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Adiposity is associated with complications from COVID-19 and other common infections, such as postoperative infections, skin infections, upper respiratory tract infections and major gastrointestinal infections. However, previous epidemiological studies have been limited by sample size, the range of adiposity measures available, and the range and detail of disease outcomes. This project will use UK Biobank (and, where appropriate, large datasets of Hospital Episode Statistics [HES] and primary care data), to assess the effect of adiposity on risk of COVID-19 and other common infections. A particular advantage of studying UKB participants is the availability of bio-impedance and body imaging (including DEXA scans and MRI) that are potentially more informative measures of adiposity compared to commonly used measures such as waist-to-hip ratio, waist circumference and BMI. Furthermore, the project will assess the causality of the observed associations by using genetic variants as instrumental variables for different measures of adiposity (Mendelian randomisation).


The candidate will become proficient in planning and conducting large-scale epidemiological analyses, and will become proficient in programming in at least one statistical analysis package (e.g. SAS, R). 

Key project steps may include: 

1. A systematic literature review detailing studies of adiposity, Covid-19 and infectious diseases. 

2. Development of survival models to relate hazard ratios for Covid-19 and infections to a range of adiposity measures. 

3. Investigation of potential mechanisms underlying the associations and use of Mendelian randomization to examine their causality. 


Williamson, E.J., Walker, A.J., Bhaskaran, K. et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature 584, 430-436 (2020).

 Simonnet, A. et al. High prevalence of obesity in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) requiring invasive mechanical ventilation. Obesity 28, 1195–1199 (2020).

 Fernandez, C. and Ari, M. Obesity, respiratory disease and pulmonary infections. Annals of Research Hospitals,1(5), (2017).

 Falagas, M. and Kompoti, M. Obesity and infection. Lancet Infect Dis 6, 438-46 (2006)


The project will provide a range of training opportunities in large-scale epidemiology, and statistical analysis and programming. By the end of the DPhil, it is expected that the candidate will be competent to plan, undertake and interpret statistical analysis of large-scale epidemiological data, and to report the findings. The candidate will join the Clinical Trial Service Unit & Epidemiological Studies Unit at the Nuffield Department of Population Health, and will based at the Big Data Institute, which has excellent facilities and a world-class community of statistical and clinical scientists.

Prospective candidate

Candidates should have a strong background in a biomedical or mathematical  discipline, and postgraduate training in epidemiology, statistics or public health. As the project will involve large-scale data and statistical analyses, the candidates should have an interest and aptitude in developing these skills in a population health research context.