The impact of trust in healthcare and medication, and beliefs about medication on medication adherence in a Dutch medication-using population

INTRODUCTION: Trust in healthcare and medication, defined as feelings of reassurance and confidence in the healthcare system or medication, may be a key prerequisite before engaging in the use of medication. However, earlier studies have focussed on beliefs about medication rather than trust as predictors of medication adherence. This study therefore aims to simultaneously explore the relationship of trust in healthcare, medication and beliefs about medication, with medication adherence.METHODS: In a cross-sectional study, an online questionnaire was sent out to 1500 members of the Dutch Health Care Consumer Panel of Nivel in November 2018. Respondents were asked to grade their level of trust in healthcare and medication (scale 1-10). The Beliefs About Medicines Questionnaire (BMQ) for general and specific medication beliefs was used to address beliefs, the Medication Adherence Report Scale (MARS-5) to measure medication adherence. Data were analysed using structural equation modelling (SEM) with a backward stepwise approach. Out of 753 people that completed the questionnaire, 407 people used prescription medication and were included in the analyses.RESULTS: A positive association between trust in medication and medication adherence was found (0.044, p &lt; 0.05). BMQ subscales Overuse (-0.083, p &lt; 0.05), Necessity (0.075, p &lt; 0.05) and Concerns (-0.134, p &lt; 0.01) related with medication adherence. BMQ subscale Harm did not relate to medication adherence.CONCLUSION: Trust in medication and beliefs about medication were both individually associated with medication adherence. Healthcare providers should therefore not only focus on patients' medication beliefs, but also on strengthening patients' trust in medication to improve medication adherence.</p

Better use of inhaled medication in asthma and COPD through training, preparation and counselling:the On TRACk study protocol for a cluster randomised controlled trial

Introduction About 70% of patients with asthma and/or chronic obstructive pulmonary disease (COPD) use their inhaled medication incorrectly, leading to reduced disease control, higher healthcare use and costs. Adequate guidance from the pharmacy team from first dispense onwards can benefit patients in the long run. We propose an intervention ('On TRACk') to improve medication adherence and inhaler technique of adult patients with asthma and/or COPD. This intervention focuses on training pharmacy technicians (PTs) in patient-centred communication and inhalation instruction skills. In addition, patients are actively involved in refill consultations at the pharmacy. The aim of this study is to improve inhaler technique and better inhaled medication adherence among patients with asthma and/or COPD. This paper describes the study protocol. Methods and analysis A cluster randomised controlled trial (RCT) with an intervention and control group of 15 pharmacies each will be conducted. Per intervention pharmacy, two PTs will be trained online. Each PT will include five patients who will prepare their second and third dispense counselling sessions by selecting three topics they wish to discuss. Pharmacies in the control cluster provide usual care. In total, 300 patients (150 per group) will be included. Up to 12 months after inclusion, patients complete 3-monthly follow-up questionnaires. Both a process evaluation and a cost-effectiveness analysis will be performed alongside the trial. Trial effectiveness on the patient level will be evaluated after the 12-month follow-up period. Patient data will be collected through questionnaires and pharmacy refill data. Patients' inhaler technique will be visually assessed by PTs. Semistructured interviews with PTs and patients will be conducted regarding implementation and fidelity. Direct and indirect health costs will be collected to assess cost-effectiveness. The primary outcome is adherence to inhalation maintenance medication measured with pharmacy refill data. Secondary outcomes are inhaler technique, persistence, patients' attitudes towards medication, self-efficacy in medication use and communication with their PTs. Ethics and dissemination The study was approved by the Vrije Universiteit Amsterdam Ethics Committee (number: 2020.358). Results will be presented at (inter)national conferences and published in peer-reviewed journals. If proven to be (cost-)effective, the intervention should be considered for reimbursement and implementation in Dutch community pharmacies

Mobile element insertions in rare diseases: a comparative benchmark and reanalysis of 60,000 exome samples

Mobile element insertions (MEIs) are a known cause of genetic disease but have been underexplored due to technical limitations of genetic testing methods. Various bioinformatic tools have been developed to identify MEIs in Next Generation Sequencing data. However, most tools have been developed specifically for genome sequencing (GS) data rather than exome sequencing (ES) data, which remains more widely used for routine diagnostic testing. In this study, we benchmarked six MEI detection tools (ERVcaller, MELT, Mobster, SCRAMble, TEMP2 and xTea) on ES data and on GS data from publicly available genomic samples (HG002, NA12878). For all the tools we evaluated sensitivity and precision of different filtering strategies. Results show that there were substantial differences in tool performance between ES and GS data. MELT performed best with ES data and its combination with SCRAMble increased substantially the detection rate of MEIs. By applying both tools to 10,890 ES samples from Solve-RD and 52,624 samples from Radboudumc we were able to diagnose 10 patients who had remained undiagnosed by conventional ES analysis until now. Our study shows that MELT and SCRAMble can be used reliably to identify clinically relevant MEIs in ES data. This may lead to an additional diagnosis for 1 in 3000 to 4000 patients in routine clinical ES

Solving patients with rare diseases through programmatic reanalysis of genome-phenome data.

Funder: EC | EC Seventh Framework Programm | FP7 Health (FP7-HEALTH - Specific Programme "Cooperation": Health); doi: https://doi.org/10.13039/100011272; Grant(s): 305444, 305444Funder: Ministerio de Economía y Competitividad (Ministry of Economy and Competitiveness); doi: https://doi.org/10.13039/501100003329Funder: Generalitat de Catalunya (Government of Catalonia); doi: https://doi.org/10.13039/501100002809Funder: EC | European Regional Development Fund (Europski Fond za Regionalni Razvoj); doi: https://doi.org/10.13039/501100008530Funder: Instituto Nacional de Bioinformática ELIXIR Implementation Studies Centro de Excelencia Severo OchoaFunder: EC | EC Seventh Framework Programm | FP7 Health (FP7-HEALTH - Specific Programme "Cooperation": Health)Reanalysis of inconclusive exome/genome sequencing data increases the diagnosis yield of patients with rare diseases. However, the cost and efforts required for reanalysis prevent its routine implementation in research and clinical environments. The Solve-RD project aims to reveal the molecular causes underlying undiagnosed rare diseases. One of the goals is to implement innovative approaches to reanalyse the exomes and genomes from thousands of well-studied undiagnosed cases. The raw genomic data is submitted to Solve-RD through the RD-Connect Genome-Phenome Analysis Platform (GPAP) together with standardised phenotypic and pedigree data. We have developed a programmatic workflow to reanalyse genome-phenome data. It uses the RD-Connect GPAP's Application Programming Interface (API) and relies on the big-data technologies upon which the system is built. We have applied the workflow to prioritise rare known pathogenic variants from 4411 undiagnosed cases. The queries returned an average of 1.45 variants per case, which first were evaluated in bulk by a panel of disease experts and afterwards specifically by the submitter of each case. A total of 120 index cases (21.2% of prioritised cases, 2.7% of all exome/genome-negative samples) have already been solved, with others being under investigation. The implementation of solutions as the one described here provide the technical framework to enable periodic case-level data re-evaluation in clinical settings, as recommended by the American College of Medical Genetics

Solve-RD: systematic pan-European data sharing and collaborative analysis to solve rare diseases.

For the first time in Europe hundreds of rare disease (RD) experts team up to actively share and jointly analyse existing patient's data. Solve-RD is a Horizon 2020-supported EU flagship project bringing together >300 clinicians, scientists, and patient representatives of 51 sites from 15 countries. Solve-RD is built upon a core group of four European Reference Networks (ERNs; ERN-ITHACA, ERN-RND, ERN-Euro NMD, ERN-GENTURIS) which annually see more than 270,000 RD patients with respective pathologies. The main ambition is to solve unsolved rare diseases for which a molecular cause is not yet known. This is achieved through an innovative clinical research environment that introduces novel ways to organise expertise and data. Two major approaches are being pursued (i) massive data re-analysis of >19,000 unsolved rare disease patients and (ii) novel combined -omics approaches. The minimum requirement to be eligible for the analysis activities is an inconclusive exome that can be shared with controlled access. The first preliminary data re-analysis has already diagnosed 255 cases form 8393 exomes/genome datasets. This unprecedented degree of collaboration focused on sharing of data and expertise shall identify many new disease genes and enable diagnosis of many so far undiagnosed patients from all over Europe

Twist exome capture allows for lower average sequence coverage in clinical exome sequencing

Background Exome and genome sequencing are the predominant techniques in the diagnosis and research of genetic disorders. Sufficient, uniform and reproducible/consistent sequence coverage is a main determinant for the sensitivity to detect single-nucleotide (SNVs) and copy number variants (CNVs). Here we compared the ability to obtain comprehensive exome coverage for recent exome capture kits and genome sequencing techniques. Results We compared three different widely used enrichment kits (Agilent SureSelect Human All Exon V5, Agilent SureSelect Human All Exon V7 and Twist Bioscience) as well as short-read and long-read WGS. We show that the Twist exome capture significantly improves complete coverage and coverage uniformity across coding regions compared to other exome capture kits. Twist performance is comparable to that of both short- and long-read whole genome sequencing. Additionally, we show that even at a reduced average coverage of 70× there is only minimal loss in sensitivity for SNV and CNV detection. Conclusion We conclude that exome sequencing with Twist represents a significant improvement and could be performed at lower sequence coverage compared to other exome capture techniques

A Solve-RD ClinVar-based reanalysis of 1522 index cases from ERN-ITHACA reveals common pitfalls and misinterpretations in exome sequencing

Purpose Within the Solve-RD project (https://solve-rd.eu/), the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies aimed to investigate whether a reanalysis of exomes from unsolved cases based on ClinVar annotations could establish additional diagnoses. We present the results of the “ClinVar low-hanging fruit” reanalysis, reasons for the failure of previous analyses, and lessons learned. Methods Data from the first 3576 exomes (1522 probands and 2054 relatives) collected from European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies was reanalyzed by the Solve-RD consortium by evaluating for the presence of single-nucleotide variant, and small insertions and deletions already reported as (likely) pathogenic in ClinVar. Variants were filtered according to frequency, genotype, and mode of inheritance and reinterpreted. Results We identified causal variants in 59 cases (3.9%), 50 of them also raised by other approaches and 9 leading to new diagnoses, highlighting interpretation challenges: variants in genes not known to be involved in human disease at the time of the first analysis, misleading genotypes, or variants undetected by local pipelines (variants in off-target regions, low quality filters, low allelic balance, or high frequency). Conclusion The “ClinVar low-hanging fruit” analysis represents an effective, fast, and easy approach to recover causal variants from exome sequencing data, herewith contributing to the reduction of the diagnostic deadlock

Implementation of an animated medication information tool in community pharmacies, with a special focus on patients with limited health literacy

Objectives The animated medication information tool 'Watchyourmeds' provides information in an accessible manner through animated videos and therefore appears to be especially suitable for people with limited health literacy. This study aimed to assess the implementation of this animated medication information tool in Dutch community pharmacies, with a special focus on patients with limited health literacy. Methods A cross-sectional survey based on the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework was sent to approximately 75% of the ±1900 community pharmacies in the Netherlands through email newsletters of pharmacy networks. Key findings 140 pharmacists (⁓10%) completed the survey and 125 of them (89%) indicated that they offered the animated medication information tool to their patients. 108 pharmacists indicated that the tool was offered to all patients, not only to patients with limited health literacy. The distribution method was primarily passive (patients were given a leaflet and were not explicitly pointed to or informed about the tool). Two frequently cited motivations for offering the tool were that it complemented other sources of information and that the health insurer provided a financial incentive. The main reasons patients refused to use the tool were that they had no access to or no affinity for the required technology. Conclusions This study demonstrated that the tool is used in community pharmacies and that it is offered to all patients, regardless of their presumed health literacy level. A more active method of offering the tool may be warranted to better reach patients with limited health literacy