A large scale patient record study has analysed 17.4 million GP records, working with GP system supplier TPP, to explore key factors associated with death from COVID-19.
University of Oxford and the London School of Hygiene & Tropical Medicine, working on behalf of NHS England, have analysed research in the public interest to deepen the understanding of the COVID-19 virus and inform the government’s response.
The group has worked with TPP to analyse pseudonymised data under strict controls, no identifiable data can be extracted from the platform.
Of the records used in the study there were 5,707 deaths in hospitals attributed to COVID-19, with key factors found to be related to death included being male, older age, uncontrolled diabetes and severe asthma.
In addition people of Asian and Black ethnic backgrounds are at a higher risk of death and the report highlighted ‘contrary to prior speculation, this is only partially attributable to pre-existing clinical risk factors or deprivation.’
The study linked data about patients that had been hospitalised with COVID-19 with data held in primary care records processed by TPP. This was carried via the OpenSAFELY analytics platform, a mechanism which allowed the GP records to be linked where they are stored for individual care.
The paper is now being peer-reviewed.
Professor Liam Smeeth, Professor of Clinical Epidemiology at LSHTM, NHS doctor and co-lead on the study “We need highly accurate data on which patients are most at risk in order to manage the pandemic and improve patient care. The answers provided by this OpenSAFELY analysis are of crucial importance to countries around the world. For example, it is very concerning to see that the higher risks faced by people from BME backgrounds are not attributable to identifiable underlying health conditions.”
Further analyses using OpenSAFELY are already underway, including investigation into the effects of specific drugs routinely prescribed in primary care. The platform can also be used to evaluate COVID-19 spread with innovative approaches to modelling; predict local health service needs; assess the indirect health impacts of the pandemic; track the impact of national interventions; and inform exit from lockdown.
