Measuring and reporting treatment adherence: What can we learn by comparing two respiratory conditions?

Adherence to inhaled corticosteroids and long-acting β2-agonists in asthma: A MASK-air study

INTRODUCTION

Adherence to controller medication is a major problem in asthma management, being difficult to assess and tackle. mHealth apps can be used to assess adherence. We aimed to assess the adherence to inhaled corticosteroids+long-acting β2-agonists (ICS+LABA) in users of the MASK-air® app, comparing the adherence to ICS+formoterol (ICS+F) with that to ICS+other LABA.

MATERIALS AND METHODS

We analysed complete weeks of MASK-air® data (2015-2022; 27 countries) from patients with self-reported asthma and ICS+LABA use. We compared patients reporting ICS+F versus ICS+other LABA on adherence levels, symptoms and symptom-medication scores. We built regression models to assess whether adherence to ICS+LABA was associated with asthma control or short-acting beta-agonist (SABA) use. Sensitivity analyses were performed considering the weeks with no more than one missing day.

RESULTS

In 2598 ICS+LABA users, 621 (23.9%) reported 4824 complete weeks and 866 (33.3%) reported weeks with at most one missing day. Higher adherence (use of medication ≥80% of weekly days) was observed for ICS+other LABA (75.1%) when compared to ICS+F (59.3%), despite both groups displaying similar asthma control and work productivity. The ICS+other LABA group was associated with more days of SABA use than the ICS+F group (median=71.4% versus 57.1% days). Each additional weekly day of ICS+F use was associated with a 4.1% less risk in weekly SABA use (95%CI=-6.5;-1.6%;p=0.001). For ICS+other LABA, the percentage was 8.2 (95%CI=-11.6;-5.0%;p<0.001).

CONCLUSIONS

In asthma patients adherent to the MASK-air app, adherence to ICS+LABA was high. ICS+F users reported lower adherence but also a lower SABA use and a similar level of control.

Estimating medication adherence from Electronic Health Records: comparing methods for mining and processing asthma treatment prescriptions

BACKGROUND

Medication adherence is usually defined as the extent of the agreement between the medication regimen agreed to by patients with their healthcare provider and the real-world implementation. Proactive identification of those with poor adherence may be useful to identify those with poor disease control and offers the opportunity for ameliorative action. Adherence can be estimated from Electronic Health Records (EHRs) by comparing medication dispensing records to the prescribed regimen. Several methods have been developed in the literature to infer adherence from EHRs, however there is no clear consensus on what should be considered the gold standard in each use case. Our objectives were to critically evaluate different measures of medication adherence in a large longitudinal Scottish EHR dataset. We used asthma, a chronic condition with high prevalence and high rates of non-adherence, as a case study.

METHODS

Over 1.6 million asthma controllers were prescribed for our cohort of 91,334 individuals, between January 2009 and March 2017. Eight adherence measures were calculated, and different approaches to estimating the amount of medication supply available at any time were compared.

RESULTS

Estimates from different measures of adherence varied substantially. Three of the main drivers of the differences between adherence measures were the expected duration (if taken as in accordance with the dose directions), whether there was overlapping supply between prescriptions, and whether treatment had been discontinued. However, there are also wider, study-related, factors which are crucial to consider when comparing the adherence measures.

CONCLUSIONS

We evaluated the limitations of various medication adherence measures, and highlight key considerations about the underlying data, condition, and population to guide researchers choose appropriate adherence measures. This guidance will enable researchers to make more informed decisions about the methodology they employ, ensuring that adherence is captured in the most meaningful way for their particular application needs.

Economic impact of a more extensive use of FENO testing on the Italian population with asthma

BACKGROUND

Asthma is a common chronic inflammatory airway affecting over 260 million people worldwide, and characterized, in the large majority of cases, by the so-called "type 2 inflammation". Fractional exhaled nitric oxide (FE_NO) testing is noninvasive point-of-care tool to assess type 2 inflammation and therefore improve asthma management. It has been suggested to determine eligibility for a specific biologic therapy and predict likelihood to respond. The aim of this study was to estimate the overall economic impact of an extensive use of FE_NO testing on the Italian population with asthma, including extra costs of testing and savings generated by more appropriate prescriptions, increased adherence and lower frequency of exacerbations.

METHODS

A cost of illness analysis was firstly performed to estimate the yearly economic burden from the National Healthcare Service (NHS) perspective in Italy of the management of asthmatic patients with standard of care (SOC) according to the application of GINA (Global Initiative for Asthma) guidelines; then, we evaluated the changes in the economic burden in patient management by introducing FE_NO testing into clinical practice. The cost items considered were: visits/exams, exacerbations, drugs, management of adverse events caused by short-term oral corticosteroids use. Efficacy of FeNO test and SOC is based on literature evidence. Costs refer to published data or Diagnosis Related Group/outpatient tariffs.

RESULTS

Considering one asthma visit every 6 months, the total yearly cost for the management of patients with asthma in Italy is 1,599,217,876€ (409.07€ per patient), while for FE_NO testing strategy this figure is 1,395,029,747€ (356.84€ per patient). An increased utilization rate of FE_NO testing from 50 to 100% of patients may lead to savings for the NHS from about 102 to 204 million € compared to SOC.

CONCLUSIONS

Our study showed that FeNO testing strategy may improve the management of asthmatic patients leading to significant savings for the NHS.

Estimating Medication Adherence from Electronic Health Records Using Rolling Averages of Single Refill-based Estimates

Medication adherence is usually defined as the manner in which a patient takes their medication, in relation to the regimen agreed to with their healthcare provider. Electronic Health Records (EHRs) can be used to estimate adherence in a cost-effective and non-invasive manner across large-scale populations, although there is no universally agreed optimal approach to doing so. We sought to explore patterns of asthma ICS prescription refills in a large EHR dataset, and to evaluate the use of rolling-average based measures towards short-term adherence estimation. Over 1.6 million asthma controllers were prescribed for our cohort of 91,332 individuals, between January 2009 and March 2017. The Continuous Single interval measures of medication Availability (CSA) and Gaps (CSG) were calculated for individual prescriptions, as well as rolling-average adherence measures of the CSA over 3, 5, or 10 past prescription intervals. 16.7% of the study population had only a single prescription during their follow-up (a median duration of 7.1 years). 51% of prescriptions were refilled before (or on the day that) supply was exhausted, and for 19% of prescription refills, the amount of medication dispensed should have lasted at least twice as long as the duration before the next refill was filled. The rolling average measures had statistically strong associations (Spearman |R|>0.7) with the estimate for the subsequent prescription refill. Rolling averages of multiple individual refill-level adherence estimates provide a novel and simple way to crudely smoothen estimates from individual prescription refills, which are strongly influenced by common (and adherent) real-world behaviors, for more meaningful and effective trend detection. Clinical Relevance- This demonstrates a novel methodology for estimating medication adherence which can detect recent changes in trends.

[Adherence to digital health interventions: definitions, methods, and open questions]

Many digital interventions rely on the participation of their users to have a positive impact. In various areas it can be observed that the use of digital interventions is often reduced or fully discontinued by the users after a short period of time. This is seen as one of the main factors that can limit the effectiveness of digital interventions. In this context, the concept of adherence to digital interventions is becoming increasingly important. Adherence to digital interventions is roughly defined as "the degree to which the user followed the program as it was designed," which can also be paraphrased as "intended use" or "use as it is designed." However, both the theoretical-conceptual and practical discussions regarding adherence to digital interventions still receive too little attention.The aim of this narrative review article is to shed more light on the concept of adherence to digital interventions and to distinguish it from related concepts. It also discusses the methods and metrics that can be used to operationalize adherence and the predictors that positively influence adherence. Finally, needs for action to better address adherence are considered critically.

The use of medication adherence guidelines in medication taking behaviour research

Medication nonadherence continues to be a serious issue in a range of long-term medical conditions and has been studied extensively over the past few decades. However, despite the plethora of research studies on medication adherence, poor methodological rigour in many studies has contributed to limited generalisability of the positive findings, limited impact on patients' medication adherence, and inability to compare between studies. This paper focuses on current guidelines designed specifically for research on medication adherence. It discusses key elements to consider during study design, selection of adherence measurements, and reporting on medication adherence research, to ensure a higher quality of research in medication adherence. Overall, there appears to be variations in adherence terminology reported in the literature despite improvements in defining medication taking behaviour and the availability of taxonomies. In addition, limited guidance exists on how best to measure adherence. Recommendations are provided on appropriate adherence measures for the adherence behaviour being investigated, including careful consideration of adherence concepts, validity of adherence instruments, appropriate instrument selection, definition of nonadherence threshold, and how to report medication adherence. Improving adherence research requires greater clarity and standardisation of descriptions of nonadherence behaviour, increased methodological rigour in study designs, better selection of adherence measurements, and comprehensive reporting.