted in 8 community pharmacies in Granada (Spain). Patients older than 18 years with T2DM and on oral antidiabetic drugs (OADs) for at least 6 months were included. The main study variables were the control of T2DM, %HbA1c, and the Adh-OAD considering three cut-off points (≥80%, ≥70%, ≥60%). This relationship was studied using multivariate binary logistic regression and multivariate linear regression, respectively.

RESULTS

A total of 107 patients were included. The mean age was 70.5 years (SD: 9.7), and 54.2% were men. Eighty-five patients (79.4%) had well-controlled T2DM (mean %HbA1c: 6.5%; SD=0.6). Considering Adh-OAD≥80%, 13.1% (n=14) had a poor adherence and was related to the %HbA1c (β=0.742; p=0.007) and the control of T2DM (OR: 7.327; 95% CI: 1.302-41.241). Poor adherence was found in 9.3% (n=10) considering Adh-OAD≥70% and in 3.7% (n=4) considering Adh-OAD≥60%. In both cases, a statistically significant relationship was found between Adh-OAD and the %HbA1c and between Adh-OAD and the control of T2DM.

CONCLUSIONS

Adh-OAD influenced the %HbA1c in patients with T2DM and the control of their disease.

Patient-Led Urate Self-Monitoring to Improve Clinical Outcomes in People With Gout: A Feasibility Study

OBJECTIVE

Self-monitored point-of-care urate-measuring devices are an underexplored strategy to improve adherence to urate-lowering therapy and clinical outcomes in gout. This study observed patient-led urate self-monitoring practice and assessed its influence on allopurinol adherence, urate control, and health-related quality of life.

METHODS

People with gout (n = 31) and prescribed allopurinol self-monitored their urate concentrations (HumaSens2.0plus) at baseline and thereafter monthly for 12 months (3 months per quarter). Adherence to allopurinol was measured using medication event monitoring technology (Medication Event Monitoring System cap). Time spent below the target urate concentration (<0.36 mmol/L) was determined. Health-related quality of life was measured using a survey (EuroQoL EQ-5D-5L). Gout flares were recorded. Two-tailed Spearman correlation and the Wilcoxon matched-pairs signed-rank test (P < 0.05) were used for statistical comparisons.

RESULTS

Most participants were male (94%) and had urate concentrations below the target (74%) at baseline. Overall, seven participants demonstrated repeated periods of "missed doses" (two or fewer allopurinol doses missed consecutively) and "drug holidays" (three or more missed doses). Most participants (94%) persisted with allopurinol. Time spent within the target urate concentration increased 1.3-fold (from 79% to 100%; P = 0.346), and the incidence of gout flares decreased 1.6-fold (from 8 to 5; P = 0.25) in the final quarter compared to that in the first quarter of the study. Health-related quality of life was reduced for participants reporting at least one gout flare (median utility values 0.9309 vs 0.9563, P = 0.04).

CONCLUSION

Patient-led urate self-monitoring may support the maintenance of allopurinol adherence and improve urate control, thus reducing the incidence of gout flares. Further research on patient-led urate self-monitoring in a randomized controlled study is warranted.

Evaluation of adherence to antipsychotics: A real-world data study using four different dosing assumptions

AIMS

This study aimed to assess the frequency of dosing inconsistencies in prescription data and the effect of four dosing assumption strategies on adherence estimates for antipsychotic treatment.

METHODS

A retrospective cohort, which linked prescription and dispensing data of adult patients with ≥1 antipsychotic prescription between 2015-2016 and followed up until 2019, in Catalonia (Spain). Four strategies were proposed for selecting the recommended dosing in overlapping prescription periods for the same patient and antipsychotic drug: (i) the minimum dosing prescribed; (ii) the dose corresponding to the latest prescription issued; (iii) the highest dosing prescribed; and (iv) all doses included in the overlapped period. For each strategy, one treatment episode per patient was selected, and the Continuous Medication Availability measure was used to assess adherence. Descriptive statistics were used to describe results by strategy.

RESULTS

Of the 277 324 prescriptions included, 76% overlapped with other prescriptions (40% with different recommended dosing instructions). The number and characteristics of patients and treatment episodes (18 292, 18 303, 18 339 and 18 536, respectively per strategy) were similar across strategies. Mean adherence was similar between strategies, ranging from 57 to 60%. However, the proportion of patients with adherence ≥90% was lower when selecting all doses (28%) compared with the other strategies (35%).

CONCLUSION

Despite the high prevalence of overlapping prescriptions, the strategies proposed did not show a major effect on the adherence estimates for antipsychotic treatment. Taking into consideration the particularities of antipsychotic prescription practices, selecting the highest dose in the overlapped period seemed to provide a more accurate adherence estimate.

Immunosuppressant adherence in adult outpatient hematopoietic cell transplant recipients

INTRODUCTION

Medication nonadherence continues to be challenging for allogeneic hematopoietic cell transplant (HCT) recipients. The risk and severity of chronic graft-versus-host disease (GVHD) are associated with low immunosuppressant concentrations (which can be improved with model-informed precision dosing (MIPD)) and with immunosuppressant nonadherence (which can be improved with acceptable interventions).

METHODS

With the goals of improving adherence and achieving therapeutic concentrations of immunosuppressants to eliminate GVHD, we characterized the feasibility of using the Medication Event Monitoring (MEMS®) Cap in adult HCT recipients.

RESULTS

Of the 27 participants offered the MEMS® Cap at the time of hospital discharge, 7 (25.9%) used it, which is below our a priori threshold of 70%. These data suggest the MEMS® Cap is not feasible for HCT recipients. The MEMS® Cap data were available for a median of 35 days per participant per medication (range: 7-109 days). The average daily adherence per participant ranged from 0 to 100%; four participants had an average daily adherence of over 80%.

CONCLUSIONS

MIPD may be supported by MEMS® technology to provide the precise time of immunosuppressant self-administration. The MEMS® Cap was used by only a small percentage (25.9%) of HCT recipients in this pilot study. In accordance with larger studies using less accurate tools to evaluate adherence, immunosuppressant adherence varied from 0% to 100%. Future studies should establish the feasibility and clinical benefit of combining MIPD with newer technology, specifically the MEMS® Button, which can inform the oncology pharmacist of the time of immunosuppressant self-administration.

Reimagining Medication Adherence: A Novel Holistic Model for Hypertension Therapy

Purpose

Patients' adherence to the prescribed therapy is influenced by several personal and social factors. However, existing studies have mostly focused on individual aspects. We took a holistic approach to develop a higher-level impact factor model.

Patients and Methods

In this independent, non-interventional, cross-sectional and anonymous study design the pharmacist recruited patients who entered the pharmacy and handed in a prescription for a blood pressure medication. The patients received a paper questionnaire with a stamped return envelope to volunteer participation. A total of 476 patients in Germany who reported having at least high normal blood pressure according to the Global Hypertension Practice Guidelines were surveyed. In this study, each patient received an average of 2.49 antihypertensive prescriptions and 7.9% of all patients received a fixed-dose combination. Partial least squares structural equation modeling was performed for model analytics since it enables robust analysis of complex relationships.

Results

Emotional attitude, behavioral control, and therapy satisfaction directly explained 65% of therapy adherence. The predictive power of the out-of-sample model for the Q2-statistic was significant. The patient's overall therapy satisfaction determined medication adherence. The medication scheme's complexity also influenced the adherence levels. Therapy satisfaction was significantly shaped by the complexity of the medication scheme, behavioral control, and emotional attitude. The results demonstrated the superior performance of fixed-dose combinations against combinations of mono-agents according to the adherence level. Additionally, patient-physician and patient-pharmacist relationships influenced behavioral control of medication therapy execution. According to the A14-scale to measure the level of adherence, 49.6% of patients were classified as adherent and the remainder as non-adherent.

Conclusion

The results enable healthcare stakeholders to target attractive variables for intervention to achieve maximum effectiveness. Moreover, the proven predictive power of the model framework enables clinicians to make predictions about the adherence levels of their hypertensive patients.

A framework for understanding sources of bias in medication adherence research

The sources of bias in medication adherence research have not been comprehensively explored. We aimed to identify biases expected to affect adherence research and to develop a framework for mapping these onto the phases of adherence (initiation, implementation and discontinuation). A literature search was conducted, key papers were reviewed and a Catalogue of Bias was consulted. The specific biases related to adherence measurement and metrics were mapped onto the phases of adherence using a tabular matrix. Twenty-three biases were identified, of which 11 were specifically relevant to adherence measures and metrics. The mapping framework showed differences in the numbers and types of biases associated with each measure and metric while highlighting those common to many adherence study designs (e.g., unacceptability bias and apprehension bias). The framework will inform the design of adherence studies and the development of risk of bias tools for adherence research.

Psychometric Properties of a Machine Learning-Based Patient-Reported Outcome Measure on Medication Adherence: Single-Center, Cross-Sectional, Observational Study

BACKGROUND

Medication adherence plays a critical role in controlling the evolution of chronic disease, as low medication adherence may lead to worse health outcomes, higher mortality, and morbidity. Assessment of their patients' medication adherence by clinicians is essential for avoiding inappropriate therapeutic intensification, associated health care expenditures, and the inappropriate inclusion of patients in time- and resource-consuming educational interventions. In both research and clinical practices the most extensively used measures of medication adherence are patient-reported outcome measures (PROMs), because of their ability to capture subjective dimensions of nonadherence. Machine learning (ML), a subfield of artificial intelligence, uses computer algorithms that automatically improve through experience. In this context, ML tools could efficiently model the complexity of and interactions between multiple patient behaviors that lead to medication adherence.

OBJECTIVE

This study aimed to create and validate a PROM on medication adherence interpreted using an ML approach.

METHODS

This cross-sectional, single-center, observational study was carried out a French teaching hospital between 2021 and 2022. Eligible patients must have had at least 1 long-term treatment, medication adherence evaluation other than a questionnaire, the ability to read or understand French, an age older than 18 years, and provided their nonopposition. Included adults responded to an initial version of the PROM composed of 11 items, each item being presented using a 4-point Likert scale. The initial set of items was obtained using a Delphi consensus process. Patients were classified as poorly, moderately, or highly adherent based on the results of a medication adherence assessment standard used in the daily practice of each outpatient unit. An ML-derived decision tree was built by combining the medication adherence status and PROM responses. Sensitivity, specificity, positive and negative predictive values (NPVs), and global accuracy of the final 5-item PROM were evaluated.

RESULTS

We created an initial 11-item PROM with a 4-point Likert scale using the Delphi process. After item reduction, a decision tree derived from 218 patients including data obtained from the final 5-item PROM allowed patient classification into poorly, moderately, or highly adherent based on item responses. The psychometric properties were 78% (95% CI 40%-96%) sensitivity, 71% (95% CI 53%-85%) specificity, 41% (95% CI 19%-67%) positive predictive values, 93% (95% CI 74%-99%) NPV, and 70% (95% CI 55%-83%) accuracy.

CONCLUSIONS

We developed a medication adherence tool based on ML with an excellent NPV. This could allow prioritization processes to avoid referring highly adherent patients to time- and resource-consuming interventions. The decision tree can be easily implemented in computerized prescriber order-entry systems and digital tools in smartphones. External validation of this tool in a study including a larger number of patients with diseases associated with low medication adherence is required to confirm its use in analyzing and assessing the complexity of medication adherence.

Methods to assess COPD medications adherence in healthcare databases: a systematic review

BACKGROUND

The Global Initiative for Chronic Obstructive Lung Disease 2023 report recommends medication adherence assessment in COPD as an action item. Healthcare databases provide opportunities for objective assessments; however, multiple methods exist. We aimed to systematically review the literature to describe existing methods to assess adherence in COPD in healthcare databases and to evaluate the reporting of influencing variables.

METHOD

We searched MEDLINE, Web of Science and Embase for peer-reviewed articles evaluating adherence to COPD medication in electronic databases, written in English, published up to 11 October 2022 (PROSPERO identifier CRD42022363449). Two reviewers independently conducted screening for inclusion and performed data extraction. Methods to assess initiation (dispensing of medication after prescribing), implementation (extent of use over a specific time period) and/or persistence (time from initiation to discontinuation) were listed descriptively. Each included study was evaluated for reporting variables with an impact on adherence assessment: inpatient stays, drug substitution, dose switching and early refills.

RESULTS

160 studies were included, of which four assessed initiation, 135 implementation and 45 persistence. Overall, one method was used to measure initiation, 43 methods for implementation and seven methods for persistence. Most of the included implementation studies reported medication possession ratio, proportion of days covered and/or an alteration of these methods. Only 11% of the included studies mentioned the potential impact of the evaluated variables.

CONCLUSION

Variations in adherence assessment methods are common. Attention to transparency, reporting of variables with an impact on adherence assessment and rationale for choosing an adherence cut-off or treatment gap is recommended.