Updated: July 11, 2020 5:02:52 pm
A study published in the journal ‘Nature’ on Wednesday – an analysis of over 17 million people in England (40 per cent of the English population) – has quantified a range of clinical risk factors for deaths associated with Covid-19. The authors of the study claim that it is the largest cohort study conducted by any country to date.
What does the study say?
In the study, the researchers linked the data of more than 17 million adults to 10,000 of those who died due to the disease. They found that deaths were associated with being male, older age, deprivation, diabetes and severe asthma, among other factors.
They also note that compared with white people, Blacks and South Asians were at a higher risk of developing poorer outcomes due to the disease. Out of the 17 million people in the study, 11 per cent had non-white ethnicities.
Significantly, they found that old age was “strongly associated” with risk, with those 80 years or above at 20-fold increased risk than those between the age 50-59 years. As per them, most comorbidities were associated with increased risk – these include cardiovascular diseases, diabetes, respiratory disease such as asthma, obesity, cancer, kidney, liver, neurological or autoimmune conditions.
Even so, one limitation of the study is that the researchers included in the study clinically suspected and not laboratory-confirmed Covid-19 cases, as a result of which some patients could have been incorrectly identified as Covid-19 positive.
So what is new about the findings?
It is already known that older individuals and those with comorbidities are at higher risk of developing severe outcomes from the disease and the findings of the study are largely in line with what is being observed in Covid-19 patients around the world.
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Therefore, more than the nature of their findings, it is the scale of the study that has allowed researchers to be more precise on rare exposures, multiple risk factors and the rapid detection of important signals. Essentially, studying the disease patterns in such a large data set has allowed the researchers to assess the less common risk factors more robustly.
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