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Background: Advancing the use of genomic data with routinely collected health data holds great promise for health care and research. Increasing the use of these data is a high priority to understand and address the causes of disease., Objective: This study aims to provide an outline of the use of genomic data alongside routinely collected data in health research to date. As this field prepares to move forward, it is important to take stock of the current state of play in order to highlight new avenues for development, identify challenges, and ensure that adequate data governance models are in place for safe and socially acceptable progress., Methods: We conducted a literature review to draw information from past studies that have used genomic and routinely collected data and conducted interviews with individuals who use these data for health research. We collected data on the following: the rationale of using genomic data in conjunction with routinely collected data, types of genomic and routinely collected data used, data sources, project approvals, governance and access models, and challenges encountered., Results: The main purpose of using genomic and routinely collected data was to conduct genome-wide and phenome-wide association studies. Routine data sources included electronic health records, disease and death registries, health insurance systems, and deprivation indices. The types of genomic data included polygenic risk scores, single nucleotide polymorphisms, and measures of genetic activity, and biobanks generally provided these data. Although the literature search showed that biobanks released data to researchers, the case studies revealed a growing tendency for use within a data safe haven. Challenges of working with these data revolved around data collection, data storage, technical, and data privacy issues., Conclusions: Using genomic and routinely collected data holds great promise for progressing health research. Several challenges are involved, particularly in terms of privacy. Overcoming these barriers will ensure that the use of these data to progress health research can be exploited to its full potential., (©Helen Daniels, Kerina Helen Jones, Sharon Heys, David Vincent Ford. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 24.09.2021.)

Objective: This study was undertaken to develop a novel pathway linking genetic data with routinely collected data for people with epilepsy, and to analyze the influence of rare, deleterious genetic variants on epilepsy outcomes. Methods: We linked whole‐exome sequencing (WES) data with routinely collected primary and secondary care data and natural language processing (NLP)‐derived seizure frequency information for people with epilepsy within the Secure Anonymised Information Linkage Databank. The study participants were adults who had consented to participate in the Swansea Neurology Biobank, Wales, between 2016 and 2018. DNA sequencing was carried out as part of the Epi25 collaboration. For each individual, we calculated the total number and cumulative burden of rare and predicted deleterious genetic variants and the total of rare and deleterious variants in epilepsy and drug metabolism genes. We compared these measures with the following outcomes: (1) no unscheduled hospital admissions versus unscheduled admissions for epilepsy, (2) antiseizure medication (ASM) monotherapy versus polytherapy, and (3) at least 1 year of seizure freedom versus <1 year of seizure freedom. Results: We linked genetic data for 107 individuals with epilepsy (52% female) to electronic health records. Twenty‐six percent had unscheduled hospital admissions, and 70% were prescribed ASM polytherapy. Seizure frequency information was linked for 100 individuals, and 10 were seizure‐free. There was no significant difference between the outcome groups in terms of the exome‐wide and gene‐based burden of rare and deleterious genetic variants. Significance: We successfully uploaded, annotated, and linked genetic sequence data and NLP‐derived seizure frequency data to anonymized health care records in this proof‐of‐concept study. We did not detect a genetic influence on real‐world epilepsy outcomes, but our study was limited by a small sample size. Future studies will require larger (WES) data to establish genetic variant contribution to epilepsy outcomes. [ABSTRACT FROM AUTHOR]

Introduction: It is widely acknowledged that breaches and misuses of health-related data can have serious implications and consequently they often carry penalties. However, harm due to the omission of health data usage, or data non-use, is a subject that lacks attention. A better understanding of this 'other side of the coin' is required before it can be addressed effectively.Approach: This article uses an international case study approach to explore why data non-use is difficult to ascertain, the sources and types of health-related data non-use, its implications for citizens and society and some of the reasons it occurs. It does this by focussing on issues with clinical care records, research data and governance frameworks and associated examples of non-use.Results and Discussion: The non-use of health-related data is a complex issue with multiple explanations. Individual instances of data non-use can be associated with harm, but taken together, they can describe a trail of data non-use that may complicate and compound its impacts. There is ample indirect evidence that health data non-use is implicated in the deaths of many thousands of people and potentially £billions in financial burdens to societies.Conclusions: Harm due to the non-use of health data is difficult to attribute unequivocally and actual proven evidence is sparse. Although it can be elusive, it is nevertheless a real problem with widespread and serious, if largely unquantifiable, consequences. The most effective initiatives to address specific contexts of data non-use will be those that: firstly, understand the pertinent sources, types and reasons for data non-use in a given domain in order to meet the challenges and create appropriate incentives and repercussions; and secondly, are cognisant of the multiple aspects to this complex issue in other domains to keep benefits and limitations in perspective, to move steadily towards socially responsible reuse of data becoming the norm to save lives and resources. [ABSTRACT FROM AUTHOR]

In order to fully understand and explore the effectiveness of any intervention for the management of multiple sclerosis (MS), it is important to have robust, valid, reliable, and universally applied measures. The recent article, ‘Disability outcome measures in multiple sclerosis clinical trials’ by Cohen, Reingold, Polman and Wolinsky (2012), explores this issue in regards to the effective measurement of MS-related disability, and the utilisation of patient-reported outcome measures, whilst highlighting the need for collaboration between the academic and clinical communities. Although it is important to examine disability measures, it is also equally important to recognise that physical function is only one aspect of a person’s experience; for example, quality of life and psychological well-being are also important aspects to assess. The application of e-health technologies and patient registers could be a useful method of gaining additional information, using patient-reported outcomes. This commentary explores these issues in relation to points raised by the Cohen et al. paper. [ABSTRACT FROM AUTHOR]