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IntroductionIndividuals with COVID-19 frequently experience symptoms and impaired quality of life beyond 4-12 weeks, commonly referred to as Long COVID. Whether Long COVID is one or several distinct syndromes is unknown. Establishing the evidence base for appropriate therapies is needed. We aim to evaluate the symptom burden and underlying pathophysiology of Long COVID syndromes in non-hospitalised individuals and evaluate potential therapies. Methods and analysisA cohort of 4000 non-hospitalised individuals with a past COVID-19 diagnosis and 1000 matched controls will be selected from anonymised primary care records from the Clinical Practice Research Datalink (CPRD) and invited by their general practitioners to participate on a digital platform (Atom5). Individuals will report symptoms, quality of life, work capability, and patient reported outcome measures. Data will be collected monthly for one year. Statistical clustering methods will be used to identify distinct Long COVID symptom clusters. Individuals from the four most prevalent clusters and two control groups will be invited to participate in the BioWear sub-study which will further phenotype Long COVID symptom clusters by measurement of immunological parameters and actigraphy. We will review existing evidence on interventions for post-viral syndromes and Long COVID to map and prioritise interventions for each newly characterised Long COVID syndrome. Recommendations will be made using the cumulated evidence in an expert consensus workshop. A virtual supportive intervention will be coproduced with patients and health service providers for future evaluation. Individuals with lived experience of Long COVID will be involved throughout this programme through a patient and public involvement group. Ethics and disseminationEthical approval was obtained from the Solihull Research Ethics Committee, West Midlands (21/WM/0203). The study is registered on the ISRCTN Registry (1567490). Research findings will be presented at international conferences, in peer-reviewed journals, to Long COVID patient support groups and to policymakers. Article SummaryO_ST_ABSStrengths and limitations of the studyC_ST_ABSO_LIThe study will generate a nationally representative cohort of individuals with Long COVID recruited from primary care. C_LIO_LIWe will recruit controls matched on a wide range of demographic and clinical factors to assess differences in symptoms between people with Long COVID and similar individuals without a history of COVID-19. C_LIO_LIWe will use a newly developed electronic patient reported outcome measure (Symptom Burden Questionnaire) for Long COVID to comprehensively assess a wide range of symptoms highlighted by existing literature, patients, and clinicians. C_LIO_LIImmunological, proteomic, genetic, and wearable data captured in the study will allow deep phenotyping of Long COVID syndromes to help better target therapies. C_LIO_LIA limitation is that a significant proportion of non-hospitalised individuals affected by COVID-19 in the first wave of the pandemic will lack confirmatory testing and will be excluded from recruitment to the study. C_LI
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ObjectivesTo undertake a preliminary hypothesis-generating analysis exploring putative risk factors for coronavirus diseae 2019 (COVID-19) population-adjusted deaths, compared with non-COVID-19 related deaths, at a local authority district (LAD) level in hospital, care homes and at home. DesignEcological retrospective cohort study SettingLocal authority districts (LADs) in England, Scotland and Wales (Great Britain (GB)). ParticipantsAll LAD deaths registered by week 16 of 2020. Main Outcome MeasuresDeath registration where COVID-19 is mentioned as a contributing factor per 100,000 people in all settings, and in i) cares homes, ii) hospitals or iii) home only, in comparison to non-COVID-19 related deaths. ResultsAcross GB by week 16 of 2020, 20,684 deaths had been registered mentioning COVID-19, equivalent to 25.6 per 100,000 people. Significant risk factors for LAD COVID-19 death in comparison to non-COVID-19 related death were air pollution and proportion of the population who were female. Significant protective factors were higher air temperature and proportion of the population who were ex-smokers. Conversely, for all COVID-19 unrelated deaths in comparison to COVID-19 deaths, higher rates of communal living, higher population rates of chronic kidney disease, chronic obstructive pulmonary disease, cerebrovascular disease deaths under 75 and dementia were predictive of death, whereas, higher rates of flight passengers was protective. Looking at individual setttings, the most notable findings in care homes was Scotland being a significant risk factor for COVID-19 related deaths compared to England. For hospital setting, the proportion of the population who were from black and Asian minority ethnic (BAME) groups significantly predicted COVID-19 related death. ConclusionsThis is the first study within GB to assess COVID-19 related deaths in comparison to COVID-19 unrelated deaths across hospital, care homes and home combined. As an ecological study, the results cannot be directly extrapolated to individuals. However, the analysis may be informative for public health policy and protective measures. From our hypothesis-generating analysis, we propose that air pollution is a significant risk factor and high temperature a significant protective factor for COVID-19 related deaths. These factors cannot readily be modelled at an individual level. Scottish local authorities and local authorities with a higher proportion of individuals of BAME origin are potential risk factors for COVID-19 related deaths in care homes and in hospitals, respectively. Altogether, this analysis shows the benefits of access to high quality open data for public information, public health policy and further research.