Genomic epidemiology of COVID-19 in care homes in the East of England
- University of Cambridge, United Kingdom
- Wellcome Sanger Institute, United Kingdom
- Cambridgeshire County Council, United Kingdom
- University College London Hospitals NHS Foundation Trust, United Kingdom
- Public Health England, United Kingdom
- Public Health England (PHE), United Kingdom
- MRC-Laboratory of Molecular Biology, United Kingdom
- University of Oxford, United Kingdom
- Wellcome Trust Sanger Institute, United Kingdom
Abstract
COVID-19 poses a major challenge to care homes, as SARS-CoV-2 is readily transmitted and causes disproportionately severe disease in older people. Here, 1,167 residents from 337 care homes were identified from a dataset of 6,600 COVID-19 cases from the East of England. Older age and being a care home resident were associated with increased mortality. SARS-CoV-2 genomes were available for 700 residents from 292 care homes. By integrating genomic and temporal data, 409 viral clusters within the 292 homes were identified, indicating two different patterns - outbreaks among care home residents and independent introductions with limited onward transmission. Approximately 70% of residents in the genomic analysis were admitted to hospital during the study, providing extensive opportunities for transmission between care homes and hospitals. Limiting viral transmission within care homes should be a key target for infection control to reduce COVID-19 mortality in this population.
Data availability
The main analysis set comprised 700 genomes from care home residents. Additionally, a randomised selection of 700 genomes from non-care home residents was used for comparing lineage composition, and genomes from 76 healthcare workers tested at CUH were included for the analysis of care home resident-HCW transmission. Consensus fasta sequences for the 1,476 genomes are publicly accessible through the COG-UK website data section (https://www.cogconsortium.uk/data/). COG-UK also regularly deposits data into public databases such as GISAID (https://www.gisaid.org/). GISAID accession IDs and virus names are included in the Supplementary Materials. 13 samples were not added to GISAID as they did not pass GISAID's quality control filtering, but these fasta sequences are available in the COG-UK database.Sequences have associated public metadata (also available via the COG-UK website or GISAID), including patient age, sex, collection date (if available), and location to the level of UK county. However, not all of the metadata used in this study can be released publicly. COG-UK samples are sequenced under statutory powers granted to the UK Public Health Agencies. Matched patient data is securely released to the COG-UK consortium under a data sharing framework which strictly controls the handling of patient data. The status of individuals living in a care home and groups of such care home patients are both on the consortium restricted data list. This means that this data cannot be publicly released linked to sequencing identifiers, sampling date and UK counties. This is because of the risk of deductive disclosure. If a research scientist would like to repeat our analysis using these data fields, they should write to the corresponding authors to discuss the process of signing a data sharing agreement that will allow them to access the data securely.
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COVID-19 Genomics Consortium UKCOGUK, see Supplementary File 1 for identifiers.
Article and author information
Author details
Charlotte J Houldcroft
Michael Spencer Chapman
Funding
Medical Research Council (COG-UK grant to SJP)
- Sharon J Peacock
National Institute for Health Research (COG-UK grant to SJP)
- William L Hamilton
- Emily R Smith
- Sharon J Peacock
Genome Research Ltd (COG-UK grant to SJP)
- Sharon J Peacock
The Wellcome Trust (Senior Fellowship 097997/Z/11/Z)
- Ian G Goodfellow
Academy of Medical Sciences (Clinician Scientist Fellowship to MET)
- M Estee Torok
Health Foundation (Clinician Scientist Fellowship to MET)
- M Estee Torok
NIHR Cambridge Biomedical Research Centre
- Ben Warne
- Gordon Dougan
- M Estee Torok
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: This study was conducted as part of surveillance for COVID-19 infections under the auspices of Section 251 of the NHS Act 2006. It therefore did not require individual patient consent or ethical approval. The COG-UK study protocol was approved by the Public Health England Research Ethics Governance Group (reference: R&D NR0195).
Copyright
© 2021, Hamilton et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Genomic epidemiology of COVID-19 in care homes in the East of England
eLife 10:e64618.
https://doi.org/10.7554/eLife.64618
Further reading
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Background: The role of circulating metabolites on child development is understudied. We investigated associations between children's serum metabolome and early childhood development (ECD).
Methods: Untargeted metabolomics was performed on serum samples of 5,004 children aged 6-59 months, a subset of participants from the Brazilian National Survey on Child Nutrition (ENANI-2019). ECD was assessed using the Survey of Well-being of Young Children's milestones questionnaire. The graded response model was used to estimate developmental age. Developmental quotient (DQ) was calculated as the developmental age divided by chronological age. Partial least square regression selected metabolites with a variable importance projection ≥ 1. The interaction between significant metabolites and the child's age was tested.
Results: Twenty-eight top-ranked metabolites were included in linear regression models adjusted for the child's nutritional status, diet quality, and infant age. Cresol sulfate (β = -0.07; adjusted-p < 0.001), hippuric acid (β = -0.06; adjusted-p < 0.001), phenylacetylglutamine (β = -0.06; adjusted-p < 0.001), and trimethylamine-N-oxide (β = -0.05; adjusted-p = 0.002) showed inverse associations with DQ. We observed opposite directions in the association of DQ for creatinine (for children aged -1 SD: β = -0.05; p =0.01; +1 SD: β = 0.05; p =0.02) and methylhistidine (-1 SD: β = - 0.04; p =0.04; +1 SD: β = 0.04; p =0.03).
Conclusion: Serum biomarkers, including dietary and microbial-derived metabolites involved in the gut-brain axis, may potentially be used to track children at risk for developmental delays.
Funding: Supported by the Brazilian Ministry of Health and the Brazilian National Research Council.
