Burden and prevalence of prognostic factors for severe covid-19 disease in Sweden

Katalin Gemes; Mats Talback; Karin Modig; Anders Ahlbom; Anita Berglund; Maria Feychting; Anthony Matthews

Several models have been produced to support COVID-19 planning in countries across the world. [9] [10] [11] [12] Many of these models are based on the assumption that disease severity increases with age, but they do not account for an increased risk of severe disease in individuals with underlying medical conditions. This is usually because age stratified burden of disease at a local level is rarely available. Even when this information is available, data from which it originates can be obtained from a sample of the population rather than from the whole population. If the sample is not representative of the population at large, results may be biased. In order to build clear robust models that will provide trustworthy estimates of the extent to which the infection will impact populations, we need reliable estimates on the underlying prevalence of medical conditions suggesting high risk of severe disease.