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Populations

Oxford University Lead: Sarah Walker PHE co-lead: Susan Hopkins

We would like to better understand who is affected by antimicrobial resistance and healthcare-associated infections, and why, including the impact of inequalities and aging, and how we can monitor these conditions. Our strategy is to exploit large-scale linked electronic health record (EHR) data from multiple sources to answer the following key questions:

  • How can routine surveillance be automated optimally?
  • What populations are at greatest risk of different healthcare associated infections (HAI) and antimicrobial resistance (AMR)

By automating surveillance using EHR we hope to improve the monitoring and management of infectious diseases, reduce the burden of data collection in the NHS and better predict future trends in antimicrobial usage, HAI and AMR.

 

Populations Theme Publications

Healthcare-associated COVID-19 in England: a national data linkage study

Bhattacharya A, Collin S M, Stimson J, et al.

https://www.medrxiv.org/content/10.1101/2021.02.16.21251625v1

 

SARS-CoV-2 infection rates of antibody-positive compared with antibody-negative health-care workers in England: a large, multicentre, prospective cohort study (SIREN)

Hall V J, Foulkes S, Charlett A, et al.

https://linkinghub.elsevier.com/retrieve/pii/S0140-6736(21)00675-9

 

Antibody Status and Incidence of SARS-CoV-2 Infection in Health Care Workers

Lumley S F, O’Donnell D, Stoesser N, et al.

https://www.nejm.org/doi/full/10.1056/NEJMoa2034545

 

The Duration, Dynamics, and Determinants of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Antibody Responses in Individual Healthcare Workers

Lumley S, Wei J, O’ Donnell D, et al.

https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciab004/6064824.