BackgroundAlthough disease-modifying anti-rheumatic drugs (DMARDs) are the cornerstone of treatment for inflammatory rheumatic diseases, medication adherence to DMARDs is often suboptimal. Effective interventions to improve adherence to DMARDs are lacking, and new targets are needed to improve adherence. The aim of the present study was to explore patients’ barriers and facilitators of optimal DMARD use. These factors might be used as targets for adherence interventions.MethodsIn a mixed method study design, patients (n = 120) with inflammatory arthritis (IA) completed a questionnaire based on an existing adapted Theoretical Domains Framework (TDF) to identify facilitators and barriers of DMARD use. A subgroup of these patients (n = 21) participated in focus groups to provide insights into their facilitators and barriers. The answers to the questionnaires and responses of the focus groups were thematically coded by three researchers independently and subsequently categorized.ResultsThe barriers and facilitators that were reported by IA patients presented large inter-individual variations. The identified barriers and facilitators could be captured in the following domains based on an adapted TDF: (i) knowledge, (ii) emotions, (iii) attention, memory, and decision processes, (iv) social influences, (v) beliefs about capability, (vi) beliefs about consequences, (vii) motivation and goals, (viii) goal conflict, (ix) environmental context and resources, and (x) skills.ConclusionsPatients with IA have a variety of barriers and facilitators with regard to their DMARD use. All of these barriers and facilitators could be categorized into adapted domains of the TDF. Interventions that address individual facilitators and barriers, based on capability, opportunity, and motivation, are needed to develop strategies for medication adherence that are tailored to individual patient needs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12891-016-1289-z) contains supplementary material, which is available to authorized users.

Many definitions of the term clinical pharmacy exist, but a number of ambiguities remain. In order to clarify the European Society of Clinical Pharmacy (ESCP) position on what defines clinical pharmacy, a consultation exercise was conducted among ESCP members with the findings used as the basis for an updated definition. The updated definition clarifies that clinical pharmacy (1) represents both a professional practice and field of research, (2) aims to optimise the utilisation of medicines in order to achieve person-centered and public health goals, (3) as a practice encompasses cognitive, managerial and interpersonal activities targeting all stages of the medicines use process, and as a field of research generates knowledge that informs clinical decision-making, health care organisation or policy, (4) as a practice is restricted to pharmacists, (5) can be practiced regardless of setting, and (6) encompasses pharmaceutical care but is not restricted to it.

The literature on pharmacogenomics as a tool to support antidepressant precision is burgeoning. Recently, a more active role has been argued for pharmacists in pharmacogenomic testing, with both pharmacists and family physicians perceiving pharmacist-led testing as a valuable method by which to scale this innovation for depression treatment. In this prospective, single-blind randomized controlled design, we evaluated the impact of pharmacogenomics guided versus standard antidepressant treatment of depression and anxiety, implemented in three large community pharmacies. Participants were 213 outpatients diagnosed with major depressive disorder and/ or generalized anxiety disorder, randomized to receive pharmacogenomics guided (n = 105) or standard antidepressant treatment (n = 108); participants were blinded to the study. Patient reported outcomes of depression, anxiety, disability, and treatment satisfaction were assessed at months 0, 1, 3, and 6. Hypotheses were investigated using mixed effect models on the full data. All clinical outcomes improved significantly. The primary outcome (depression) and two secondary outcomes (generalized anxiety and disability) exhibited significant time by group interactions indicating that they improved for participants who received pharmacogenomics guided treatment more so than they did for participants who received standard treatment. Treatment satisfaction improved similarly for both groups. Results contribute to a growing body of work evaluating the impact of pharmacogenomics testing to inform antidepressant medication treatment for depression and anxiety, and provides important initial evidence for the role of pharmacists in care delivery. Pharmacogenomic testing may be a valuable tool to allow pharmacists to more effectively collaborate in facilitating clinical treatment decisions. ClinicalTrials.gov registration: (NCT03591224). This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

ObjectiveTo analyse how non-adherence to prescribed treatments might be prevented, screened, assessed and managed in people with rheumatic and musculoskeletal diseases (RMDs).MethodsAn overview of systematic reviews (SR) was performed in four bibliographic databases. Research questions focused on: (1) effective interventions or strategies, (2) associated factors, (3) impact of shared decision making and effective communication, (4) practical things to prevent non-adherence, (5) effect of non-adherence on outcome, (6) screening and assessment tools and (7) responsible healthcare providers. The methodological quality of the reviews was assessed using AMSTAR-2. The qualitative synthesis focused on results and on the level of evidence attained from the studies included in the reviews.ResultsAfter reviewing 9908 titles, the overview included 38 SR on medication, 29 on non-pharmacological interventions and 28 on assessment. Content and quality of the included SR was very heterogeneous. The number of factors that may influence adherence exceed 700. Among 53 intervention studies, 54.7% showed a small statistically significant effect on adherence, and all three multicomponent interventions, including different modes of patient education and delivered by a variety of healthcare providers, showed a positive result in adherence to medication. No single assessment provided a comprehensive measure of adherence to either medication or exercise.ConclusionsThe results underscore the complexity of non-adherence, its changing pattern and dependence on multi-level factors, the need to involve all stakeholders in all steps, the absence of a gold standard for screening and the requirement of multi-component interventions to manage it.

ObjectivesRedispensing by pharmacies of medication unused by another patient could contribute to optimal use of healthcare resources. This study aimed to assess patient willingness to use medication returned by another patient and patient characteristics associated with this willingness.DesignCross-sectional survey.SettingA total of 41 community and 5 outpatient pharmacies in the Netherlands.ParticipantsTotal of 2215 pharmacy visitors.Primary and secondary outcome measuresPatients completed a questionnaire regarding their willingness to use medication returned unused to the pharmacy by another patient, assuming quality was guaranteed. Secondary outcome measures included patient sociodemographic characteristics that were associated with patient willingness, analysed using logistic regression analysis and reported as ORs with 95% CIs.ResultsOf the 2215 patients (mean (SD) age 50.6(18.0) years; 61.4% female), 61.2% were willing to use medication returned unused to the pharmacy by another patient. Patients who were unwilling mostly found it risky. Men were more willing to use returned medication (OR 1.3 95% CI 1.1 to 1.6), as did patients with a high educational level (OR 1.8 95% CI 1.3 to 2.5), those who regularly use 1–3 medications (OR 1.3 95% CI 1.1 to 1.7), those who returned medication to the pharmacy for disposal (OR 1.5 95% CI 1.0 to 2.3) and those who ever had unused medication themselves (OR 1.3 95% CI 1.1 to 1.6)). Patients with non-Dutch cultural background were less willing to use returned medication (OR 0.395% CI 0.3 to 0.4)).ConclusionsWhen quality is guaranteed, a substantial proportion of patients are willing to use medication returned unused to the pharmacy by another patient. This suggests that implementation of redispensing may be supported by patients.

Because practice-based data on the usage of idarucizumab for urgent dabigatran reversal is unavailable, we evaluated the appropriateness of idarucizumab usage, its haemostatic effectiveness and clinical outcomes.

Associations between the three measures are weak. Explanations are individual differences in the uptake of MTX, and little variance in adherence between patients. Moreover, the measurement domains differ: perceptions (CQR), behaviour (MEMS) and pharmacokinetics (MTX).

In immune-mediated inflammatory diseases (IMIDs), early symptom control is a key therapeutic goal. Methotrexate (MTX) is the first-line treatment across IMIDs. However, MTX is underutilized and suboptimally dosed, partly due to the inability of making individualized treatment decisions through therapeutic drug monitoring (TDM). To implement TDM in clinical practice, establishing a relationship between drug concentration and disease activity is paramount. In this meta-analysis, we investigated the relationship between concentrations of MTX polyglutamates (MTX-PG) in erythrocytes and efficacy as well as toxicity across IMIDs. Methods: Studies analysing MTX-PG in relation to disease activity and/or toxicity were included for inflammatory arthritis (rheumatoid [RA] and juvenile idiopathic arthritis [JIA]), inflammatory bowel disease (Crohn's and ulcerative colitis) and dermatitis (psoriasis and atopic dermatitis). Meta-analyses were performed resulting in several summary effect measures: regression coefficient (β), correlation coefficient and mean difference (of MTX-PG in responders vs. nonresponders) for IMIDs separately and collectively. Maartje M. van de Meeberg and Renske C.F. Hebing shared first author.