Background
The recent COVID-19 outbreak has generated an unprecedented public health crisis, with millions of infections and hundreds of thousands of deaths worldwide. Using hospital-based or mortality data, several COVID-19 risk factors have been identified, but these may be confounded or biased.
Methods
Using SARS-CoV-2 infection test data (N=4,509 tests; 1,325 positive) from Public Health England, linked to the UK Biobank study, we explored the contribution of demographic, social, health risk, medical, and environmental factors to COVID-19 risk. We used multivariable and penalised logistic regression models for the risk of (i) being tested, (ii) testing positive/negative in the study population and, adopting a test negative design, (iv) the risk of testing positive within the tested population.
Results
In the fully adjusted model, variables independently associated with the risk of being tested for COVID-19 with OR >1.05 were: male sex; Black ethnicity; social disadvantage (as measured by education, housing and income); occupation (healthcare worker, retired, unemployed); ever smoker; severely obese; comorbidities; and greater exposure to PM2.5-absorbance. Of these, only male sex, non-White ethnicity, lower educational attainment, and none of the comorbidities or health risk factors, were associated with testing positive among tested individuals.
Conclusions
We adopted a careful and exhaustive approach within a large population-based cohort, which enabled us to triangulate evidence linking, male sex, lower educational attainment, non-White ethnicity with the risk of COVID-19. The elucidation of the joint and independent effects of these factors is a high-priority area for further research to inform on COVID-19 natural history.