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While the use of data such as QCovid to prioritise people for vaccination is a good idea, unfortunately the way in which this has been used to identify a new shielding group is yet another example of how things have been implemented badly during the pandemic.
Shielding is not a neutral measure as Williams notes (BMJ 2021;372:n382), with significant negative effects on people's wellbeing, and should be reserved for those at highest absolute risk of death from Covid-19. That is not what has been implemented: people are being identified based on increased relative risk, even if their absolute risk is tiny.
For example, a 32 year old with diabetes is at significantly greater relative risk than someone else of that age, but with an absolute risk as low as 0.005, a hundred times lower than the absolute risk threshold for shielding. But they are still identified for shielding based on their relative risk: having got through the worst of the pandemic, would you want to be told to shield now if your risk of dying was only 1:20000?
Worse still, the algorithm is identifying women who had gestational diabetes in pregnancy and treating them as diabetic when they have nothing currently wrong with them. Surely before something like this was implemented, the implications should have been tested and refined before large numbers of people were labelled unnecessarily, causing some to shield when their absolute risk is low and unnecessarily increasing the workload in primary care as many others call to find out why, when they are young and well, they are suddenly being told they are 'extremely vulnerable'. As with other aspects of the pandemic (BMJ 2021;372:n424), it is hard to support the actions of our leaders (who do have a difficult job to do) when things are this badly implemented.