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PurposeThe purpose of this paper is to outline the challenges and potential solutions of initiating a Sharia law module within a UK law school.Design/methodology/approachThe approach is practical with focus placed on the local and international dimensions.FindingsSharia law is a popular module which adds to a law graduate's portfolio of international legal experience alongside the supplementary benefits provided to students attending from other disciplines. The advantages of interactions with local communities are also discussed.Originality/valueOnly a relatively small number of UK law schools run a module concerning Sharia or Islamic law, thus the paper facilitates other schools furthering the international aspects involved in the teaching and practice of law.

ABSTRACT
ObjectivesTo explore the application of automated zone design tools to protect record-level datasets with attribute detail and a large data volume in a way that might be implemented by a data provider (e.g. National Statistical Organisation/Health Service Provider), initially using a synthetic microdataset. Successful implementation could facilitate the release of rich linked record datasets to researchers so as to preserve small area geographical associations, while not revealing actual locations which are currently lost due to the high level of geographical coding required by data providers prior to release to researchers. Data perturbation is undesirable because of the need for detailed information on certain spatial attributes (e.g. distance to a medical practitioner, exposure to local environment) which has driven demand for new linked administrative datasets, along with provision of suitable research environments. The outcome is a bespoke aggregation of the microdata that meets a set of design constraints but the exact configuration of which is never revealed. Researchers are provided with detailed data and suitable geographies, yet with appropriately reduced disclosure risk.
ApproachUsing a synthetic flat file microdataset of individual records with locality-level (MSOA) geography codes for England and Wales (variables: age, gender, economic activity, marital status, occupation, number of hours worked and general health), we synthesize address-level locations within MSOAs using 2011 Census headcount data. These synthetic locations are then associated with a range of spatial measures and indicators such as distance to a medical practitioner. Implementation of the AZTool zone design software enables a bespoke, non-disclosive zone design solution, providing area codes that can be added to the research data without revealing their true locations to the researcher.
ResultsTwo sets of results will be presented. Firstly, we will explain the spatial characteristics of the new synthetic dataset which we propose may have broader utility. Secondly, we will present results showing changing risk of disclosure and utility when coding to spatial units from different scales and aggregations. Using the synthetic dataset will therefore demonstrate the utility of the approach for a variety of linked and administrative data without any actual disclosure risk.
ConclusionsThis approach is applicable to a variety of datasets. The ability to quantify the zone design solution and security in relation to statistical disclosure control will be discussed. Provision of parameters from the zone design process to the data user and the implications of this for security and data users will be considered.

In light of the economic deregulation and financial policy amendments, there is a great deal of capital-flows and interconnected financial activities between the developed and developing countries. As such, banking operations have become very complex and some financial institutions through their sheer reach, across geographies and markets have become 'too big to fail'. For example, international banking involves a variety of activities such as deposits/loans to countries but also covers cross-border operations, trade finance, foreign exchange, corresponding banking, international payment servi

BackgroundPOOL is a cohort study designed to establish whether waterbirth, compared to leaving a pool prior to birth, is as safe for mothers and infants. It is a novel case study using routine maternity and neonatal data for research purposes, including the adaptation and addition of locally collected electronic data at sites.
AimWe aimed to establish the feasibility of the study design, which combines data from maternity information systems(MIS) and neonatal data using a dissent-based linkage model and the addition of new variables into the MIS.
MethodsThe study will utilise individual level data entered into local MIS at 30 NHS Trusts linked with the National Neonatal Research Database(NNRD). New variables were added to one pilot site before rolling out to the remaining sites. A third party added the new variables, extracted data from the MIS to send to the study team and transferred data for matching to the NNRD, ensuring the study team receive only anonymised data. The feasibility study aimed to establish the quality and completion of the newly added variables, the levels of missingness for key outcomes from an extract of 4 years of retrospective data, and ensured the datasets could link using the study ID generated by our third party.
ResultsThe new variables were added into the MIS and data were extracted after a 10week pilot. New variables were present and had been entered as per specified metadata requirements. 23,140 records were extracted from the retrospective data and key variables assessed for missingness e.g.place of birth(<0.1% missing). The datasets could link using the studyID generated by the third party.
ConclusionThrough study set-up and in this pilot, we provide evidence that the main study is feasible, satisfies governance requirements and likely to generate data of sufficient quality to address our main research questions.

BACKGROUND: We aimed to study the effect of social containment mandates on ACS presentation during COVID-19 pandemic using location activity and mobility data from mobile phone map services. METHODS: We conducted a cross-sectional study using data from the Swedish Coronary Angiography and Angioplasty Registry (SCAAR) including all ACS presentations during the pandemic until May 07, 2020. Using a count regression model, we adjusted for day of the week, daily weather, and incidence of COVID-19. RESULTS: A 10% increase in activity around areas of residence was associated with 38% lower rates of ACS hospitalisations whereas increased activity relating to retail and recreation, grocery stores and pharmacies, workplaces as well as mode of mobility was associated with 10-20% higher rates of ACS hospitalisations. CONCLUSION: Government policy regarding social containment mandates has important public health implications for medical emergencies like ACS and may explain the decline in ACS presentations observed during COVID-19 pandemic.

BackgroundUsing routinely collected clinical data for observational research is an increasingly important method for data collection, especially when rare outcomes are being explored. The POOL study was commissioned to evaluate the safety of waterbirth in the UK using routine maternity and neonatal clinical data. This paper describes the design, rationale, set-up and pilot for this data linkage study using bespoke methods.
MethodsClinical maternity information systems hold many data items of value for research purposes, but often lack specific data items required for individual studies. This study used the novel method of amending an existing clinical maternity database for the purpose of collecting additional research data fields. In combination with the extraction of existing data fields, this maximised the potential use of existing routinely collected clinical data for research purposes, whilst reducing NHS staff data collection burden.
Wellbeing Software® provider of the Euroking® Maternity Information System, added new study specific data fields to their information system, extracted data from participating NHS sites and transferred data for matching with the National Neonatal Research Database to ascertain outcomes for babies admitted to neonatal units. Study set-up processes were put in place for all sites. The data extraction, linkage and cleaning processes were piloted with one pre-selected NHS site.
ResultsTwenty-six NHS sites were set-up over 27 months (January 2019 - April 2021). Twenty-four thousand maternity records were extracted from the one NHS site, pertaining to the period January 2015 to March 2019. Data field completeness for maternal and neonatal primary outcomes were mostly acceptable. Neonatal identifiers flowed to the National Neonatal Research Database for successful matching and linkage between maternity and neonatal unit records.
DiscussionPiloting the data extraction and linkage highlighted the need for additional governance arrangements, training at NHS sites and new processes for the study team to ensure data quality and confidentiality are upheld during the study. Amending existing NHS electronic information systems and accessing clinical data at scale, is possible, but continues to be a time consuming and a technically challenging exercise.

ABSTRACTObjectivesHealth services researchers are increasingly engaging with the emerging field of data science, but relatively few have the expertise to understand how innovative data linkage methodologies can, and cannot, be successfully applied in practice. There is little published guidance written specifically for this purpose.
We aimed to develop study design criteria to help researchers in considering whether these methods are appropriate for their projects. A secondary objective was to test the criteria in a case study, and evaluate the application of the data linkage approach.
ApproachClinical procedures requiring further research (according to the National Institute for Health and Care Excellence) were assessed against newly-developed CINDER criteria (Coverage; Identifiers; Numbers; Data; Existing records; Retrieval) to check the suitability of using data linkage methods. The CALON (Cardiac Ablation: Linking Outcomes for NICE) study was then established to evaluate outcomes of cardiac ablation procedures.
Records from the UK's Cardiac Rhythm Management (CRM) register were linked to routinely-recorded primary care, secondary care and mortality data in the Secure Anonymised Information Linkage (SAIL) Databank. Demographic profiles of patients identified from the register were compared with a group identified from SAIL. Outpatient attendances before and after ablation were compared using a Generalised Linear Mixed Model, assuming significance of p<0.05. Kaplan-Meier analyses were used to estimate survival.
Evaluation of methodological success concentrated on: adequately characterising patient populations from existing data; matching individuals between datasets; and data completeness/consistency.
ResultsThe linked dataset contained 2220 anonymised records. Almost all (99.7%) patients from the register were matched using deterministic and probabilistic techniques. These patients were similar to individuals identified from hospital records with respect to sex and comorbidity score (p>0.05); mean age differed by 1.6 years (95% CI 0.23-3.04; p=0.02).
Patients accessed 26.7% fewer hospital outpatient appointments after ablation (95% CI 23.4 to 29.8; p<0.001). There was no significant difference in the number of primary care events before and after ablation (95% CI -4.3 to 4.0; p=0.91). Survival was estimated at 91.0% after 5 years. Insufficient granularity of data precluded subgroup analyses.
ConclusionsData linkage can be used to evaluate outcomes of interventions, although there are limitations associated with secondary use of observational data. The CALON study identified a post-ablation reduction in hospital attendances, suggesting an overall improvement in general health.
We aim to publish the full CINDER checklist as a generic resource to facilitate assessment of the feasibility of using data linkage methods in other projects.

Advances in cancer treatments have improved clinical outcomes, leading to an increasing population of cancer survivors. However, this success is associated with high rates of short- and long-term cardiovascular (CV) toxicities. The number and variety of cancer drugs and CV toxicity types make long-term care a complex undertaking. This requires a multidisciplinary approach that includes expertise in oncology, cardiology and other related specialties, and has led to the development of the cardio-oncology subspecialty. This paper aims to provide an overview of the main adverse events, risk assessment and risk mitigation strategies, early diagnosis, medical and complementary strategies for prevention and management, and long-term follow-up strategies for patients at risk of cancer therapy-related cardiotoxicities. Research to better define strategies for early identification, follow-up and management is highly necessary. Although the academic cardio-oncology community may be the best vehicle to foster awareness and research in this field, additional stakeholders (industry, government agencies and patient organizations) must be involved to facilitate cross-discipline interactions and help in the design and funding of cardio-oncology trials. The overarching goals of cardio-oncology are to assist clinicians in providing optimal care for patients with cancer and cancer survivors, to provide insight into future areas of research and to search for collaborations with industry, funding bodies and patient advocates. However, many unmet needs remain. This document is the product of brainstorming presentations and active discussions held at the Cardiovascular Round Table workshop organized in January 2020 by the European Society of Cardiology.

Advances in cancer treatments have improved clinical outcomes, leading to an increasing population of cancer survivors. However, this success is associated with high rates of short- and long-term cardiovascular (CV) toxicities. The number and variety of cancer drugs and CV toxicity types make long-term care a complex undertaking. This requires a multidisciplinary approach that includes expertise in oncology, cardiology and other related specialties, and has led to the development of the cardio-oncology subspecialty. This paper aims to provide an overview of the main adverse events, risk assessment and risk mitigation strategies, early diagnosis, medical and complementary strategies for prevention and management, and long-term follow-up strategies for patients at risk of cancer therapy-related cardiotoxicities. Research to better define strategies for early identification, follow-up and management is highly necessary. Although the academic cardio-oncology community may be the best vehicle to foster awareness and research in this field, additional stakeholders (industry, government agencies and patient organizations) must be involved to facilitate cross-discipline interactions and help in the design and funding of cardio-oncology trials. The overarching goals of cardio-oncology are to assist clinicians in providing optimal care for patients with cancer and cancer survivors, to provide insight into future areas of research and to search for collaborations with industry, funding bodies and patient advocates. However, many unmet needs remain. This document is the product of brainstorming presentations and active discussions held at the Cardiovascular Round Table workshop organized in January 2020 by the European Society of Cardiology.

This work was supported by Health Data Research UK (HDR-9006; CFC0110) and the Medical Research Council (MR/S027750/1). Health Data Research UK is funded by: UK Medical Research Council; Engineering and Physical Sciences Research Council; Economic and Social Research Council; National Institute for Health Research (England); Chief Scientist Office of the Scottish Government Health and Social Care Directorates; Health and Social Care Research and Development Division (Welsh Government); Public Health Agency (Northern Ireland); British Heart Foundation and Wellcome Trust. ; Introduction Multimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence. We have created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbidity, characterise clusters that lead to highest burden on individuals and healthcare services, and evaluate and provide new multimorbidity phenotypes and algorithms to the National Health Service and research communities to support prevention, healthcare planning and the management of individuals with multimorbidity. Methods and analysis The WMC has been created and derived from multisourced demographic, administrative and electronic health record data relating to the Welsh population in the Secure Anonymised Information Linkage (SAIL) Databank. The WMC consists of 2.9 million people alive and living in Wales on the 1 January 2000 with follow-up until 31 December 2019, Welsh residency break or death. Published comorbidity indices and phenotype code lists will be used to measure and conceptualise multimorbidity.Study outcomes will include: (1) a description of multimorbidity using published data phenotype algorithms/ontologies, (2) investigation of the associations between baseline demographic factors and multimorbidity, (3) identification of temporal trajectories of clusters of conditions and multimorbidity and (4) investigation of multimorbidity clusters with poor outcomes such as mortality and high healthcare service utilisation. Ethics and dissemination The SAIL Databank independent Information Governance Review Panel has approved this study (SAIL Project: 0911). Study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals. ; Publisher PDF ; Peer reviewed

This work was supported by Health Data Research UK (HDR-9006; CFC0110) and the Medical Research Council (MR/S027750/1). Health Data Research UK is funded by: UK Medical Research Council; Engineering and Physical Sciences Research Council; Economic and Social Research Council; National Institute for Health Research (England); Chief Scientist Office of the Scottish Government Health and Social Care Directorates; Health and Social Care Research and Development Division (Welsh Government); Public Health Agency (Northern Ireland); British Heart Foundation and Wellcome Trust. ; Introduction Multimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence. We have created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbidity, characterise clusters that lead to highest burden on individuals and healthcare services, and evaluate and provide new multimorbidity phenotypes and algorithms to the National Health Service and research communities to support prevention, healthcare planning and the management of individuals with multimorbidity. Methods and analysis The WMC has been created and derived from multisourced demographic, administrative and electronic health record data relating to the Welsh population in the Secure Anonymised Information Linkage (SAIL) Databank. The WMC consists of 2.9 million people alive and living in Wales on the 1 January 2000 with follow-up until 31 December 2019, Welsh residency break or death. Published comorbidity indices and phenotype code lists will be used to measure and conceptualise multimorbidity. Study outcomes will include: (1) a description of multimorbidity using published data phenotype algorithms/ontologies, (2) investigation of the associations between baseline demographic factors and multimorbidity, (3) identification of temporal trajectories of clusters of conditions and multimorbidity and (4) investigation of multimorbidity clusters with poor outcomes such as mortality and high healthcare service utilisation. Ethics and dissemination The SAIL Databank independent Information Governance Review Panel has approved this study (SAIL Project: 0911). Study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals. ; Publisher PDF ; Peer reviewed