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BackgroundThe first wave of the London COVID-19 epidemic peaked in April 2020. Attention initially focused on severe presentations, intensive care capacity, and the timely supply of equipment. While general practice has seen a rapid uptake of technology to allow for virtual consultations, little is known about the pattern of suspected COVID-19 presentations in primary care.AimTo quantify the prevalence and time course of clinically suspected COVID-19 presenting to general practices, to report the risk of suspected COVID-19 by ethnic group, and to identify whether differences by ethnicity can be explained by clinical data in the GP record.Design and settingCross-sectional study using anonymised data from the primary care records of approximately 1.2 million adults registered with 157 practices in four adjacent east London clinical commissioning groups. The study population includes 55% of people from ethnic minorities and is in the top decile of social deprivation in England.MethodSuspected COVID-19 cases were identified clinically and recorded using SNOMED codes. Explanatory variables included age, sex, self-reported ethnicity, and measures of social deprivation. Clinical factors included data on 16 long-term conditions, body mass index, and smoking status.ResultsGPs recorded 8985 suspected COVID-19 cases between 10 February and 30 April 2020.Univariate analysis showed a two-fold increase in the odds of suspected COVID-19 for South Asian and black adults compared with white adults. In a fully adjusted analysis that included clinical factors, South Asian patients had nearly twice the odds of suspected infection (odds ratio [OR] = 1.93, 95% confidence interval [CI] = 1.83 to 2.04). The OR for black patients was 1.47 (95% CI = 1.38 to 1.57).ConclusionUsing data from GP records, black and South Asian ethnicity remain as predictors of suspected COVID-19, with levels of risk similar to hospital admission reports. Further understanding of these differences requires social and occupational data.

Health City was established in the fall of 2018 as a Canadian not-for-profit corporation that works with numerous stakeholders to develop new pathways of care that can drive better health outcomes and economic development in the health sector. Data, artificial intelligence, and extended reality are technology platforms in healthcare that are highlighted in the context of Health City Initiatives presented here. Health City’s future area of focus in addressing challenges in procurement for health innovations is also discussed as a new approach that connects the health industry to healthcare. Health City has been an active stakeholder in health innovation in Edmonton and will continue to focus on developing a global niche and owning that space through meaningful partnerships and impactful projects. This will drive improved health outcomes and economic development for the Edmonton region and Canada that can be scaled globally.

ObjectiveThe Familial Hypercholesterolaemia Case Ascertainment Tool (FAMCAT) has been proposed to enhance case finding in primary care. In this study, we test application of the FAMCAT algorithm to describe risks of familial hypercholesterolaemia (FH) in a large unselected and ethnically diverse primary care cohort.MethodWe studied patients aged 18–65 years from three contiguous areas in inner London. We retrospectively applied the FAMCAT algorithm to routine primary care data and estimated the numbers of possible cases of FH and the potential service implications of subsequent investigation and management.ResultsOf the 777 128 patients studied, the FAMCAT score estimated between 11 736 and 23 798 (1.5%–3.1%) individuals were likely to have FH, depending on an assumed FH prevalence of 1 in 250 or 1 in 500, respectively. There was over-representation of individuals of South Asian ethnicity among those likely to have FH, with this cohort making up 41.9%–45.1% of the total estimated cases, a proportion which significantly exceeded their 26% representation in the study population.ConclusionsWe have demonstrated feasibility of application of the FAMCAT as an aid to case finding for FH using routinely recorded primary care data. Further research is needed on validity of the tool in different ethnic groups and more refined consideration of family history should be explored. While FAMCAT may aid case finding, implementation requires information on the cost-effectiveness of additional health services to investigate, diagnose and manage case ascertainment in those identified as likely to have FH.