Knowledge support for optimising antibiotic prescribing for common infections in general practices: evaluation of the effectiveness of periodic feedback, decision support during consultations and peer comparisons in a cluster randomised trial (BRIT2) - study protocol.

INTRODUCTION: This project applies a Learning Healthcare System (LHS) approach to antibiotic prescribing for common infections in primary care. The approach involves iterations of data analysis, feedback to clinicians and implementation of quality improvement activities by the clinicians. The main research question is, can a knowledge support system (KSS) intervention within an LHS implementation improve antibiotic prescribing without increasing the risk of complications? METHODS AND ANALYSIS: A pragmatic cluster randomised controlled trial will be conducted, with randomisation of at least 112 general practices in North-West England. General practices participating in the trial will be randomised to the following interventions: periodic practice-level and individual prescriber feedback using dashboards; or the same dashboards plus a KSS. Data from large databases of healthcare records are used to characterise heterogeneity in antibiotic uses, and to calculate risk scores for clinical outcomes and for the effectiveness of different treatment strategies. The results provide the baseline content for the dashboards and KSS. The KSS comprises a display within the electronic health record used during the consultation; the prescriber (general practitioner or allied health professional) will answer standard questions about the patient's presentation and will then be presented with information (eg, patient's risk of complications from the infection) to guide decision making. The KSS can generate information sheets for patients, conveyed by the clinicians during consultations. The primary outcome is the practice-level rate of antibiotic prescribing (per 1000 patients) with secondary safety outcomes. The data from practices participating in the trial and the dashboard infrastructure will be held within regional shared care record systems of the National Health Service in the UK. ETHICS AND DISSEMINATION: Approved by National Health Service Ethics Committee IRAS 290050. The research results will be published in peer-reviewed journals and also disseminated to participating clinical staff and policy and guideline developers. TRIAL REGISTRATION NUMBER: ISRCTN16230629.

Educating the Healthcare Workforce to Support Digital Transformation.

Digital transformation of the healthcare workforce is a priority if we are to leverage the potential of digital technologies, artificial intelligence in clinical decision support and the potential of data captured within electronic health records. Educational programmes need to be diverse and support the digital novices through to the champions whom will be responsible for procuring and implementing digital solutions. In order to professionalise the workforce in this area, digital competencies need to be built into training from early on and be underpinned by frameworks that help to guide regulators and professional bodies and support educational providers to deliver them. Here we describe Manchester's involvement in the development of digital competency frameworks and our digital transformation education programmes that we have created, including a Massive Online Open Course and a professional development course for England's Topol Digital Fellows.

Prevalence and early-life risk factors of school-age allergic multimorbidity: The EuroPrevall-iFAAM birth cohort.

BACKGROUND: Coexistence of childhood asthma, eczema and allergic rhinitis is higher than can be expected by chance, suggesting a common mechanism. Data on allergic multimorbidity from a pan-European, population-based birth cohort study have been lacking. This study compares the prevalence and early-life risk factors of these diseases in European primary school children. METHODS: In the prospective multicentre observational EuroPrevall-iFAAM birth cohort study, we used standardized questionnaires on sociodemographics, medical history, parental allergies and lifestyle, and environmental exposures at birth, 12 and 24 months. At primary school age, parents answered ISAAC-based questions on current asthma, rhinitis and eczema. Allergic multimorbidity was defined as the coexistence of at least two of these. RESULTS: From 10,563 children recruited at birth in 8 study centres, we included data from 5,572 children (mean age 8.2 years; 51.8% boys). Prevalence estimates were as follows: asthma, 8.1%; allergic rhinitis, 13.3%; and eczema, 12.0%. Allergic multimorbidity was seen in 7.0% of the whole cohort, ranging from 1.2% (Athens, Greece) to 10.9% (Madrid, Spain). Risk factors for allergic multimorbidity, identified with AICc, included family-allergy-score, odds ratio (OR) 1.50 (95% CI 1.32-1.70) per standard deviation; early-life allergy symptoms, OR 2.72 (2.34-3.16) for each symptom; and caesarean birth, OR 1.35 (1.04-1.76). Female gender, OR 0.72 (0.58-0.90); older siblings, OR 0.79 (0.63-0.99); and day care, OR 0.81 (0.63-1.06) were protective factors. CONCLUSION: Allergic multimorbidity should be regarded as an important chronic childhood disease in Europe. Some of the associated early-life factors are modifiable and may be considered for prevention strategies.

Probabilistic Approaches to Overcome Content Heterogeneity in Data Integration: A Study Case in Systematic Lupus Erythematosus.

Integrating data from different sources into homogeneous dataset increases the opportunities to study human health. However, disparate data collections are often heterogeneous, which complicates their integration. In this paper, we focus on the issue of content heterogeneity in data integration. Traditional approaches for resolving content heterogeneity map all source datasets to a common data model that includes only shared data items, and thus omit all items that vary between datasets. Based on an example of three datasets in Systemic Lupus Erythematosus, we describe and experimentally evaluate a probabilistic data integration approach which propagates the uncertainty resulting from content heterogeneity into statistical inference, avoiding the need to map to a common data model.

Frequency of food allergy in school-aged children in eight European countries-The EuroPrevall-iFAAM birth cohort.

BACKGROUND: The prevalence of food allergy (FA) among European school children is poorly defined. Estimates have commonly been based on parent-reported symptoms. We aimed to estimate the frequency of FA and sensitization against food allergens in primary school children in eight European countries. METHODS: A follow-up assessment at age 6-10 years of a multicentre European birth cohort based was undertaken using an online parental questionnaire, clinical visits including structured interviews and skin prick tests (SPT). Children with suspected FA were scheduled for double-blind, placebo-controlled oral food challenges (DBPCFC). RESULTS: A total of 6105 children participated in this school-age follow-up (57.8% of 10 563 recruited at birth). For 982 of 6069 children (16.2%), parents reported adverse reactions after food consumption in the online questionnaire. Of 2288 children with parental face-to-face interviews and/or skin prick testing, 238 (10.4%) were eligible for a DBPCFC. Sixty-three foods were challenge-tested in 46 children. Twenty food challenges were positive in 17 children, including seven to hazelnut and three to peanut. Another seventy-one children were estimated to suffer FA among those who were eligible but refused DBPCFC. This yielded prevalence estimates for FA in school age between 1.4% (88 related to all 6105 participants of this follow-up) and 3.8% (88 related to 2289 with completed eligibility assessment). INTERPRETATION: In primary school children in eight European countries, the prevalence of FA was lower than expected even though parents of this cohort have become especially aware of allergic reactions to food. There was moderate variation between centres hampering valid regional comparisons.

Towards an Open Infrastructure for Relating Scholarly Assets.

Discovery of useful relationships between scholarly assets on the web is challenging, both in terms generating the right metadata around the assets, and in connecting all relevant digital entities in chain of provenance accessible to the whole community. This paper reports the development of a framework and tools enabling scholarly asset relationships to be expressed in a standard and open way, illustrated with use-cases of discovering new knowledge across cohort studies. The framework uses Research Objects for aggregation, distributed databases for storage, and distributed ledgers for provenance. Our proposal avoids management by a single central platform or organization, instead leveraging the use of existing resources and platforms across natural partnerships. Our proposed infrastructure will support a wide range of users from system administrators to researchers.

An eHealth Approach to Reporting Allergic Reactions to Food and Closing the Knowledge Gap.

There is an important knowledge gap in food allergy management in understanding the factors that determine allergic reactions to food, in gathering objective reports of reactions in real time, and in accessing patients' reaction-histories during consultations. We investigate how eHealth methods can close this knowledge gap. We report experiences with an online tool for reporting allergic reactions that we have developed as a web application. This application has been successfully validated by participants from Ireland and the UK, and is currently being used in a pilot where participants report allergic reactions in near-real time.

e-Labs and the stock of health method for simulating health policies.

Regional outcomes of national health policies are difficult to forecast. This is partly due to a lack of realistically complex models that can be used to appraise policy options and partly a lack of accessible and adaptable tools that can be used to simulate the consequences of policy decisions. These barriers might be overcome by exploiting the commoditization of massively parallel computing architectures, advances in machine learning, and the increased availability of large-scale linked healthcare data. This paper presents a novel modelling methodology, The Stock of Health, for harnessing emerging data and computational resources to simulate health policy, with application initially to coronary heart disease. We detail the use of multi-core graphical processing architectures to facilitate a micro-simulation approach. The simulation tools have been deployed through the IMPACT Framework. We explore how this framework can be extended to support the sharing and reuse of policy models and simulations based on the digital publishing concept of e-Lab.

IMPACT: A generalisable system for simulating public health interventions.

Populations are under-served by local health policies and management of resources, partly because of a lack of realistically complex models to enable a wide range of potential options to be appraised. Rising computing power coupled with advances in machine learning and healthcare information now enables such models to be constructed and executed. However, such models are not generally accessible to public health practitioners because they do not have the requisite technical knowledge or skills. This paper presents a system for creating, executing and analyzing the results of simulated public health and healthcare policy interventions, which is more accessible and usable by modellers and policy-makers alike.