Data quality and patient characteristics in European ANCA-associated vasculitis registries: data retrieval by federated querying.

OBJECTIVES: This study aims to describe the data structure and harmonisation process, explore data quality and define characteristics, treatment, and outcomes of patients across six federated antineutrophil cytoplasmic antibody-associated vasculitis (AAV) registries. METHODS: Through creation of the vasculitis-specific Findable, Accessible, Interoperable, Reusable, VASCulitis ontology, we harmonised the registries and enabled semantic interoperability. We assessed data quality across the domains of uniqueness, consistency, completeness and correctness. Aggregated data were retrieved using the semantic query language SPARQL Protocol and Resource Description Framework Query Language (SPARQL) and outcome rates were assessed through random effects meta-analysis. RESULTS: A total of 5282 cases of AAV were identified. Uniqueness and data-type consistency were 100% across all assessed variables. Completeness and correctness varied from 49%-100% to 60%-100%, respectively. There were 2754 (52.1%) cases classified as granulomatosis with polyangiitis (GPA), 1580 (29.9%) as microscopic polyangiitis and 937 (17.7%) as eosinophilic GPA. The pattern of organ involvement included: lung in 3281 (65.1%), ear-nose-throat in 2860 (56.7%) and kidney in 2534 (50.2%). Intravenous cyclophosphamide was used as remission induction therapy in 982 (50.7%), rituximab in 505 (17.7%) and pulsed intravenous glucocorticoid use was highly variable (11%-91%). Overall mortality and incidence rates of end-stage kidney disease were 28.8 (95% CI 19.7 to 42.2) and 24.8 (95% CI 19.7 to 31.1) per 1000 patient-years, respectively. CONCLUSIONS: In the largest reported AAV cohort-study, we federated patient registries using semantic web technologies and highlighted concerns about data quality. The comparison of patient characteristics, treatment and outcomes was hampered by heterogeneous recruitment settings.

Can social prescribing put the 'social' into the biopsychosocial management of people with long-term musculoskeletal disorders?

BACKGROUND: Chronic musculoskeletal disorders (MSD) are a significant burden on individuals' quality of life and society and are made more complex by the presence of multimorbidity. It is recommended that interventions targeting MSD be sustainable, equitable and incorporate the biopsychosocial model of care (BPS). AIMS: A criticism of the BPS approach is that the social component of this model is not addressed adequately during the management of people with long-term MSD and that a gap exists between theory and implementation. The use of social prescribing (SP) as an intervention to bridge this gap is discussed. RESULTS AND DISCUSSION: Social prescribing is a holistic non-medical person-centered approach to well-being that utilizes link workers (LW) to support individuals with long-term conditions (LTC) in the community. Social prescribing referrals are received from primary healthcare practitioners to LW and range from light touch signposting for employment or financial advice to more intensive support for LTC such as obesity, decreased physical activity and mental health needs. CONCLUSION: There is evidence to suggest that SP interventions are effective in the management of LTC; however, due to the paucity of high-quality evidence, it is difficult to be conclusive. Large-scale randomised controlled trials are recommended to support the use of SP interventions in the management of LTC.

Experience and perceptions of Social Prescribing interventions; a qualitative study with people with long-term conditions, link workers and health care providers.

Background: Long-term conditions (LTC) are a leading cause of reduced quality of life and early mortality. People with LTC are living longer with increasing economic and social needs. Novel patient centred care pathways are required to support traditional medical management of these patients. Social Prescribing (SP) has gained popularity as a non-medical approach to support patients with LTC and their unmet health needs. The current focus group study aims to explore the experiences and perceptions to SP interventions from the perspective of people with long-term conditions, link workers, healthcare providers and community-based services. Methods: Six toeight participants will be recruited into three specific 60 to 90 minute focus groups relative to their role as a patient, link worker and community-based service. 8 to12 participants with a Health care provider and GP background will be interviewed individually online. The participants within these focus groups and semi-structured interviews will be invited to provide opinions on what factors they think are important to the successful implementation of a SP service from their respective stakeholder positions. The data will be recorded and exported to NVivo software for further analysis using Thematic Reflexive analysis methods. Coded categorical data will inform emerging themes from which a narrative summary will be consolidated and presented for dissemination. Conclusion: The conclusions made from this study will help inform the next study, which will aim to develop a pilot SP service for patients with long-term musculoskeletal conditions as part of an overall larger project.

The association between ambient UVB dose and ANCA-associated vasculitis relapse and onset.

BACKGROUND: The aetiology of ANCA-associated vasculitis (AAV) and triggers of relapse are poorly understood. Vitamin D (vitD) is an important immunomodulator, potentially responsible for the observed latitudinal differences between granulomatous and non-granulomatous AAV phenotypes. A narrow ultraviolet B spectrum induces vitD synthesis (vitD-UVB) via the skin. We hypothesised that prolonged periods of low ambient UVB (and by extension vitD deficiency) are associated with the granulomatous form of the disease and an increased risk of AAV relapse. METHODS: Patients with AAV recruited to the Irish Rare Kidney Disease (RKD) (n = 439) and UKIVAS (n = 1961) registries were studied. Exposure variables comprised latitude and measures of ambient vitD-UVB, including cumulative weighted UVB dose (CW-D-UVB), a well-validated vitD proxy. An n-of-1 study design was used to examine the relapse risk using only the RKD dataset. Multi-level models and logistic regression were used to examine the effect of predictors on AAV relapse risk, phenotype and serotype. RESULTS: Residential latitude was positively correlated (OR 1.41, 95% CI 1.14-1.74, p = 0.002) and average vitD-UVB negatively correlated (0.82, 0.70-0.99, p = 0.04) with relapse risk, with a stronger effect when restricting to winter measurements (0.71, 0.57-0.89, p = 0.002). However, these associations were not restricted to granulomatous phenotypes. We observed no clear relationship between latitude, vitD-UVB or CW-D-UVB and AAV phenotype or serotype. CONCLUSION: Our findings suggest that low winter ambient UVB and prolonged vitD status contribute to AAV relapse risk across all phenotypes. However, the development of a granulomatous phenotype does not appear to be directly vitD-mediated. Further research is needed to determine whether sufficient vitD status would reduce relapse propensity in AAV.

FAIRVASC: A semantic web approach to rare disease registry integration.

Rare disease data is often fragmented within multiple heterogeneous siloed regional disease registries, each containing a small number of cases. These data are particularly sensitive, as low subject counts make the identification of patients more likely, meaning registries are not inclined to share subject level data outside their registries. At the same time access to multiple rare disease datasets is important as it will lead to new research opportunities and analysis over larger cohorts. To enable this, two major challenges must therefore be overcome. The first is to integrate data at a semantic level, so that it is possible to query over registries and return results which are comparable. The second is to enable queries which do not take subject level data from the registries. To meet the first challenge, this paper presents the FAIRVASC ontology to manage data related to the rare disease anti-neutrophil cytoplasmic antibody (ANCA) associated vasculitis (AAV), which is based on the harmonisation of terms in seven European data registries. It has been built upon a set of key clinical questions developed by a team of experts in vasculitis selected from the registry sites and makes use of several standard classifications, such as Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT) and Orphacode. It also presents the method for adding semantic meaning to AAV data across the registries using the declarative Relational to Resource Description Framework Mapping Language (R2RML). To meet the second challenge a federated querying approach is presented for accessing aggregated and pseudonymized data, and which supports analysis of AAV data in a manner which protects patient privacy. For additional security the federated querying approach is augmented with a method for auditing queries (and the uplift process) using the provenance ontology (PROV-O) to track when queries and changes occur and by whom. The main contribution of this work is the successful application of semantic web technologies and federated queries to provide a novel infrastructure that can readily incorporate additional registries, thus providing access to harmonised data relating to unprecedented numbers of patients with rare disease, while also meeting data privacy and security concerns.

Effect of Functional Rehabilitation on Performance of the Star Excursion Balance Test Among Recreational Athletes With Chronic Ankle Instability: A Systematic Review.

OBJECTIVE: To determine (1) the effectiveness of rehabilitation for chronic ankle instability as measured by the Star Excursion Balance Test (SEBT) and (2) the relative efficacy and the long-term effects of these rehabilitation interventions. DATA SOURCES: Ten electronic databases were searched (2009-2019). STUDY SELECTION: Included articles were randomized controlled trials in English investigating recreational athletes aged ≥18 years with chronic ankle instability. At least 1 functional rehabilitation intervention had to be included and the SEBT test (or the modified version) used as an outcome measure. DATA EXTRACTION: Two researchers (L.A., O.N.) extracted data regarding participant demographics; intervention characteristics; trial size; and results at baseline, postintervention, and at follow-up, where appropriate. DATA SYNTHESIS: A systematic review and narrative synthesis was conducted. Methodological quality of included studies was assessed using the Cochrane Risk of Bias Tool and the van Tulder scale. The review was registered with PROSPERO (ID: 164468). Ten studies (n=368), 2 high-quality, 1 moderate-quality, and 7 low-quality, were included in the review. Interventions included balance training, strength training, vibration training, and mixed training. Results suggest that rehabilitation of chronic ankle instability that includes wobble board exercises (average percentage change: 14.3%) and hip strengthening exercises (average percentage change: 12.8%) are most effective. Few studies compared different types of rehabilitation for chronic ankle instability. However, improvements on the SEBT suggest that a rehabilitation program focusing on wobble board training and hip strengthening performed 3 times weekly for 4-6 weeks is the optimal rehabilitation program to improve dynamic postural control in recreational athletes with chronic ankle instability. CONCLUSIONS: Few studies directly compared different rehabilitation interventions, and there was limited long-term follow-up; therefore, the relative efficacy of different rehabilitation programs remains unclear. However, it seems that rehabilitation of chronic ankle instability should include proprioceptive and strengthening exercises of relatively short duration.

Cyberbullying severity detection: A machine learning approach.

With widespread usage of online social networks and its popularity, social networking platforms have given us incalculable opportunities than ever before, and its benefits are undeniable. Despite benefits, people may be humiliated, insulted, bullied, and harassed by anonymous users, strangers, or peers. In this study, we have proposed a cyberbullying detection framework to generate features from Twitter content by leveraging a pointwise mutual information technique. Based on these features, we developed a supervised machine learning solution for cyberbullying detection and multi-class categorization of its severity in Twitter. In the study we applied Embedding, Sentiment, and Lexicon features along with PMI-semantic orientation. Extracted features were applied with Naïve Bayes, KNN, Decision Tree, Random Forest, and Support Vector Machine algorithms. Results from experiments with our proposed framework in a multi-class setting are promising both with respect to Kappa, classifier accuracy and f-measure metrics, as well as in a binary setting. These results indicate that our proposed framework provides a feasible solution to detect cyberbullying behavior and its severity in online social networks. Finally, we compared the results of proposed and baseline features with other machine learning algorithms. Findings of the comparison indicate the significance of the proposed features in cyberbullying detection.

"Just-in-time" generation of datasets by considering structured representations of given consent for GDPR compliance.

Data processing is increasingly becoming the subject of various policies and regulations, such as the European General Data Protection Regulation (GDPR) that came into effect in May 2018. One important aspect of GDPR is informed consent, which captures one's permission for using one's personal information for specific data processing purposes. Organizations must demonstrate that they comply with these policies. The fines that come with non-compliance are of such importance that it has driven research in facilitating compliance verification. The state-of-the-art primarily focuses on, for instance, the analysis of prescriptive models and posthoc analysis on logs to check whether data processing is compliant to GDPR. We argue that GDPR compliance can be facilitated by ensuring datasets used in processing activities are compliant with consent from the very start. The problem addressed in this paper is how we can generate datasets that comply with given consent "just-in-time". We propose RDF and OWL ontologies to represent the consent that an organization has collected and its relationship with data processing purposes. We use this ontology to annotate schemas, allowing us to generate declarative mappings that transform (relational) data into RDF driven by the annotations. We furthermore demonstrate how we can create compliant datasets by altering the results of the mapping. The use of RDF and OWL allows us to implement the entire process in a declarative manner using SPARQL. We have integrated all components in a service that furthermore captures provenance information for each step, further contributing to the transparency that is needed towards facilitating compliance verification. We demonstrate the approach with a synthetic dataset simulating users (re-)giving, withdrawing, and rejecting their consent on data processing purposes of systems. In summary, it is argued that the approach facilitates transparency and compliance verification from the start, reducing the need for posthoc compliance analysis common in the state-of-the-art.

Data linkage in medical science using the resource description framework: the AVERT model.

There is an ongoing challenge as to how best manage and understand 'big data' in precision medicine settings. This paper describes the potential for a Linked Data approach, using a Resource Description Framework (RDF) model, to combine multiple datasets with temporal and spatial elements of varying dimensionality. This "AVERT model" provides a framework for converting multiple standalone files of various formats, from both clinical and environmental settings, into a single data source. This data source can thereafter be queried effectively, shared with outside parties, more easily understood by multiple stakeholders using standardized vocabularies, incorporating provenance metadata and supporting temporo-spatial reasoning. The approach has further advantages in terms of data sharing, security and subsequent analysis. We use a case study relating to anti-Glomerular Basement Membrane (GBM) disease, a rare autoimmune condition, to illustrate a technical proof of concept for the AVERT model.

Methods for Practising Ethics in Research and Innovation: A Literature Review, Critical Analysis and Recommendations.

This paper provides a systematic literature review, analysis and discussion of methods that are proposed to practise ethics in research and innovation (R&I). Ethical considerations concerning the impacts of R&I are increasingly important, due to the quickening pace of technological innovation and the ubiquitous use of the outcomes of R&I processes in society. For this reason, several methods for practising ethics have been developed in different fields of R&I. The paper first of all presents a systematic search of academic sources that present and discuss such methods. Secondly, it provides a categorisation of these methods according to three main kinds: (1) ex ante methods, dealing with emerging technologies, (2) intra methods, dealing with technology design, and (3) ex post methods, dealing with ethical analysis of existing technologies. Thirdly, it discusses the methods by considering problems in the way they deal with the uncertainty of technological change, ethical technology design, the identification, analysis and resolving of ethical impacts of technologies and stakeholder participation. The results and discussion of our literature review are valuable for gaining an overview of the state of the art and serve as an outline of a future research agenda of methods for practising ethics in R&I.

Mobile Medical Apps and mHealth Devices: A Framework to Build Medical Apps and mHealth Devices in an Ethical Manner to Promote Safer Use - A Literature Review.

This paper presents a preliminary literature review in the area of ethics in the development of Mobile Medical Apps and mHealth. The review included both direct health apps and also apps marketed under the area of well-being in addition to mHealth devices. The following words and combinations of them were used to carry out the search for publications, mHealth, Apps, Ethics. The search engines used were Google Scholar, and PubMed. The paper is restricted to publications since 2012. The total number of papers found was 1,920 of which 84 were reviewed. The reason for so few being reviewed was that the majority only considered security. The search revealed many papers dealing with security for all types of apps and mHealth devices but there are very few papers dealing with the ethical issues related to Apps or mHealth devices in the area. It is noted however that the number of apps is increasing in number exponentially and therefore it is argued that it is necessary to pay attention to the ethical aspects. There are now estimated to be 165,000 apps available in this area. How ethics are addressed in health and well-being apps is important as they can have an effect on the health of the individual using them. In a similar way, the need for addressing ethical issues for development of well-being apps is evident. In a study [1] it was noted that even though Electronic Health Record (EHR) was the highest ranked tablet-related task only one third of clinicians said that EHR was optimized for smartphones. When apps are integrated with the EHR they fully optimize productivity. In the same study the significant challenges identified included the method of evaluation and selection of mobile health solutions in order to ensure that clinical outcomes, care and efficiency are included. Security is mentioned but again wider ethical issues were not a consideration. From the literature review it is clear that there is a need for guidelines for how developers of medical ad well-being apps and mHealth devices should address ethical issues during development, and the generation of these guidelines is the subject of ongoing research by the authors.