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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.

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