Purpose: The purpose of this study was to evaluate and compare medication adherence to chronic therapies in older populations across different regions in Europe. Methods: This explorative study applied a harmonized method of data extraction and analysis from pharmacy claims databases of three European countries to compare medication adherence at a cross-country level. Data were obtained for the period between January 1, 2010, and December 31, 2011. Patients (aged ≥65 years) who newly initiated to oral antidiabetics, antihyperlipidemics, or antiosteoporotics were identified and followed for over a 12-month period. Main outcome measures were medication adherence (medication possession ratio, [MPR]; implementation) and persistence on index treatment. All country-specific data sets were prepared by employing a common data input model. Outcome measures were calculated for each country and pooled using random effect models. Results: In total, 39,186 new users were analyzed. In pooled data from the three countries, suboptimal implementation (MPR 50% of older people non-adherent to antihyperlipidemics and antiosteoporotics in the three European cohorts. However, the degree of variability in adherence rates among the three countries was high. A harmonized method of data extraction and analysis across health-related database in Europe is useful to compare medication-taking behavior at a cross-country level.

Objective: To assess suitability and comparability of the most common methods of treatment adherence and persistence assessment, as applied to the same pharmacy dataset. Methods: Data on drugs prescribed for cardiovascular primary prevention to participants in the Aragon Workers’ Health Study (AWHS) were collected from a regional electronic drug prescription database. Several different approaches were used to measure treatment adherence (with the medication possession ratio [MPR]) and proportion of days covered [PDC]) and persistence in new users by therapeutic subgroup. Defined daily dose (DDD) was used as a proxy of the number of days’ supply, or substituted with surrogate daily dose values. Results: Higher mean adherence values and proportions of adherent patients were obtained using MPR versus PDC, with additional differences depending on the approach used. The proportion of adherent patients was lowest for oral antidiabetics (14.4%–30.6%) and highest for antihypertensives (70.2%–82.1%). The use of surrogate daily dose values increased adherence for antidiabetics and statins and decreased adherence for antihypertensives. After a 1 year follow-up, treatment persistence was observed for 21.1%, 58.7%, and 29.5% of antidiabetic, antihypertensive and statin users, respectively. Conclusions: Our findings indicate that use of multiple measures of treatment adherence and persistence provides a more complete overview of medication use patterns, and certain limitations associated with DDD for some drug groups can be overcome with replacement by surrogate doses. The PDC indicator seems to provide a more accurate reflection of patient behavior and treatment continuity than the MPR. Any comparison of adherence/persistence should always consider the method used, variables analyzed, and corresponding data collection process.

Medication adherence is a priority for health systems worldwide and is widely recognised as a key component of quality of care for disease management. Adherence-related indicators were rarely explicitly included in national health policy agendas. One barrier is the lack of standardised adherence terminology and of routine measures of adherence in clinical practice. This paper discusses the possibility of developing adherence-related performance indicators highlighting the value of measuring persistence as a robust indicator of quality of care. To standardise adherence and persistence-related terminology allowing for benchmarking of adherence strategies, the European Ascertaining Barriers for Compliance (ABC) project proposed a Taxonomy of Adherence in 2012 consisting of three components: initiation, implementation, discontinuation. Persistence, which immediately precedes discontinuation, is a key element of taxonomy, which could capture adherence chronology allowing the examination of patterns of medication-taking behaviour. Advances in eHealth and Information Communication Technology (ICT) could play a major role in providing necessary structures to develop persistence indicators. We propose measuring persistence as an informative and pragmatic measure of medication-taking behaviour. Our view is to develop quality and performance indicators of persistence, which requires investing in ICT solutions enabling healthcare providers to review complete information on patients’ medication-taking patterns, as well as clinical and health outcomes.

It is essential to understand the impact of social inequalities on the risk of COVID-19 infection in order to mitigate the social consequences of the pandemic. With this aim, the objective of our study was to analyze the effect of socioeconomic inequalities, both at the individual and area of residence levels, on the probability of COVID-19 confirmed infection, and its variations across three pandemic waves. We conducted a retrospective cohort study and included data from all individuals tested for COVID-19 during the three waves of the pandemic, from March to December 2020 (357,989 individuals) in Aragón (Spain). We studied the effect of inequalities on the risk of having a COVID-19 confirmed diagnosis after being tested using multilevel analyses with two levels of aggregation: individuals and basic healthcare area of residence (deprivation level and type of zone). Inequalities in the risk of COVID-19 confirmed infection were observed at both the individual and area level. There was a predominance of low-paid employees living in deprived areas. Workers with low salaries, unemployed and people on minimum integration income or who no longer receive the unemployment allowance, had a higher probability of COVID-19 infection than workers with salaries ≥ €18,000 per year. Inequalities were greater in women and in the second wave. The deprivation level of areas of residence influenced the risk of COVID-19 infection, especially in the second wave. It is necessary to develop individual and area coordinated measures by areas in the control, diagnosis and treatment of the epidemic, in order to avoid an increase in the already existing inequalities.

Despite half a century of dedicated studies, medication adherence remains far from perfect, with many patients not taking their medications as prescribed. The magnitude of this problem is rising, jeopardizing the effectiveness of evidence-based therapies. An important reason for this is the unprecedented demographic change at the beginning of the 21st century. Aging leads to multimorbidity and complex therapeutic regimens that create a fertile ground for nonadherence. As this scenario is a global problem, it needs a worldwide answer. Could this answer be provided, given the new opportunities created by the digitization of health care? Daily, health-related information is being collected in electronic health records, pharmacy dispensing databases, health insurance systems, and national health system records. These big data repositories offer a unique chance to study adherence both retrospectively and prospectively at the population level, as well as its related factors. In order to make full use of this opportunity, there is a need to develop standardized measures of adherence, which can be applied globally to big data and will inform scientific research, clinical practice, and public health. These standardized measures may also enable a better understanding of the relationship between adherence and clinical outcomes, and allow for fair benchmarking of the effectiveness and cost-effectiveness of adherence-targeting interventions. Unfortunately, despite this obvious need, such standards are still lacking. Therefore, the aim of this paper is to call for a consensus on global standards for measuring adherence with big data. More specifically, sound standards of formatting and analyzing big data are needed in order to assess, uniformly present, and compare patterns of medication adherence across studies. Wide use of these standards may improve adherence and make health care systems more effective and sustainable.

The World Health Organization considers the non-adherence to medication a significant issue with global impact, especially in chronic conditions such as type 2 diabetes. We aim to study antidiabetic treatment initiation, add-on, treatment switching, and medication persistence. We conducted an observational study on 4247 individuals initiating antidiabetic treatment between 2013 and 2014 in the EpiChron Cohort (Spain). We used Cox regression models to estimate the likelihood of non-persistence after a one-year follow-up, expressed as hazard ratios (HRs). Metformin was the most frequently used first-line antidiabetic (80% of cases); combination treatment was the second most common treatment in adults aged 40–79 years, while dipeptidyl peptidase-4 inhibitors were the second most common in individuals in their 80s and over, and in patients with renal disease. Individuals initiated on metformin were less likely to present addition and switching events compared with any other antidiabetic. Almost 70% of individuals initiated on monotherapy were persistent. Subjects aged 40 and over (HR 0.53–0.63), living in rural (HR 0.79) or more deprived areas (HR 0.77–0.82), or receiving polypharmacy (HR 0.84), were less likely to show discontinuation. Our findings could help identify the population at risk of discontinuation, and offer them closer monitoring for proper integrated management to improve prognosis and health outcomes.