Research Case Studies
Case study examples of how we have enabled research across the UK.
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Four-nations study showed vaccine protection against severe COVID-19 outcomes wanes over time
Researchers, part of the EAVE II programme, analysed data from 12.9 million individuals across the UK to explore the effectiveness of COVID-19 vaccines.
Vital research that helped identify the priority groups for COVID-19 boosters
Research part-funded by HDR UK provided a detailed population-level analysis which was invaluable in guiding vaccine prioritisation decisions.
Feasibility and ethics of using data from the Scottish newborn blood spot archive for research
The HDR UK Impact Commitee selected a paper by Cunningham-Burley et al as the Open Impact Publication of the Month.
New research questions reliability of studies into Lassa fever drug
Researchers identified and re-analysed a series of studies to assess the reliability of the evidence backing the use of ribavirin as a treatment for Lassa fever.
CogStack information retrieval and extraction platform
CogStack represents a major advance in the capacity to extract and analyse unstructured data from electronic health records (EHRs). It uses a range of technologies to support modern open-source...
Biobank conversion allows greater UK contribution to international research
A team of researchers processed and converted over 1.3 billion rows of UK Biobank data to the Observational Medical Outcomes Partnership (OMOP) common data model (CDM), improving its usability for...
Multi-generational households, living alone and ethnicity – older people and the risk of severe COVID-19 outcomes
Researchers carried out an analysis of older people and the risk of severe COVID-19 outcomes using the OpenSAFELY research platform
New advances in understanding long COVID
Researchers analysed the primary healthcare records of 7.4 million patients in England to develop a long COVID phenotype, strengthening further research to improve the understanding of the condition.
Paving the way for quicker, simpler and less expensive use of scans to diagnose heart damage
Researchers have applied machine learning to improve the detection of heart damage through cardiac magnetic resonance (CMR) scans.