Long COVID will have long-lasting impacts on human resources and the economy.

First published: March 2022.

Some pretty shocking data from the ONS on long COVID. The 4-week long COVID estimates include infections until the end of December 2021, so including the first two weeks after the Omicron variant became dominant. There are clear increases in prevalence being seen already.

Overall prevalence has increased to 1.5 million when considering the 28-day definition – that means 2.4% of our population. That’s 1 in 42 people having persistent symptoms for 4 weeks or more currently. If you consider the 12-week definition it’s still very high: 1.7%.

685,000 people are estimated to have now had persistent symptoms for more than one year. Around 1 million say this affects their day to day activity to some extent. So very functionally relevant, and longstanding symptoms in a very large population.

Who is being affected?

Young people, poor people, and those in high-exposure occupations: greatest prevalence (approaching 4%) in teaching and health and social care occupations.

Prevalence of ongoing symptoms following coronavirus (COVID-19) infection in the UK: 3 March 2022. | ONS

Children are also affected: 119,000 children now with long COVID based on the 28-day definition of whom 21,000 now have had persistent symptoms for more than a year.

I cannot believe that we are essentially completely ignoring a condition that has affected 1.5 million people, of which 700,000 people affected now for more than a year.

Ignoring long COVID will be our undoing: given the huge impact it’s clearly having.

Even from a purely economic standpoint, if that is all policy makers care about, this is hugely significant. Even if they don’t care about people’s health, they should care about this because it will have long-lasting impacts on human resources and the economy.

And remember the full impact of the omicron wave hasn’t been felt in these estimates yet. That will only be known in the coming months.

PMP Magazine

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Dr Deepti Gurdasani, Senior Lecturer in Epidemiology, Statistical Genetics, Machine Learning, Queen Mary University of London.

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