QCovid prediction algorithm wrongly identifies high-risk patients

The reliability of an algorithm used to predict the risk of someone catching, being admitted to hospital, or dying from COVID-19 in England, UK, is in the spotlight for wrongly identifying some patients as high risk.

Based on data from 'the first few months of the pandemic', the University of Oxford-developed QCovid model took into account various socio-economic indicators, underlying health conditions such as diabetes and heart disease, body-mass index, and postcode (within Britain), among other factors, to return an 'absolute risk of a covid-19 associated death' or hospitalisation.

Following the launch of the tool, an additional 1.7 million people were instructed to shield, with around 800,000 people moved up the priority list to be vaccinated. These included women with previous gestational diabetes but who were healthy and could not understand why it was being recommended that they shield.

Some General Practitioners also described seeing healthy young men on the list. Young, healthy people are less likely to have measurements such as body weight recorded in their health records, Irene Petersen, professor of epidemiology and health informatics at University College London, told The Guardian.