UK Prescribing Safety Assessment (PSA): The development, implementation and outcomes of a national online prescribing assessment.

AIMS: The United Kingdom (UK) Prescribing Safety Assessment (PSA) is a 2-h online assessment of basic competence to prescribe and supervise the use of medicines. It has been undertaken by students and doctors in UK medical and foundation schools for the past decade. This study describes the academic characteristics and performance of the assessment; longitudinal performance of candidates and schools; stakeholder feedback; and surrogate markers of prescribing safety in UK healthcare practice. METHODS: We reviewed the performance data generated by over 70 000 medical students and 3700 foundation doctors who have participated in the PSA since its inception in 2013. These data were supplemented by Likert scale and free text feedback from candidates and a variety of stakeholder groups. Further data on medication incidents, collected by national reporting systems and the regulatory body, are reported, with permission. RESULTS: We demonstrate the feasibility, high quality and reliability of an online prescribing assessment, uniquely providing a measure of prescribing competence against a national standard. Over 90% of candidates pass the PSA on their first attempt, while a minority are identified for further training and assessment. The pass rate shows some variation between different institutions and between undergraduate and foundation cohorts. Most responders to a national survey agreed that the PSA is a useful instrument for assessing prescribing competence, and an independent review has recommended adding the PSA to the Medical Licensing Assessment. Surrogate markers suggest there has been improvement in prescribing safety in practice, temporally associated with the introduction of the PSA but other factors could be influential too. CONCLUSIONS: The PSA is a practical and cost-effective way of delivering a reliable national assessment of prescribing competence that has educational impact and is supported by the majority of stakeholders. There is a need to develop national systems to identify and report prescribing errors and the harm they cause, enabling the impact of educational interventions to be measured.

British South Asian ancestry participants views of pharmacogenomics clinical implementation and research: a thematic analysis.

BACKGROUND: South Asian ancestry populations are underrepresented in genomic studies and therapeutics trials. British South Asians suffer from multi-morbidity leading to polypharmacy. Our objective was to elucidate British South Asian ancestry community perspectives on pharmacogenomic implementation and sharing pharmacogenomic clinical data for research. METHODS: Four focus groups were conducted (9-12 participants in each). Two groups were mixed gender, while one group was male only and one was female only. Simultaneous interpretation was available to participants in Urdu and Bengali. Focus groups were recorded and abridged transcription and thematic analysis were undertaken. RESULTS: There were 42 participants, 64% female. 26% were born in the UK or Europe. 52% were born in Bangladesh and 17% in Pakistan. 36% reported university level education. Implementation of pharmacogenomics was perceived to be beneficial to individuals but pose a risk of overburdening resource limited systems. Pharmacogenomic research was perceived to be beneficial to the community, with concerns about data privacy and misuse. Data sharing was desirable if the researchers did not have a financial stake, and benefits would be shared. Trust was the key condition for the acceptability of both clinical implementation and research. Trust was linked with medication compliance. Education, outreach, and communication facilitate trust. CONCLUSIONS (SIGNIFICANCE AND IMPACT OF THE STUDY): Pharmacogenomics implementation with appropriate education and communication has the potential to enhance trust and contribute to increased medication compliance. Trust drives data sharing, which would enable enhanced representation in research. Representation in scientific evidence base could cyclically enhance trust and compliance.

SLCO1B1*5 is protective against non-senile cataracts in cohort prescribed statins: analysis in a British-South Asian cohort.

BACKGROUND: Reported association between statin use and cataract risk is controversial. The SLCO1B1 gene encodes a transport protein responsible for statin clearance. The aim of this study was to investigate a possible association between the SLCO1B1*5 reduced function variant and cataract risk in statin users of South Asian ethnicity. METHODS: The Genes & Health cohort consists of British-Bangladeshi and British-Pakistani participants from East London, Manchester and Bradford, UK. SLCO1B1*5 genotype was assessed with the Illumina GSAMD-24v3-0-EA chip. Medication data from primary care health record linkage was used to compare those who had regularly used statins compared to those who had not. Multivariable logistic regression was used to test for association between statin use and cataracts, adjusting for population characteristics and potential confounders in 36,513 participants. Multivariable logistic regression was used to test association between SLCO1B1*5 heterozygotes or homozygotes and cataracts, in subgroups having been regularly prescribed statins versus not. RESULTS: Statins were prescribed to 35% (12,704) of participants (average age 41 years old, 45% male). Non-senile cataract was diagnosed in 5% (1686) of participants. An apparent association between statins and non-senile cataract (12% in statin users and 0.8% in non-statin users) was negated by inclusion of confounders. In those prescribed a statin, presence of the SLCO1B1*5 genotype was independently associated with a decreased risk of non-senile cataract (OR 0.7 (CI 0.5-0.9, p 0.007)). CONCLUSIONS: Our findings suggest that there is no independent association between statin use and non-senile cataract risk after adjusting for confounders. Among statin users, the SLCO1B1*5 genotype is associated with a 30% risk reduction of non-senile cataracts. Stratification of on-drug cohorts by validated pharmacogenomic variants is a useful tool to support or repudiate adverse drug events in observational cohorts.

Rare disease gene association discovery from burden analysis of the 100,000 Genomes Project data.

To discover rare disease-gene associations, we developed a gene burden analytical framework and applied it to rare, protein-coding variants from whole genome sequencing of 35,008 cases with rare diseases and their family members recruited to the 100,000 Genomes Project (100KGP). Following in silico triaging of the results, 88 novel associations were identified including 38 with existing experimental evidence. We have published the confirmation of one of these associations, hereditary ataxia with UCHL1 , and independent confirmatory evidence has recently been published for four more. We highlight a further seven compelling associations: hypertrophic cardiomyopathy with DYSF and SLC4A3 where both genes show high/specific heart expression and existing associations to skeletal dystrophies or short QT syndrome respectively; monogenic diabetes with UNC13A with a known role in the regulation of ß cells and a mouse model with impaired glucose tolerance; epilepsy with KCNQ1 where a mouse model shows seizures and the existing long QT syndrome association may be linked; early onset Parkinson's disease with RYR1 with existing links to tremor pathophysiology and a mouse model with neurological phenotypes; anterior segment ocular abnormalities associated with POMK showing expression in corneal cells and with a zebrafish model with developmental ocular abnormalities; and cystic kidney disease with COL4A3 showing high renal expression and prior evidence for a digenic or modifying role in renal disease. Confirmation of all 88 associations would lead to potential diagnoses in 456 molecularly undiagnosed cases within the 100KGP, as well as other rare disease patients worldwide, highlighting the clinical impact of a large-scale statistical approach to rare disease gene discovery.